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The genomic landscape of canine diffuse large B-cell lymphoma identifies distinct subtypes with clinical and therapeutic implications

Lab Animal volume 51, pages 191–202 (2022)Cite this article

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Abstract

Diffuse large B-cell lymphoma (DLBCL) is the most common lymphoid neoplasm in dogs and in humans. It is characterized by a remarkable degree of clinical heterogeneity that is not completely elucidated by molecular data. This poses a major barrier to understanding the disease and its response to therapy, or when treating dogs with DLBCL within clinical trials. We performed an integrated analysis of exome (n = 77) and RNA sequencing (n = 43) data in a cohort of canine DLBCL to define the genetic landscape of this tumor. A wide range of signaling pathways and cellular processes were found in common with human DLBCL, but the frequencies of the most recurrently mutated genes (TRAF3, SETD2, POT1, TP53, MYC, FBXW7, DDX3X and TBL1XR1) differed. We developed a prognostic model integrating exonic variants and clinical and transcriptomic features to predict the outcome in dogs with DLBCL. These results comprehensively define the genetic drivers of canine DLBCL and can be prospectively utilized to identify new therapeutic opportunities.

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Fig. 1: KM curves for 77 dogs with DLBCL according to treatment.
Fig. 2: Mutational landscape of cDLBCL determined by WES.
Fig. 3: Oncoplot of recurrently mutated genes and CNAs in cDLBCL.
Fig. 4: Somatic CNAs and short mutations in the top-four mutated genes.
Fig. 5: Somatic interactions in cDLBCL.
Fig. 6: Mutational signature analysis in cDLBCL.
Fig. 7: Oncogenic pathways affected by SNVs and CNAs in cDLBCL.
Fig. 8: CNA profile of cDLBCL.
Fig. 9: Association of TP53 mutation with CNAs.
Fig. 10: KM curves for 77 dogs with DLBCL according to TP53, MYC and POT1 mutational status.

Data availability

Raw Illumina reads of RNA-Seq and WES are publicly available in SRA Archive with reference numbers SRP137798 and PRJNA752630.

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Acknowledgements

This study was financially supported by the ‘Scientific Independence of young Researchers’ (SIR) grant received from the Ministero dell’Università (K9LYMPHOMET, protocol RBSI14EDX9).

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Authors and Affiliations

  1. Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padua, Padua, Italy

    Diana Giannuzzi

  2. Department of Veterinary Medical Science, University of Bologna, Ozzano dell’Emilia, Bologna, Italy

    Laura Marconato

  3. Department of Veterinary Sciences, University of Turin, Grugliasco, Turin, Italy

    Antonella Fanelli, Luca Licenziato, Raffaella De Maria & Luca Aresu

  4. Institute of Oncology Research, Faculty of Biomedical Sciences, USI, Bellinzona, Switzerland

    Andrea Rinaldi, Afua A. Mensah & Francesco Bertoni

  5. Department of Experimental Oncology, European Institute of Oncology (IEO), Milan, Italy

    Luca Rotta

  6. Hastim, Toulouse, France

    Nicole Rouquet

  7. Department of Medical Sciences, University of Turin, Turin, Italy

    Giovanni Birolo & Piero Fariselli

  8. Oncology Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland

    Francesco Bertoni

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Contributions

L.A. and F.B. designed the study, interpreted the data and wrote the manuscript; N.R. contributed to study design; L.M. provided samples and clinical data and contributed to manuscript revision; D.G., A.F., L.L., R.D.M., A.R. and L.R. carried out experiments; D.G. conducted bioinformatic analysis and contributed to data interpretation; D.G., A.F. and L.L. performed formal analysis and data visualization and conducted statistical analysis; G.B. and P.F. developed the in silico prediction models; A.A.M. interpreted data and revised the manuscript; all authors contributed to manuscript revision and approved the final draft.

Corresponding authors

Correspondence to Francesco Bertoni or Luca Aresu.

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Lab Animal thanks the anonymous reviewers for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–9 and Supplementary Tables 1–4.

Reporting Summary

Supplementary Data 1

Clinical details for the 77 dogs with DLBCL sequenced in this study.

Supplementary Data 2

Survival analysis of 77 cDLBCLs sequenced in this study.

Supplementary Data 3

Somatic mutations identified by WES in 77 cDLBCLs.

Supplementary Data 4

Variant filtering using COSMIC for known cancer genes and genes reported in human DLBCL (hDLBCL).

Supplementary Data 5

Significant regions affected by CNAs identified by GISTIC.

Supplementary Data 6

CNAs significantly associated with TTP and LSS (FDR < 0.05).

Supplementary Data 7

Median TTP and LSS and survival analysis in dogs carrying CFA13, CFA14 and CFA31 aberrations.

Supplementary Data 8

Significant associations between gene mutations and clinicopathological variables.

Supplementary Data 9

GSEA in cDLBCL based on DDX3X, SETD2, TP53, MYC, TRAF3, POT1 and FBXW7 mutational status (mut versus WT).

Supplementary Data 10

Clinical details for the 56 dogs with DLBCL included in TP53 mutational analysis.

Supplementary Data 11

TP53 mutations identified in the validation cohort of 56 cDLBCLs.

Supplementary Data 12

Survival analysis of 56 cDLBCLs analyzed for TP53 mutation.

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Giannuzzi, D., Marconato, L., Fanelli, A. et al. The genomic landscape of canine diffuse large B-cell lymphoma identifies distinct subtypes with clinical and therapeutic implications. Lab Anim 51, 191–202 (2022). https://doi.org/10.1038/s41684-022-00998-x

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