Background

Natural killer/T cell lymphoma (NKTCL) is a unique subtype of non-Hodgkin lymphoma with poor prognosis, which predominantly occurs in East Asia and Latin America [1]. The occurrence of NKTCL is associated with Epstein-Barr virus (EBV) infection, although the actual mechanism remains elusive [2]. Currently, the asparaginase-based chemotherapy is used as the first-line treatment for NKTCL, which helps to notably improve the survival of NKTCL patients [1, 3]. Yet approximately 40–50% of patients, especially those in advanced NKTCL stages, do not respond well to this treatment and frequently experience relapse after the first-line treatment [4, 5]. Patients with relapsed/refractory NKTCL had poor prognosis with a median overall survival (OS) less than 1 year [6]. So far, there is a lack of progress in the development of targeted therapy for NKTCL, which urges a need for better understanding of the molecular pathogenesis of NKTCL.

Recent applications of genome sequencing on NKTCL patients revealed recurrent mutational targets such as RNA helicase (e.g., DDX3X), tumor suppressors (e.g., TP53), and genes involved in the JAK-STAT and RAS-MAPK signaling pathways, as well as epigenetic modulators (e.g., KMT2C and KMT2D) [

Methods

Patients and NGS

Patients diagnosed with NKTCL during 2010–2020 at Sun Yat-sen University Cancer Center, Sichuan Cancer Hospital, and the Sixth Affiliated Hospital of Sun Yat-sen University were retrospectively enrolled in this study. A total of 42 patients (19 primary, 23 relapsed/refractory) were enrolled and subjected to WES. Technical details of DNA extraction and NGS are described in the Additional file 1: Supplementary materials and methods. In addition, raw NKTCL WGS and WES reads from several published studies were retrieved and reanalyzed as well, which include (1) the International Cancer Genome Consortium (ICGC), with 23 WGS datasets (20 primary, 3 relapsed/refractory) [19]; (2) the European Genome-phenome Archive (EGA), with 12 WGS datasets (2 primary, 10 relapsed/refractory) [20]; and (3) the National Omics Data Encyclopedia (NODE), which includes data on primary patients with 36 WGS and 50 WES datasets [Somatic mutation calling

Somatic SNVs and INDELs were detected with MuTect2 (v4.1.0.0) [21] and annotated by ANNOVAR (v2020Jun08) [22]. MSIsensor-pro (v1.2.0) [23] was utilized to evaluate microsatellite instability (MSI), generating an MSI score for each sample. SigProfiler Tools (v1.2.14) [24] were used for extracted and visualized mutational signatures. Somatic CNVs were called by CNVkit (v0.9.9) [25] and ASCAT (v3.1.1) [26]. The KEGG pathway enrichment analysis for gene-level CNVs was performed by clusterProfiler (v4.7.1.3) [27]. The aneuploidy score was calculated using the get_Aneuploidy_score() function implemented in sigminer (v2.1.9) [28]. For WGS data, we also examined the landscape of SVs using manta (v1.6.0) [29], profiled chromothripsis events with ShatterSeek (v1.1) [32]. A total of 151 patients were successfully classified into four clusters (C1–C4) (Fig. 6A). As for the remaining 12 patients, no characteristic driver genetic alterations were identified and therefore we classified them as the C0 cluster, although their genomes did show substantial alterations (mean SNVs = 26.4 per sample, mean CNVs = 63.6 per sample, mean SVs = 9.6 per sample) as those of other patients. All patients classified as C0 are primary patients. The C1 cluster is characterized by the mutations in DST, RELN, ARID1A, and CN loss of RBMX. The CD274 (PD-L1) SVs are also highly noticeable in this cluster. The C2 cluster is represented by the mutations involving the JAK-STAT pathway, including STAT3 and CN gain of CRLF2. The C3 cluster is associated with mutations in epigenetic modifiers such as TET2, KDM6B, and ARID1B as well as the mutations of tumor suppressors including FAT1 and KMT2C. Finally, the C4 cluster is characterized by the CN gain of STK11 and EPOR and mutations in genomic instability including PCNT, PRDM9, TUBGCP6, and NF1. Interestingly, patients from the C4 cluster predominantly have advanced-staged NKTCL with many of them being relapsed/refractory, whereas most patients from C1–C3 clusters have primary NKTCL (Fig. 6A). In accordance to this observation, the C4 cluster is also associated with the worst prognosis and higher PINK and IPI scores (Additional file 3: Fig. S6). Taken together, we found relapsed/refractory NKTCL patients are characterized by high levels of TMB, CNVs, and SVs, as well as higher propensity for chromothripsis and focal amplifications, reflecting their severe genome instability (Fig. 6B).

