Peripheral T-cell lymphoma (PTCL) is a heterogenous group of lymphoid tumors and encompass peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS), angioimmunoblastic T-cell lymphoma (AITL), and several other entities of T-cell lymphoma (Swerdlow et al., 2017), likely driven by an array of recurrent genomic defects (Fiore et al., 2020a). Except for PTCL-NOS, AITL is the most common subtype of PTCL (21–36.1%) (Chiba and Sakata-Yanagimoto, 2020; de Leval et al., 2015) and is believed to arise from a subset of peripheral mature CD4+ T-cells corresponding to T-follicular helper (TFH) cells, characterized immunophenotypically by expression of a set of cellular markers like PD1, CXCR5, BCL-6, CD10, CXCL13, and ICOS-1 (Attygalle et al., 2002; Chiba and Sakata-Yanagimoto, 2020; Dupuis et al., 2006; Marafioti et al., 2010; Mourad et al., 2008). Morphologically, AITL is typically characterized by a polymorphous lymphoid infiltrate with a proliferation of medium-sized tumor cells with clear cytoplasm (clear cell immunoblasts), associated with prominent proliferation of high endothelial venules and follicular dendritic cells. A subset of PTCL, termed PTCL with TFH phenotype in the updated WHO classification, is thought to be also derived from TFH and may be biologically related to AITL, sharing some clinical-pathological features with the latter (Swerdlow et al., 2017). Although progress has been made in understanding AITL pathogenesis and developing new treatment (Lemonnier et al., 2018), AITL remains as an aggressive lymphoid tumor, with low estimated rates of overall and failure-free survival at 5 years (33% and 18%, respectively) (Federico et al., 2013). To develop more effective therapeutic agents against AITL and PTCL in general, with TFH phenotype further understanding of the molecular pathogenic mechanisms of AITL is needed.

Genetically, AITL is characterized by a number of genomic mutations in TET2, RHOA, DNMT3A, and IDH2 (Fiore et al., 2020a; Nakamoto-Matsubara et al., 2014; Odejide et al., 2014; Sakata-Yanagimoto et al., 2014; Yoo et al., 2014). Cell-intrinsic and/or -extrinsic factors that facilitate the accumulation of these AITL-related mutations remain unclear. Mutations in TET2 and DNMT3A are also frequently associated with myeloid malignancies, including acute myeloid leukemia (AML), myeloproliferative neoplasms (MPN), and myelodysplastic/myeloproliferative neoplasms (MDS/MPN). TET2 and DNMT3A are also the most commonly mutated genes associated with clonal hematopoiesis (CH) in healthy adults, especially those over 60 years of age. CH has been shown to be an aging-related process characterized by the clonal expansion of hematopoietic cells harboring one or more somatic mutations as a result of selective advantage in the hematopoietic stem and progenitor cells due to enhanced self-renewal and inhibition of differentiation (Challen and Goodell, 2020; Gondek and DeZern, 2020; Jaiswal and Ebert, 2019; Steensma and Ebert, 2020). It has been noted that myeloid and lymphoid malignancies may co-occur in the same patients (Holst et al., 2020). Considering the similarities in genomic mutation profiles of AITL, myeloid malignancies, and CH, it has been postulated that there may be a biological link between these entities. To test this, the current study implemented next-generation sequencing (NGS) approach to analyze neoplastic T-cells and paired bone marrow/peripheral blood (BM/PB) specimens from a cohort of 27 patients with AITL or PTCL-NOS, and explored the potential of using the genomic findings from this study to shed light into the etiology of AITL development and to predict clinical progression related to development of second hematologic neoplasms, which is one of the clinical challenges in the clinical management of these cancer patients.

In the current study, we examined the landscape of the genomic alterations in AITL/PTLC-NOS and their paired BM or PB using a large-panel targeted sequencing approach in the largest cohort of the AITL patients reported to date. We demonstrated that in about 60% of AITL/PTLC-NOS patients identical pathogenic TET2 and/or DNMT3A mutations were shared between AITL/PTCL-NOS and CH found in the BM or PB. Studies of large cohorts have demonstrated an increased risk of hematologic malignancy for CH (Genovese et al., 2014; Jaiswal et al., 2014), but no definitive link has been established between CH and AITL/PTCL-NOS from those studies. Our findings suggest that these TET2 and/or DNMT3A mutations may occur very early in the hematopoietic stem cells (HSC) before they give rise to the common lymphoid progenitors and common myeloid progenitors and propose a possible link between CH and development of AITL (Fiore et al., 2020a). Interestingly, the VAF of the CH-associated mutations is 22.5% on average in our cohort and is higher compared to the average VAF of CH-related mutations in the general population (Genovese et al., 2014). This observation is in line with the higher risk of hematopoietic malignancy associated with increased VAF (>10%; Warren and Link, 2020). It is conceivable that certain TET2 or DNMT3A mutations are stronger drivers that can result in more expanded CH and/or higher efficient T-cell lymphoma development. For example, as seen in patient #28, there were three TET2 mutations identified in the LN, each of which appears to be present in separate clones and have different capacity to generate CH based on VAF in the BM (0, 5.47, and 10.89%, respectively). In addition, our study supports a mutated HSC as potential origin for AITL. As the TET2 and/or DNMT3A mutations are propagated to the lymphoid and myeloid progeny of the mutated HSC, it can be speculated that in the lymphoid compartment the impacts of these mutations vary depending on the developmental and differentiation stage of the T-cells, and may be most felt in the T-cells of follicular helper cell origin (TFH). Lastly, our interesting case of an AITL patient with CH and subsequent development of DLBCL and the sharing of the same TET2 mutation among all three lesions suggest that a subset of DLBCL, possibly the molecular subtype characterized by mutated TET2 (Lacy et al., 2020), may originate from mutated HSC. To our knowledge, this is the first reported case in which the mutated HSC developed into three distinct tumors of diverse lineages.

