Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

LYMPHOMA

Tumor evolution analysis uncovered immune-escape related mutations in relapse of diffuse large B-cell lymphoma

Leukemia volume 38, pages 2276–2280 (2024)Cite this article

Subjects

Access through your institution
Buy or subscribe

Diffuse large B-cell lymphoma (DLBCL) is the most common form of non-Hodgkin lymphoma, and over 60% patients can achieve durable remission after receiving standard immunotherapy treatment that comprises rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisolone (R-CHOP) [1,2,3]. The remaining patients are refractory to this frontline treatment or suffer tumor relapses with an extremely poor prognosis [4]. Due to the limited understanding of the molecular pathogenesis of DLBCL relapse, there are currently no effective approaches to overcome resistance of R-CHOP. Therefore, characterization of the clonal evolutionary pattern and relapse-specific lesions accounting for drug resistance in relapsed DLBCL are vital for finding new treatment solutions. However, due to the difficulty in obtaining relapse samples, previous studies to understand the genetic changes upon disease recurrence are usually limited by the small number of cases with paired diagnosis-relapse sampling [5]. To characterize the evolutionary landscape of DLBCL under treatment, in this study, we performed whole-exome sequencing (WES) of matched diagnosis and relapse DNA samples from 44 DLBCL patients receiving R-CHOP treatment, together with deep targeted sequencing of 481 candidate genes in 26 patients (Table S1, Supplementary Fig. 1A).

Genomic variants emerging in tumor relapse or expanding under therapy may account for treatment failure. Hence, we used three complementary approaches to identify relapse-associated variants. Firstly, we compared the mutation frequency between the diagnosis-relapse pairs in our cohort. We found that most of the known DLBCL drivers have similar mutation frequencies (Fig. 1C). Notably, we identified several genes appeared to be mutated exclusively in recurrent tumors, including ZFHX3 (4/44) and PKD1 (3/44). ZFHX3, which encodes a transcription factor that negatively regulates c-Myb and transactivates CDKN1A, has been reported as a tumor suppressor gene in several cancers [7]. PKD1 is a serine/threonine kinase involved in a multitude of mechanisms associated with tumor progression, such as invasion, proliferation, and apoptosis [8]. Secondly, we compared the mutation frequency from our relapse DLBCL with three independent primary DLBCL cohorts (in total 1861 cases) [6, 9, 10]. By this comparison, 129 genes show a significantly higher mutation frequency in our relapsed tumors, including CD58 and ZFHX3 (Supplementary Fig. 5A, Table S4). CD58 activates T cells and natural killer (NK) cells by binding to CD2 receptors. The absence of CD58 may allow DLBCL cells to escape NK and T cell-mediated immune surveillance [11]. Finally, to characterize the evolutionary pattern of mutations under treatment, we compared the mutation Cancer Cell Fraction (CCF) between DLBCL primary and relapsed samples. A higher CCF at relapse was observed for a number of mutations involved in immune escape (CD58, TNFRSF14, FAS), apoptosis (ATM, PKD1) or transcriptional regulation (ZFHX3), suggesting new acquirements of mutations or clonal expansion at tumor relapse (Fig. 1D and Supplementary Fig. 5B). Loss of ATM was reported to lead to poor treatment response and resistance to DNA damaging agents in B-cell malignancies [12]. Interestingly, we found that TNFRSF14 alterations were specifically enriched in relapse GCB-DLBCL (n = 6) (Supplementary Fig. 5C). Consistently, in a public primary DLBCL cohort, TNFRSF14 alterations were more common in GCB-DLBCL than in ABC-DLBCL or unclassified DLBCL (Supplementary Fig. 5D) [6]. Notably, 80% of TNFRSF14 mutations were predicted to be loss-of-function mutations (Table S5). In addition, mutations in TNFRSF14 (p = 0.039) were associated with shorter overall survival in GCB-DLBCL (Supplementary Fig. 5E) [6]. These results support the hypothesis that TNFRSF14 alterations define a malignant subtype of GCB-DLBCL.

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Mutational landscape and dynamics in DLBCL.
Fig. 2: Clonal diversity in DLBCL evolution under treatment.