Fig. 6
figure 6

Identification of clusters of NKTCL with coordinate genomic alterations. A Nonnegative matrix factorization clustering was carried out using somatic SNVs, INDELs, CNVs, and SVs in the 163 NKTCL patients (columns). Samples without candidate alterations were defined as cluster C0. Clusters C1–C4 with their associated representative genetic alterations are visualized. B Sankey-diagram of the clinical and mutational characteristics for NKTCL patients. The eight columns from left to right represent molecular subty** clusters, Ann Arbor stage, sampling status, TMB, CNV, SV, chromothripsis, and focal amplifications respectively, with the total height represents the full 71 WGS samples. The curves with different colors show the correspondence relationship among different characteristics and molecular subty** clusters. C Kaplan–Meier curve of PFS and D OS of all NKTCL patients from different molecular subty** clusters. E Kaplan–Meier curve of PFS and F OS of primary NKTCL patients from different molecular subty** clusters

Full size image

With this molecular subty** analysis, we mainly want to identify characteristic genomic alterations that helps to classify patients and potentially guide their treatment strategies, which is the ultimate goal of precision medicine. Nevertheless, it is also interesting to test if our molecular subty** system captures the inherent factors that determines patients’ prognosis. First, we found patients from these five clusters (i.e., C0–C4) show clear differences in PFS (P < 0.001, Fig. 6C) and OS (P = 0.005, Fig. 6D). Moreover, by excluding relapsed/refractory patients and performing the survival analysis for the 127 primary patients only, we found our molecular subty** systems is still capable of differentiating the prognostic performance of primary NKTCL patients in terms of both PFS (P = 0.004, Fig. 6E) and OS (P = 0.04, Fig. 6F). These results underscore the value of our molecular subty** system for future clinical practice on NKTCL.

Discussion

In this study, we comprehensively characterized the first full-spectral NKTCL somatic mutational landscape for NKTCL. Not only did we validate the previously reported common NKTCL mutational targets such as STAT3, TP53, and DDX3X [

Availability of data and materials

The newly generated WES data reported in this paper have been deposited in the Genome Sequence Archive with the accession number of HRA004366 (https://ngdc.cncb.ac.cn/gsa-human/browse/HRA004366) [71]. The publicly available WGS/WES data can be retrieved from European Genome-phenome Archive (EGA) repository with accession number of EGAD00001004140 (https://ega-archive.org/datasets/EGAD00001004140) [20] and EGAS00001002398 (https://ega-archive.org/studies/EGAS00001002398) [19] as well as from The National Omics Data Encyclopedia (NODE) with the accession number of OEP000498 (https://www.biosino.org/node/project/detail/OEP000498) [8]. Custom auxiliary scripts used for results visualization are publicly available at Zenodo (https://doi.org/https://doi.org/10.5281/zenodo.10842061) [72].

Abbreviations

CNVs:

Copy number variants

DSBs:

Double strand breaks

EBV:

Epstein-Barr virus

eccDNA:

Extracellular circular DNA

HRD:

Homologous recombination deficiency

INDELs:

Insertion/deletions

MSI:

Microsatellite instability

NHEJ:

Non-homologous DNA end-joining

NKTCL:

Natural killer/T cell lymphoma

NMF:

Non-negative matrix factorization

OS:

Overall survival

PD-1:

Programmed cell death protein 1

PFS:

Progression-free survival

SBS:

Single base substitution

SNVs:

Single nucleotide variants

SVs:

Structural variants

TMB:

Tumor mutation burden

WES:

Whole-exome sequencing

WGS:

Whole-genome sequencing

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Acknowledgements

We are grateful to all patients who enrolled in this study and donate their clinical samples. We are also very grateful to researchers who shared WGS and WES data generated in their previous studies. We thank the two anonymous reviewers for their insightful critics and valuable suggestions, which helped us to markedly improve the robustness and depth of this study and the associated manuscript.