The findings from this investigation confirm and extend the results previously published regarding the cellular origin of AITL (Couronné et al., 2012; Lewis et al., 2020; Nguyen et al., 2017; Quivoron et al., 2011; Sakata-Yanagimoto et al., 2014; Schwartz et al., 2017; Tiacci et al., 2018). Most of these previous studies presented sporadic AITL cases in which the TET2 or/and DNMT3A mutations present in AITL were also found in their BM/PB compartments. Two reports showed that AITL shared the same TET2 mutations with the isolated CD20⁺/CD19⁺ (B-cells) or CD34⁺ cells (Couronné et al., 2012; Schwartz et al., 2017). These studies also pointed to a mutated HSC that gives rise to lymphoid and myeloid cells harboring the same mutations. A high-risk CH was also documented as the cellular origin of AITL and NPM1-mutated AML in a patient (Tiacci et al., 2018). While our manuscript was under preparation, the results of a study conceptually similar to ours regarding the cellular origin of AITL were reported (Lewis et al., 2020). Consistent with our observations, the report showed that the mutations related to CH (i.e., TET2 or DNMT3A) were detected in both the neoplastic T-cell and myeloid compartments in 15 out of 22 AITL patients (68%), and associated with second myeloid neoplasm development after the diagnosis of AITL in 4 cases. However, in their cohort, no cases were reported where AITL developed subsequent to myeloid neoplasms. Our study presented one such case (patient #20) whose AITL developed after 10 years of PV and the two hematologic neoplasms shared three identical JAK2 and TET2 mutations (Figure 3B). The identification of cases in which myeloid neoplasms precede the diagnosis of AITL provides additional supportive evidence to the postulation that the mutated HSCs are the common origin for these hematologic neoplasms, which develop independently and divergently in tumor evolution. Whether AITL precedes or develops subsequent to the myeloid neoplasms may depend on the stochastic dynamics of the clonal evolution.

Additional late non-CH mutations are found in 68.4% of the AITL/PTCL-NOS in our cohort, consistent with the belief that CH-associated TET2 and DNMT3A mutations are insufficient for tumorigenesis and additional genetic alterations are required. Consistent with this notion, in AITL animal models, TET2 disruption or RHOA^G17V expression alone failed to induce AITL development; however, AITL-like lymphoma developed once TET2 disruption and RHOA^G17V expression were combined (Cortes et al., 2018; Ng et al., 2018; Nguyen et al., 2020). For the development of myeloid neoplasms, additional mutations beyond CH-associated TET2 and DNMT3A mutations drive further clonal expansion from CH. These mutations may be acquired early (patient #20, JAK2, Figure 3B) or late during tumor development (patient # 24, JAK2, Figure 1—figure supplement 2).

We discovered that the late non-CH mutations are enriched for the missense mutations and the C>A substitutions (Figures 1C and D and 2). Further analysis identified the major mutational signatures that were very similar (CCS = 0.9) in the Cornell and Kyoto cohorts (LM_Sign.01 vs. Kyoto_Sign.01, Figure 2—figure supplements 2–4). The main features shared by these two signatures were the enriched C>A mutations and the closest match with the smoking-associated COSMIC Signature 4 (CCS = 0.5–0.55) among all 30 established signatures (SBS30). The C>A base substitutions as a result of the LM_Sign.01 or Kyoto_Sign.01 signature activity are related to critical mutations in a number of oncogenic genes, including RHOA, TET2, and IDH2. This finding may have implications on treatment and prevention of AITL. It is believed that the C>A mutations associated with Signature 4 are likely caused by mis-replication or mis-repairing of DNA damage induced by tobacco carcinogens (Alexandrov et al., 2016; Alexandrov et al., 2013), which largely result in missense mutations (Blackford et al., 2009). Consistent with this causative link are our findings that the majority of the late non-CH mutations identified in our cohort were missense mutation (75.2%, Figure 1D) and that the patients with AITL/PTCL-NOS have a 172.3-fold increased risk for development of lung cancer compared to the age-matched/adjusted general population (Figure 2D). Medical records showed that 19 out of 26 AITL/PTCL-NOS cases were non-smokers, but the major mutational signature extracted from them still matched with smoking-associated Signature 4 (CCS = 0.48) among SBS30 (Figure 2—figure supplement 5). This discrepancy may be due to misreporting and undocumented SHS, which were observed in 13.8% of non-smokers with lung cancers (Alexandrov et al., 2016). In our cohort, patient #7 was recorded as a non-smoker, but carried many smoking-associated COSMIC Signature 4 mutations (two mutations per megabase, Figure 2—figure supplement 2C). The CDC screening study showed that between 1988 and 1994, 20.9% of non-smokers in the US population were exposed to home SHS (at least one family member was a smoker), and 83.9% were exposed to SHS to various degrees during 1988–1994 as cotinine (the main metabolite of nicotine) could be detected at a level of >0.05 ng/ml in the sera of non-smokers. This suggests that most of the patients included in this study might have been exposed to undocumented SHS for ~25–50 years when they were diagnosed with AITL/PTCL-NOS from 2008 to 2019 because 86% of the patients were 50 years old or older (median, 65). Since there is no safe level of SHS exposure (https://www.cdc.gov/tobacco/data_statistics/fact_sheets/secondhand_smoke/health_effects/index.htm), it is conceivable that exposure to undocumented SHS may lead to the gradual accumulation of Signature 4 mutations in the Cornell cohort. In the Kyoto cohort (TFH-PTCL), a similar situation might apply. In Japan, a recent study showed that the overall prevalence of SHS exposure in workplaces, restaurants, and bars were 49, 55, and 83% (Sansone et al., 2020). These data may partially explain the accumulation of COSMIC Signature 4-like driver mutations in the non-smokers. Consequently, our findings suggest that cessation of smoking or avoiding exposure to SHS in home or public places may be a potential effective intervention to prevent AITL development in higher risk population, particularly those already found to harbor CH.