Data availability

The data that support the findings of this study are available upon request. The sequencing data have been deposited in European Genome-phenome Archive (EGA) under accession number EGAD50000000049.

References

  1. Coiffier B, Lepage E, Briere J, Herbrecht R, Tilly H, Bouabdallah R, et al. CHOP chemotherapy plus rituximab compared with CHOP alone in elderly patients with diffuse large-B-cell lymphoma. N Engl J Med. 2002;346:235–42.

    Article  CAS  PubMed  Google Scholar 

  2. Pfreundschuh M, Trumper L, Osterborg A, Pettengell R, Trneny M, Imrie K, et al. CHOP-like chemotherapy plus rituximab versus CHOP-like chemotherapy alone in young patients with good-prognosis diffuse large-B-cell lymphoma: a randomised controlled trial by the MabThera International Trial (MInT) Group. Lancet Oncol. 2006;7:379–91.

    Article  CAS  PubMed  Google Scholar 

  3. Pfreundschuh M, Schubert J, Ziepert M, Schmits R, Mohren M, Lengfelder E, et al. Six versus eight cycles of bi-weekly CHOP-14 with or without rituximab in elderly patients with aggressive CD20+ B-cell lymphomas: a randomised controlled trial (RICOVER-60). Lancet Oncol. 2008;9:105–16.

    Article  CAS  PubMed  Google Scholar 

  4. Maurer MJ, Ghesquieres H, Jais JP, Witzig TE, Haioun C, Thompson CA, et al. Event-free survival at 24 months is a robust end point for disease-related outcome in diffuse large B-cell lymphoma treated with immunochemotherapy. J Clin Oncol. 2014;32:1066–73.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Berendsen MR, Stevens WBC, van den Brand M, van Krieken JH, Scheijen B. Molecular genetics of relapsed diffuse large B-cell lymphoma: insight into mechanisms of therapy resistance. Cancers (Basel). 2020;12:3553.

    Article  CAS  PubMed  Google Scholar 

  6. Schmitz R, Wright GW, Huang DW, Johnson CA, Phelan JD, Wang JQ, et al. Genetics and pathogenesis of diffuse large B-cell lymphoma. N Engl J Med. 2018;378:1396–407.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Sun X, Frierson HF, Chen C, Li C, Ran Q, Otto KB, et al. Frequent somatic mutations of the transcription factor ATBF1 in human prostate cancer. Nat Genet. 2005;37:407–12.

    Article  CAS  PubMed  Google Scholar 

  8. Youssef I, Ricort JM. Deciphering the role of protein kinase D1 (PKD1) in cellular proliferation. Mol Cancer Res. 2019;17:1961–74.

    Article  CAS  PubMed  Google Scholar 

  9. Chapuy B, Stewart C, Dunford AJ, Kim J, Kamburov A, Redd RA, et al. Molecular subtypes of diffuse large B cell lymphoma are associated with distinct pathogenic mechanisms and outcomes. Nat Med. 2018;24:679–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Reddy A, Zhang J, Davis NS, Moffitt AB, Love CL, Waldrop A, et al. Genetic and functional drivers of diffuse large B cell lymphoma. Cell. 2017;171:481–94.e15

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Challa-Malladi M, Lieu YK, Califano O, Holmes AB, Bhagat G, Murty VV, et al. Combined genetic inactivation of beta2-Microglobulin and CD58 reveals frequent escape from immune recognition in diffuse large B cell lymphoma. Cancer Cell. 2011;20:728–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Austen B, Skowronska A, Baker C, Powell JE, Gardiner A, Oscier D, et al. Mutation status of the residual ATM allele is an important determinant of the cellular response to chemotherapy and survival in patients with chronic lymphocytic leukemia containing an 11q deletion. J Clin Oncol. 2007;25:5448–57.