Funding

This work is supported by Guangdong Science and Technology Department (2017B020227002 to T.L.), the Regional Innovation and Cooperation Project of Sichuan Province (2021YFQ0037 to T.L.), National Natural Science Foundation of China (32070592 to J.-X.Y., 82270198 to Huangming Hong), Guangdong Pearl River Talents Program (2019QN01Y183 to J.-X.Y.), Guangdong Basic and Applied Basic Research Foundation (2022A1515010717 to J.-X.Y.), and Young Talents Program of Sun Yat-sen University Cancer Center (YTP-SYSUCC-0042 to J.-X. Y.).

Author information

Author notes

  1. Zegeng Chen, He Huang, and Huangming Hong contributed equally to this work and share first authorship.

Authors and Affiliations

  1. State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China

    Zegeng Chen & Jia-**ng Yue

  2. Department of Medical Oncology, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China

    Zegeng Chen, He Huang, Huageng Huang, Zhao Wang, **aojie Fang, Yuyi Yao & Tongyu Lin

  3. Department of Medical Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, 610041, China

    Huangming Hong, Huawei Weng, Le Yu & Tongyu Lin

  4. Department of Medical Oncology, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510655, China

    Jian **ao

Authors
  1. Zegeng Chen
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  2. He Huang
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  3. Huangming Hong
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  4. Huageng Huang
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  5. Huawei Weng
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  6. Le Yu
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  7. Jian **ao
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  8. Zhao Wang
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  9. **aojie Fang
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  10. Yuyi Yao
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  11. Jia-**ng Yue
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  12. Tongyu Lin
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Contributions

Tongyu Lin and Jia-**ng Yue designed and supervised the study. Zegeng Chen and He Huang performed the genomic data collection, curation, and analysis. Huangming Hong, Huageng Huang, Huawei Weng, Yuyi Yao, Jian **ao, and Le Yu helped with genomic data collection and curation. Zhao Wang and **aojie Fang managed the clinical data and helped with the survival analyses. Zegeng Chen, He Huang, Jia-**ng Yue, and Tongyu Lin wrote the manuscript. All authors contributed to the data interpretation and discussion. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Jia-**ng Yue or Tongyu Lin.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the Ethics Review Board of the Sun Yat-sen University Cancer Center (issue number: B2021-271–01) and participating hospitals. All patients provided written informed consent. This study complies with the ethical standards of the Helsinki Declaration.

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Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1. 

Supplementary materials and methods.

Additional file 2: Table S1.

Summary of the sequencing coverage of the 163 NKTCL tumor samples. Table S2. Distribution of clinical features across different patient cohorts (n = 163). Table S3. Prognostic factors for overall survival (OS) by univariate and multivariate analysis. Table S4. The clinical feature summary for the 163 NKTCL patients. Table S5. Cancer-related genes interrupted by structural variant (SV) breakpoints in NKTCL patients with WGS data.

Additional file 3: Fig. S1.

Kaplan–Meier survival curves of overall survival in patients with NKTCL. Fig. S2. Characterization of somatic mutations, tumor mutation burden, and MSI status in NKTCL. Fig. S3. Mutational landscape of NKTCL. Fig. S4. The landscape of copy number variation in NKTCL. Fig. S5. The Copy number (CN) signatures identified for NKTCL patients. Fig. S6. Sankey-diagram of the molecular subtypes and clinical prognostic models for NKTCL patients.

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Chen, Z., Huang, H., Hong, H. et al. Full-spectral genome analysis of natural killer/T cell lymphoma highlights impacts of genome instability in driving its progression. Genome Med 16, 48 (2024). https://doi.org/10.1186/s13073-024-01324-5

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