On the contrary, the CH-associated genetic alterations in the current cohort are characterized primarily by the C>T and C>G mutations (64% of all the mutations) in TET2 and DNMT3A (Figure 2A and C). Analysis revealed that this mutation pattern primarily matched to COSMIC Signature 2 (CCS = 0.65, Figure 2—figure supplement 1), which was reported to be associated with the AID/APOBEC family of cytidine deaminases or aging-dependent function decline of base-excision repair machinery (Alexandrov et al., 2013). This mutational mechanism might also play a role in the non-CH late mutations as 35% of the non-CH mutations were C>T substitutions (Figure 2B and C). Interestingly, reduced accumulation of the C to T mutations by inactivation of AID blocked development of B-cell malignancies in aging TET2-deficient mice (Mouly et al., 2018), implying that AID might be a therapeutic targeting candidate for lymphoma, including AITL.

Furthermore, we found that CH associated with multiple-hit TET2 (defined as ≥2 pathogenic TET2 mutations with VAFs of ≥15%) is an independent risk factor for development of concurrent hematologic malignancies in AITL patients (Figure 4). Recently, certain features of CH predictive of hematopoietic malignancy development were identified (Warren and Link, 2020). These features include >1 mutated gene, VAF >10%, and mutations in specific genes and variants, for example, TP53 and IDH1/2. Our TET2 biomarker includes two or more mutations and VAF of at least 15%. However, TET2 has not been previously implicated as a marker for increased risk of hematologic malignancy in CH in general. It is possible that this multiple-hit TET2 biomarker is specific and only relevant in the setting of patients with AITL and CH. Mechanistically, two or more TET2 mutations each with relatively high mutation burden (≥15%) correlate with increased clonal expansion and/or more severe disruption of TET2 activity, thereby increasing the global chance of acquiring additional driver mutations and hence increased risk for development of second hematologic neoplasms. Consistent with this hypothesis, aging TET2-deficient mice develop diverse hematologic malignancies (Pan et al., 2017). A reliable predictor for concurrent hematologic malignancies may be helpful for clinical stratification and management for this subset of the AITL patients.

In summary, our study provides genomic evidence of a potential origin of AITL/PTCL-NOS from a mutated HSC clone, which can be associated with CH as well as development of myeloid and even B-cell malignancies. The development of these hematopoietic malignancies of different lineages occurs via divergent evolution from the mutated hematopoietic precursor clone, often with acquisition of additional mutations frequently induced by the C>A-associated mutagenic agents like tobacco. We also identified a potential biomarker: two or more pathogenic TET2 mutations with high mutation burden for the development of second hematologic neoplasm in AITL patients. Single-cell methodology will help definitively determine the clonal architecture in lymphoma and BM with multiple mutations and enhance our understanding of the initiation of tumor induced by CH-associated mutations.

All relevant data are included in this manuscript and the supplementary files.

1. 1. Alexandrov LB
   2. Nik-Zainal S
   3. Wedge DC
   4. Aparicio SAJR
   5. Behjati S
   6. Biankin AV
   7. Bignell GR
   8. Bolli N
   9. Borg A
   10. Børresen-Dale A-L
   11. Boyault S
   12. Burkhardt B
   13. Butler AP
   14. Caldas C
   15. Davies HR
   16. Desmedt C
   17. Eils R
   18. Eyfjörd JE
   19. Foekens JA
   20. Greaves M
   21. Hosoda F
   22. Hutter B
   23. Ilicic T
   24. Imbeaud S
   25. Imielinski M
   26. Imielinsk M
   27. Jäger N
   28. Jones DTW
   29. Jones D
   30. Knappskog S
   31. Kool M
   32. Lakhani SR
   33. López-Otín C
   34. Martin S
   35. Munshi NC
   36. Nakamura H
   37. Northcott PA
   38. Pajic M
   39. Papaemmanuil E
   40. Paradiso A
   41. Pearson JV
   42. Puente XS
   43. Raine K
   44. Ramakrishna M
   45. Richardson AL
   46. Richter J
   47. Rosenstiel P
   48. Schlesner M
   49. Schumacher TN
   50. Span PN
   51. Teague JW
   52. Totoki Y
   53. Tutt ANJ
   54. Valdés-Mas R
   55. van Buuren MM
   56. van ’t Veer L
   57. Vincent-Salomon A
   58. Waddell N
   59. Yates LR
   60. Australian Pancreatic Cancer Genome Initiative
   61. ICGC Breast Cancer Consortium
   62. ICGC MMML-Seq Consortium
   63. ICGC PedBrain
   64. Zucman-Rossi J
   65. Futreal PA
   66. McDermott U
   67. Lichter P
   68. Meyerson M
   69. Grimmond SM
   70. Siebert R
   71. Campo E
   72. Shibata T
   73. Pfister SM
   74. Campbell PJ
   75. Stratton MR

   (2013) Signatures of mutational processes in human cancer

   Nature 500:415–421.

   https://doi.org/10.1038/nature12477

   • PubMed
   • Google Scholar
2. 1. Alexandrov LB
   2. Ju YS
   3. Haase K
   4. Van Loo P
   5. Martincorena I
   6. Nik-Zainal S
   7. Totoki Y
   8. Fujimoto A
   9. Nakagawa H
   10. Shibata T
   11. Campbell PJ
   12. Vineis P
   13. Phillips DH
   14. Stratton MR

   (2016) Mutational signatures associated with tobacco smoking in human cancer

   Science 354:618–622.

   https://doi.org/10.1126/science.aag0299

   • PubMed
   • Google Scholar
3. 1. Attygalle A
   2. Al-Jehani R
   3. Diss TC
   4. Munson P
   5. Liu H
   6. Du MQ
   7. Isaacson PG
   8. Dogan A

   (2002) Neoplastic T cells in angioimmunoblastic t-cell lymphoma express cd10

   Blood 99:627–633.