    Article  CAS  PubMed  Google Scholar 

  13. Iorio F, Knijnenburg TA, Vis DJ, Bignell GR, Menden MP, Schubert M, et al. A landscape of pharmacogenomic interactions in cancer. Cell. 2016;166:740–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Grzelak L, Roesch F, Vaysse A, Biton A, Legendre R, Porrot F, et al. IRF8 regulates efficacy of therapeutic anti-CD20 monoclonal antibodies. Eur J Immunol. 2022;52:1648–61.

    Article  CAS  PubMed  Google Scholar 

  15. Morin RD, Assouline S, Alcaide M, Mohajeri A, Johnston RL, Chong L, et al. Genetic landscapes of relapsed and refractory diffuse large B-cell lymphomas. Clin Cancer Res. 2016;22:2290–300.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Key R&D Program of China (grant 2022YFC2704202 to ZL), National Natural Science Foundation of China (grant 32170565 to ZL), the Chinese Academy of Sciences Hundred Talents Program (to ZL) and the Cancer Institute (R01 CA194547 to WT and OE). We thank Jiguang Wang and Zheng Hu for their helpful suggestions on cancer evolution analysis.

Author information

Author notes

  1. These authors contributed equally: Xueshuai Han, Jingru Sui.

Authors and Affiliations

  1. China National Center for Bioinformation, Beijing, China

    Xueshuai Han, Jingru Sui, Yang Zhao, Xuan Lv, Jindou Xie & Zhaoqi Liu

  2. Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China

    Xueshuai Han, Jingru Sui, Yang Zhao, Xuan Lv, Jindou Xie & Zhaoqi Liu

  3. University of Chinese Academy of Sciences, Beijing, China

    Xueshuai Han, Jingru Sui, Xuan Lv, Jindou Xie & Zhaoqi Liu

  4. Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA

    Kui Nie, Giorgio Inghirami & Wayne Tam

  5. Department of Pathology, Singapore General Hospital, Singapore, Singapore

    Leonard Tan

  6. Department of Pathology, the University of Hong Kong, Queen Mary Hospital, Hong Kong SAR, China

    Rex K. H. Au-Yeung

  7. Department of Biochemistry and Molecular Cell Biology, Shanghai Jiaotong University School of Medicine, Shanghai, China

    Jiao Ma

  8. Department of Physiology and Biophysics, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA

    Olivier Elemento

  9. Division of Hematopathology, Department of Pathology and Laboratory Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA

    Wayne Tam

Authors
  1. Xueshuai Han
    View author publications

    Search author on:PubMed Google Scholar

  2. Jingru Sui
    View author publications

    Search author on:PubMed Google Scholar

  3. Kui Nie
    View author publications

    Search author on:PubMed Google Scholar

  4. Yang Zhao
    View author publications

    Search author on:PubMed Google Scholar

  5. Xuan Lv
    View author publications

    Search author on:PubMed Google Scholar

  6. Jindou Xie
    View author publications

    Search author on:PubMed Google Scholar

  7. Leonard Tan
    View author publications

    Search author on:PubMed Google Scholar

  8. Rex K. H. Au-Yeung
    View author publications

    Search author on:PubMed Google Scholar

  9. Jiao Ma
    View author publications

    Search author on:PubMed Google Scholar

  10. Giorgio Inghirami
    View author publications

    Search author on:PubMed Google Scholar

  11. Olivier Elemento
    View author publications

    Search author on:PubMed Google Scholar

  12. Wayne Tam
    View author publications

    Search author on:PubMed Google Scholar

  13. Zhaoqi Liu
    View author publications

    Search author on:PubMed Google Scholar

Contributions

XH, JS, YZ, XL, JX, JM and OE performed bioinformatic analyses. KN, GI, and WT performed data collection and clinical information arrangement. XH, LHCT, RKHAY, wrote the manuscript with the feedback from GI, WT and ZL. WT and ZL designed and supervised the entire project.

Corresponding authors

Correspondence to Wayne Tam or Zhaoqi Liu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Han, X., Sui, J., Nie, K. et al. Tumor evolution analysis uncovered immune-escape related mutations in relapse of diffuse large B-cell lymphoma. Leukemia 38, 2276–2280 (2024). https://doi.org/10.1038/s41375-024-02349-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41375-024-02349-w

Search

Advanced search

Quick links