   https://doi.org/10.1182/blood.v99.2.627

   • PubMed
   • Google Scholar
4. 1. Béguelin W
   2. Popovic R
   3. Teater M
   4. Jiang Y
   5. Bunting KL
   6. Rosen M
   7. Shen H
   8. Yang SN
   9. Wang L
   10. Ezponda T
   11. Martinez-Garcia E
   12. Zhang H
   13. Zheng Y
   14. Verma SK
   15. McCabe MT
   16. Ott HM
   17. Van Aller GS
   18. Kruger RG
   19. Liu Y
   20. McHugh CF
   21. Scott DW
   22. Chung YR
   23. Kelleher N
   24. Shaknovich R
   25. Creasy CL
   26. Gascoyne RD
   27. Wong KK
   28. Cerchietti L
   29. Levine RL
   30. Abdel-Wahab O
   31. Licht JD
   32. Elemento O
   33. Melnick AM

   (2013) EZH2 is required for germinal center formation and somatic ezh2 mutations promote lymphoid transformation

   Cancer Cell 23:677–692.

   https://doi.org/10.1016/j.ccr.2013.04.011

   • PubMed
   • Google Scholar
5. 1. Bellanné-Chantelot C
   2. Chaumarel I
   3. Labopin M
   4. Bellanger F
   5. Barbu V
   6. De Toma C
   7. Delhommeau F
   8. Casadevall N
   9. Vainchenker W
   10. Thomas G
   11. Najman A

   (2006) Genetic and clinical implications of the val617phe jak2 mutation in 72 families with myeloproliferative disorders

   Blood 108:346–352.

   https://doi.org/10.1182/blood-2005-12-4852

   • PubMed
   • Google Scholar
6. 1. Blackford A
   2. Parmigiani G
   3. Kensler TW
   4. Wolfgang C
   5. Jones S
   6. Zhang X
   7. Parsons DW
   8. Lin JC-H
   9. Leary RJ
   10. Eshleman JR
   11. Goggins M
   12. Jaffee EM
   13. Iacobuzio-Donahue CA
   14. Maitra A
   15. Klein A
   16. Cameron JL
   17. Olino K
   18. Schulick R
   19. Winter J
   20. Vogelstein B
   21. Velculescu VE
   22. Kinzler KW
   23. Hruban RH

   (2009) Genetic mutations associated with cigarette smoking in pancreatic cancer

   Cancer Research 69:3681–3688.

   https://doi.org/10.1158/0008-5472.CAN-09-0015

   • PubMed
   • Google Scholar
7. 1. Centers for Disease Control and Prevention (CDC)

   (2008)

   Disparities in secondhand smoke exposure--united states, 1988-1994 and 1999-2004

   MMWR. Morbidity and Mortality Weekly Report 57:744–747.

   • PubMed
   • Google Scholar
8. 1. Challen GA
   2. Goodell MA

   (2020) Clonal hematopoiesis: Mechanisms driving dominance of stem cell clones

   Blood 136:1590–1598.

   https://doi.org/10.1182/blood.2020006510

   • PubMed
   • Google Scholar
9. 1. Cheng S
   2. Singh K
   3. Liu YC
   4. Kluk MJ
   5. Hassane DC
   6. Tam W

   (2017)

   Targeted sequencing of recurrently mutated genes in myeloid neoplasms using the Raindance Thunderstorm-Illumina Miseq Platform: My Heme (Myeloid Hematologic Malignancy) Panel (abstract

   The Journal of Molecular Diagnostics 19:962.

   • Google Scholar
10. 1. Chiba S
    2. Sakata-Yanagimoto M

    (2020) Advances in understanding of angioimmunoblastic T-cell lymphoma

    Leukemia 34:2592–2606.

    https://doi.org/10.1038/s41375-020-0990-y

    • PubMed
    • Google Scholar
11. 1. Cortes JR
    2. Ambesi-Impiombato A
    3. Couronné L
    4. Quinn SA
    5. Kim CS
    6. da Silva Almeida AC
    7. West Z
    8. Belver L
    9. Martin MS
    10. Scourzic L
    11. Bhagat G
    12. Bernard OA
    13. Ferrando AA
    14. Palomero T

    (2018) RHOA G17V induces t follicular helper cell specification and promotes lymphomagenesis

    Cancer Cell 33:259–273.

    https://doi.org/10.1016/j.ccell.2018.01.001

    • PubMed
    • Google Scholar
12. 1. Couronné L
    2. Bastard C
    3. Bernard OA

    (2012) TET2 and DNMT3A mutations in human t-cell lymphoma

    The New England Journal of Medicine 366:95–96.

    https://doi.org/10.1056/NEJMc1111708

    • PubMed
    • Google Scholar
13. 1. de Leval L
    2. Parrens M
    3. Le Bras F
    4. Jais JP
    5. Fataccioli V
    6. Martin A
    7. Lamant L
    8. Delarue R
    9. Berger F
    10. Arbion F
    11. Bossard C
    12. Copin MC
    13. Canioni D
    14. Charlotte F
    15. Damaj G
    16. Dartigues P
    17. Fabiani B
    18. Ledoux-Pilon A
    19. Montagne K
    20. Molina T
    21. Patey M
    22. Tas P
    23. Peoch M
    24. Petit B
    25. Petrella T
    26. Picquenot JM
    27. Rousset T
    28. Rousselet MC
    29. Soubeyran I
    30. Thiebault S
    31. Tournilhac O
    32. Xerri L
    33. Gisselbrecht C
    34. Haioun C
    35. Delsol G
    36. Gaulard P

    (2015) Angioimmunoblastic t-cell lymphoma is the most common t-cell lymphoma in two distinct french information data sets

    Haematologica 100:e361.

    https://doi.org/10.3324/haematol.2015.126300

    • PubMed
    • Google Scholar
14. 1. Dupuis J
    2. Boye K
    3. Martin N
    4. Copie-Bergman C
    5. Plonquet A
    6. Fabiani B
    7. Baglin AC
    8. Haioun C
    9. Delfau-Larue MH
    10. Gaulard P

    (2006) Expression of CXCL13 by neoplastic cells in angioimmunoblastic t-cell lymphoma (AITL): A new diagnostic marker providing evidence that AITL derives from follicular helper T cells

    The American Journal of Surgical Pathology 30:490–494.

    https://doi.org/10.1097/00000478-200604000-00009

    • PubMed
    • Google Scholar
15. 1. Fantini D
    2. Vidimar V
    3. Yu Y
    4. Condello S
    5. Meeks JJ

    (2020) Mutsignatures: An R package for extraction and analysis of cancer mutational signatures

    Scientific Reports 10:18217.

    https://doi.org/10.1038/s41598-020-75062-0

    • PubMed
    • Google Scholar
16. Software

    1. Fantini D

    (2021) mutSignatures R package

    Version - 2.1.3.

    https://github.com/dami82/mutSignatures
17. 1. Federico M
    2. Rudiger T
    3. Bellei M
    4. Nathwani BN
    5. Luminari S
    6. Coiffier B
    7. Harris NL
    8. Jaffe ES
    9. Pileri SA
    10. Savage KJ
    11. Weisenburger DD
    12. Armitage JO
    13. Mounier N
    14. Vose JM

    (2013) Clinicopathologic characteristics of angioimmunoblastic t-cell lymphoma: Analysis of the international peripheral t-cell lymphoma project

    Journal of Clinical Oncology 31:240–246.

    https://doi.org/10.1200/JCO.2011.37.3647

    • PubMed
    • Google Scholar
18. 1. Fiore D
    2. Cappelli LV
    3. Broccoli A
    4. Zinzani PL
    5. Chan WC
    6. Inghirami G

    (2020a) Peripheral T cell lymphomas: From the bench to the clinic

    Nature Reviews Cancer 20:323–342.

    https://doi.org/10.1038/s41568-020-0247-0

    • Google Scholar
19. 1. Fiore D
    2. Cappelli LV
    3. Zumbo P
    4. Phillips JM
    5. Liu Z
    6. Cheng S
    7. Yoffe L
    8. Ghione P
    9. Di Maggio F
    10. Dogan A
    11. Khodos I
    12. de Stanchina E
    13. Casano J
    14. Kayembe C
    15. Tam W
    16. Betel D
    17. Foa’ R
    18. Cerchietti L
    19. Rabadan R
    20. Horwitz S
    21. Weinstock DM
    22. Inghirami G

    (2020b) A novel JAK1 mutant breast implant-associated anaplastic large cell lymphoma patient-derived xenograft fostering pre-clinical discoveries

    Cancers 12:E1603.

    https://doi.org/10.3390/cancers12061603

    • PubMed
    • Google Scholar
20. 1. Genovese G
    2. Kähler AK
    3. Handsaker RE
    4. Lindberg J
    5. Rose SA
    6. Bakhoum SF
    7. Chambert K
    8. Mick E
    9. Neale BM
    10. Fromer M
    11. Purcell SM
    12. Svantesson O
    13. Landén M
    14. Höglund M
    15. Lehmann S
    16. Gabriel SB
    17. Moran JL
    18. Lander ES
    19. Sullivan PF
    20. Sklar P
    21. Grönberg H
    22. Hultman CM
    23. McCarroll SA

    (2014) Clonal hematopoiesis and blood-cancer risk inferred from blood dna sequence

    The New England Journal of Medicine 371:2477–2487.

    https://doi.org/10.1056/NEJMoa1409405

    • PubMed
    • Google Scholar
21. 1. Gondek LP
    2. DeZern AE

    (2020) Assessing clonal haematopoiesis: Clinical burdens and benefits of diagnosing myelodysplastic syndrome precursor states

    The Lancet. Haematology 7:e73–e81.

    https://doi.org/10.1016/S2352-3026(19)30211-X

    • PubMed
    • Google Scholar
22. 1. Holst JM
    2. Plesner TL
    3. Pedersen MB
    4. Frederiksen H
    5. Møller MB
    6. Clausen MR
    7. Hansen MC
    8. Hamilton-Dutoit SJ
    9. Nørgaard P
    10. Johansen P
    11. Ramm Eberlein T
    12. Mortensen BK
    13. Mathiasen G
    14. Øvlisen A
    15. Wang R
    16. Wang C
    17. Zhang W
    18. Beier Ommen H
    19. Stentoft J
    20. Ludvigsen M
    21. Tam W
    22. Chan WC
    23. Inghirami G
    24. d’Amore F

    (2020) Myeloproliferative and lymphoproliferative malignancies occurring in the same patient: A nationwide discovery cohort

    Haematologica 105:2432–2439.

    https://doi.org/10.3324/haematol.2019.225839

    • PubMed
    • Google Scholar
23. 1. Jaiswal S
    2. Fontanillas P
    3. Flannick J
    4. Manning A
    5. Grauman PV
    6. Mar BG
    7. Lindsley RC
    8. Mermel CH
    9. Burtt N
    10. Chavez A
    11. Higgins JM
    12. Moltchanov V
    13. Kuo FC
    14. Kluk MJ
    15. Henderson B
    16. Kinnunen L
    17. Koistinen HA
    18. Ladenvall C
    19. Getz G
    20. Correa A
    21. Banahan BF
    22. Gabriel S
    23. Kathiresan S
    24. Stringham HM
    25. McCarthy MI
    26. Boehnke M
    27. Tuomilehto J
    28. Haiman C
    29. Groop L
    30. Atzmon G
    31. Wilson JG
    32. Neuberg D
    33. Altshuler D
    34. Ebert BL

    (2014) Age-related clonal hematopoiesis associated with adverse outcomes

    The New England Journal of Medicine 371:2488–2498.

    https://doi.org/10.1056/NEJMoa1408617

    • PubMed
    • Google Scholar
24. 1. Jaiswal S
    2. Ebert BL

    (2019) Clonal hematopoiesis in human aging and disease

    Science 366:eaan4673.

    https://doi.org/10.1126/science.aan4673

    • PubMed
    • Google Scholar
25. 1. Lacy SE
    2. Barrans SL
    3. Beer PA
    4. Painter D
    5. Smith AG
    6. Roman E
    7. Cooke SL
    8. Ruiz C
    9. Glover P
    10. Van Hoppe SJL
    11. Webster N
    12. Campbell PJ
    13. Tooze RM
    14. Patmore R
    15. Burton C
    16. Crouch S
    17. Hodson DJ

    (2020) Targeted sequencing in DLBCL, molecular subtypes, and outcomes: A haematological malignancy research network report

    Blood 135:1759–1771.

    https://doi.org/10.1182/blood.2019003535

    • PubMed
    • Google Scholar
26. 1. Lemonnier F
    2. Couronné L
    3. Parrens M
    4. Jaïs J-P
    5. Travert M
    6. Lamant L
    7. Tournillac O
    8. Rousset T
    9. Fabiani B
    10. Cairns RA
    11. Mak T
    12. Bastard C
    13. Bernard OA
    14. de Leval L
    15. Gaulard P

    (2012) Recurrent TET2 mutations in peripheral t-cell lymphomas correlate with tfh-like features and adverse clinical parameters

    Blood 120:1466–1469.

    https://doi.org/10.1182/blood-2012-02-408542

    • PubMed
    • Google Scholar
27. 1. Lemonnier F
    2. Dupuis J
    3. Sujobert P
    4. Tournillhac O
    5. Cheminant M
    6. Sarkozy C
    7. Pelletier L
    8. Marçais A
    9. Robe C
    10. Fataccioli V
    11. Haioun C
    12. Hermine O
    13. Gaulard P
    14. Delarue R

    (2018) Treatment with 5-azacytidine induces a sustained response in patients with angioimmunoblastic t-cell lymphoma

    Blood 132:2305–2309.

    https://doi.org/10.1182/blood-2018-04-840538

    • PubMed
    • Google Scholar
28. 1. Lewis NE
    2. Petrova-Drus K
    3. Huet S
    4. Epstein-Peterson ZD
    5. Gao Q
    6. Sigler AE
    7. Baik J
    8. Ozkaya N
    9. Moskowitz AJ
    10. Kumar A
    11. Horwitz SM
    12. Zhang Y
    13. Arcila ME
    14. Levine RL
    15. Roshal M
    16. Dogan A
    17. Xiao W

    (2020) Clonal hematopoiesis in angioimmunoblastic t-cell lymphoma with divergent evolution to myeloid neoplasms

    Blood Advances 4:2261–2271.

    https://doi.org/10.1182/bloodadvances.2020001636

    • PubMed
    • Google Scholar
29. 1. Li MM
    2. Datto M
    3. Duncavage EJ
    4. Kulkarni S
    5. Lindeman NI
    6. Roy S
    7. Tsimberidou AM
    8. Vnencak-Jones CL
    9. Wolff DJ
    10. Younes A
    11. Nikiforova MN

    (2017) Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer: A Joint Consensus Recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists

    The Journal of Molecular Diagnostics 19:4–23.

    https://doi.org/10.1016/j.jmoldx.2016.10.002

    • PubMed
    • Google Scholar
30. 1. Marafioti T
    2. Paterson JC
    3. Ballabio E
    4. Chott A
    5. Natkunam Y
    6. Rodriguez-Justo M
    7. Plonquet A
    8. Rodriguez-Pinilla SM
    9. Klapper W
    10. Hansmann M-L
    11. Pileri SA
    12. Isaacson PG
    13. Stein H
    14. Piris MA
    15. Mason DY
    16. Gaulard P

    (2010) The inducible t-cell co-stimulator molecule is expressed on subsets of t cells and is a new marker of lymphomas of T follicular helper cell-derivation

    Haematologica 95:432–439.

    https://doi.org/10.3324/haematol.2009.010991

    • PubMed
    • Google Scholar
31. Software

    1. Mayakonda A

    (2021) Maftools

    Version 2.4.12.

    https://github.com/PoisonAlien/maftools
32. 1. Morin RD
    2. Johnson NA
    3. Severson TM
    4. Mungall AJ
    5. An J
    6. Goya R
    7. Paul JE
    8. Boyle M
    9. Woolcock BW
    10. Kuchenbauer F
    11. Yap D
    12. Humphries RK
    13. Griffith OL
    14. Shah S
    15. Zhu H
    16. Kimbara M
    17. Shashkin P
    18. Charlot JF
    19. Tcherpakov M
    20. Corbett R
    21. Tam A
    22. Varhol R
    23. Smailus D
    24. Moksa M
    25. Zhao Y
    26. Delaney A
    27. Qian H
    28. Birol I
    29. Schein J
    30. Moore R
    31. Holt R
    32. Horsman DE
    33. Connors JM
    34. Jones S
    35. Aparicio S
    36. Hirst M
    37. Gascoyne RD
    38. Marra MA

    (2010) Somatic mutations altering ezh2 (tyr641) in follicular and diffuse large b-cell lymphomas of germinal-center origin

    Nature Genetics 42:181–185.

    https://doi.org/10.1038/ng.518

    • PubMed
    • Google Scholar
33. 1. Mouly E
    2. Ghamlouch H
    3. Della-Valle V
    4. Scourzic L
    5. Quivoron C
    6. Roos-Weil D
    7. Pawlikowska P
    8. Saada V
    9. Diop MK
    10. Lopez CK
    11. Fontenay M
    12. Dessen P
    13. Touw IP
    14. Mercher T
    15. Aoufouchi S
    16. Bernard OA

    (2018) B-cell tumor development in tet2-deficient mice

    Blood Advances 2:703–714.

    https://doi.org/10.1182/bloodadvances.2017014118

    • PubMed
    • Google Scholar
34. 1. Mourad N
    2. Mounier N
    3. Brière J
    4. Raffoux E
    5. Delmer A
    6. Feller A
    7. Meijer CJLM
    8. Emile J-F
    9. Bouabdallah R
    10. Bosly A
    11. Diebold J
    12. Haioun C
    13. Coiffier B
    14. Gisselbrecht C
    15. Gaulard P

    (2008) Clinical, biologic, and pathologic features in 157 patients with angioimmunoblastic T-cell lymphoma treated within the Groupe d’Etude des Lymphomes de l’Adulte (GELA) trials

    Blood 111:4463–4470.

    https://doi.org/10.1182/blood-2007-08-105759

    • PubMed
    • Google Scholar
35. 1. Nakamoto-Matsubara R
    2. Enami T
    3. Yoshida K
    4. Shiozawa Y
    5. Nanmoku T
    6. Satomi K
    7. Muto H
    8. Obara N
    9. Kato T
    10. Kurita N
    11. Yokoyama Y
    12. Izutsu K
    13. Ota Y
    14. Sanada M
    15. Shimizu S
    16. Komeno T
    17. Sato Y
    18. Ito T
    19. Kitabayashi I
    20. Takeuchi K
    21. Nakamura N
    22. Ogawa S
    23. Chiba S

    (2014) Detection of the G17V rhoa mutation in angioimmunoblastic t-cell lymphoma and related lymphomas using quantitative allele-specific PCR

    PLOS ONE 9:e109714.

    https://doi.org/10.1371/journal.pone.0109714

    • PubMed
    • Google Scholar
36. 1. Ng SY
    2. Brown L
    3. Stevenson K
    4. deSouza T
    5. Aster JC
    6. Louissaint A
    7. Weinstock DM

    (2018) RHOA G17V is sufficient to induce autoimmunity and promotes t-cell lymphomagenesis in mice

    Blood 132:935–947.

    https://doi.org/10.1182/blood-2017-11-818617

    • PubMed
    • Google Scholar
37. 1. Nguyen TB
    2. Sakata-Yanagimoto M
    3. Asabe Y
    4. Matsubara D
    5. Kano J
    6. Yoshida K
    7. Shiraishi Y
    8. Chiba K
    9. Tanaka H
    10. Miyano S
    11. Izutsu K
    12. Nakamura N
    13. Takeuchi K
    14. Miyoshi H
    15. Ohshima K
    16. Minowa T
    17. Ogawa S
    18. Noguchi M
    19. Chiba S

    (2017) Identification of cell-type-specific mutations in nodal t-cell lymphomas

    Blood Cancer Journal 7:e516.

    https://doi.org/10.1038/bcj.2016.122

    • PubMed
    • Google Scholar
38. 1. Nguyen TB
    2. Fujisawa M
    3. Nuhat ST
    4. Miyoshi H
    5. Nannya Y
    6. Hashimoto K
    7. Fukumoto K
    8. Bernard OA
    9. Kiyoki Y
    10. Ishitsuka K
    11. Momose H
    12. Sukegawa S
    13. Shinagawa A
    14. Suyama T
    15. Sato Y
    16. Nishikii H
    17. Obara N
    18. Kusakabe M
    19. Ogawa S
    20. Ohshima K
    21. Chiba S

    (2020) Dasatinib is an effective treatment for angioimmunoblastic t-cell lymphoma

    Cancer Research 80:1875–1884.

    https://doi.org/10.1158/0008-5472.CAN-19-2787

    • PubMed
    • Google Scholar
39. 1. Odejide O
    2. Weigert O
    3. Lane AA
    4. Toscano D
    5. Lunning MA
    6. Kopp N
    7. Kim S
    8. van Bodegom D
    9. Bolla S
    10. Schatz JH
    11. Teruya-Feldstein J
    12. Hochberg E
    13. Louissaint A
    14. Dorfman D
    15. Stevenson K
    16. Rodig SJ
    17. Piccaluga PP
    18. Jacobsen E
    19. Pileri SA
    20. Harris NL
    21. Ferrero S
    22. Inghirami G
    23. Horwitz SM
    24. Weinstock DM

    (2014) A targeted mutational landscape of angioimmunoblastic T-cell lymphoma

    Blood 123:1293–1296.

    https://doi.org/10.1182/blood-2013-10-531509

    • PubMed
    • Google Scholar
40. 1. Pan F
    2. Wingo TS
    3. Zhao Z
    4. Gao R
    5. Makishima H
    6. Qu G
    7. Lin L
    8. Yu M
    9. Ortega JR
    10. Wang J
    11. Nazha A
    12. Chen L
    13. Yao B
    14. Liu C
    15. Chen S
    16. Weeks O
    17. Ni H
    18. Phillips BL
    19. Huang S
    20. Wang J
    21. He C
    22. Li G-M
    23. Radivoyevitch T
    24. Aifantis I
    25. Maciejewski JP
    26. Yang F-C
    27. Jin P
    28. Xu M

    (2017) Tet2 loss leads to hypermutagenicity in haematopoietic stem/progenitor cells

    Nature Communications 8:15102.

    https://doi.org/10.1038/ncomms15102

    • PubMed
    • Google Scholar
41. 1. Quivoron C
    2. Couronné L
    3. Della Valle V
    4. Lopez CK
    5. Plo I
    6. Wagner-Ballon O
    7. Do Cruzeiro M
    8. Delhommeau F
    9. Arnulf B
    10. Stern M-H
    11. Godley L
    12. Opolon P
    13. Tilly H
    14. Solary E
    15. Duffourd Y
    16. Dessen P
    17. Merle-Beral H
    18. Nguyen-Khac F
    19. Fontenay M
    20. Vainchenker W
    21. Bastard C
    22. Mercher T
    23. Bernard OA

    (2011) Tet2 inactivation results in pleiotropic hematopoietic abnormalities in mouse and is a recurrent event during human lymphomagenesis

    Cancer Cell 20:25–38.

    https://doi.org/10.1016/j.ccr.2011.06.003

    • PubMed
    • Google Scholar
42. 1. Sakata-Yanagimoto M
    2. Enami T
    3. Yoshida K
    4. Shiraishi Y
    5. Ishii R
    6. Miyake Y
    7. Muto H
    8. Tsuyama N
    9. Sato-Otsubo A
    10. Okuno Y
    11. Sakata S
    12. Kamada Y
    13. Nakamoto-Matsubara R
    14. Tran NB
    15. Izutsu K
    16. Sato Y
    17. Ohta Y
    18. Furuta J
    19. Shimizu S
    20. Komeno T
    21. Sato Y
    22. Ito T
    23. Noguchi M
    24. Noguchi E
    25. Sanada M
    26. Chiba K
    27. Tanaka H
    28. Suzukawa K
    29. Nanmoku T
    30. Hasegawa Y
    31. Nureki O
    32. Miyano S
    33. Nakamura N
    34. Takeuchi K
    35. Ogawa S
    36. Chiba S

    (2014) Somatic RHOA mutation in angioimmunoblastic t cell lymphoma

    Nature Genetics 46:171–175.

    https://doi.org/10.1038/ng.2872

    • PubMed
    • Google Scholar
43. 1. Sansone G
    2. Fong GT
    3. Meng G
    4. Craig LV
    5. Xu SS
    6. Quah ACK
    7. Ouimet J
    8. Mochizuki Y
    9. Yoshimi I
    10. Tabuchi T

    (2020) Secondhand smoke exposure in public places and support for smoke-free laws in Japan: Findings from the 2018 ITC Japan Survey

    International Journal of Environmental Research and Public Health 17:E979.

    https://doi.org/10.3390/ijerph17030979

    • PubMed
    • Google Scholar
44. 1. Schwartz FH
    2. Cai Q
    3. Fellmann E
    4. Hartmann S
    5. Mäyränpää MI
    6. Karjalainen-Lindsberg M-L
    7. Sundström C
    8. Scholtysik R
    9. Hansmann M-L
    10. Küppers R

    (2017) TET2 mutations in B cells of patients affected by angioimmunoblastic t-cell lymphoma

    The Journal of Pathology 242:129–133.

    https://doi.org/10.1002/path.4898

    • PubMed
    • Google Scholar
45. 1. Steensma DP
    2. Ebert BL

    (2020) Clonal hematopoiesis as a model for premalignant changes during aging

    Experimental Hematology 83:48–56.

    https://doi.org/10.1016/j.exphem.2019.12.001

    • PubMed
    • Google Scholar
46. Book

    1. Swerdlow S
    2. Campo E
    3. Harris N
    4. Jaffe E
    5. Pileri S
    6. Stein H
    7. Thiele J

    (2017)

    Who Classification of Tumours of Haematopoietic and Lymphoid Tissues

    IARC.

    • Google Scholar
47. 1. Tiacci E
    2. Venanzi A
    3. Ascani S
    4. Marra A
    5. Cardinali V
    6. Martino G
    7. Codoni V
    8. Schiavoni G
    9. Martelli MP
    10. Falini B

    (2018) High-risk clonal hematopoiesis as the origin of AITL and npm1-mutated AML

    The New England Journal of Medicine 379:981–984.

    https://doi.org/10.1056/NEJMc1806413

    • PubMed
    • Google Scholar
48. 1. Warren JT
    2. Link DC

    (2020) Clonal hematopoiesis and risk for hematologic malignancy

    Blood 136:1599–1605.

    https://doi.org/10.1182/blood.2019000991

    • PubMed
    • Google Scholar
49. 1. Watatani Y
    2. Sato Y
    3. Miyoshi H
    4. Sakamoto K
    5. Nishida K
    6. Gion Y
    7. Nagata Y
    8. Shiraishi Y
    9. Chiba K
    10. Tanaka H
    11. Zhao L
    12. Ochi Y
    13. Takeuchi Y
    14. Takeda J
    15. Ueno H
    16. Kogure Y
    17. Shiozawa Y
    18. Kakiuchi N
    19. Yoshizato T
    20. Nakagawa MM
    21. Nanya Y
    22. Yoshida K
    23. Makishima H
    24. Sanada M
    25. Sakata-Yanagimoto M
    26. Chiba S
    27. Matsuoka R
    28. Noguchi M
    29. Hiramoto N
    30. Ishikawa T
    31. Kitagawa J
    32. Nakamura N
    33. Tsurumi H
    34. Miyazaki T
    35. Kito Y
    36. Miyano S
    37. Shimoda K
    38. Takeuchi K
    39. Ohshima K
    40. Yoshino T
    41. Ogawa S
    42. Kataoka K

    (2019) Molecular heterogeneity in peripheral t-cell lymphoma, not otherwise specified revealed by comprehensive genetic profiling

    Leukemia 33:2867–2883.

    https://doi.org/10.1038/s41375-019-0473-1

    • PubMed
    • Google Scholar
50. 1. Yoo HY
    2. Sung MK
    3. Lee SH
    4. Kim S
    5. Lee H
    6. Park S
    7. Kim SC
    8. Lee B
    9. Rho K
    10. Lee J-E
    11. Cho K-H
    12. Kim W
    13. Ju H
    14. Kim J
    15. Kim SJ
    16. Kim WS
    17. Lee S
    18. Ko YH

    (2014) A recurrent inactivating mutation in RHOA GTPASE in angioimmunoblastic t cell lymphoma

    Nature Genetics 46:371–375.

    https://doi.org/10.1038/ng.2916

    • PubMed
    • Google Scholar

Author details

1. Shuhua Cheng

   Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

2. Wei Zhang

   Genomics Resources Core Facility, Weill Cornell Medicine, New York, United States

   Contribution

   Investigation, Methodology, Validation

   Competing interests

   No competing interests declared

3. Giorgio Inghirami

   Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, United States

   Contribution

   Resources, Writing – review and editing

   Competing interests

   No competing interests declared

4. Wayne Tam

   Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – original draft, Writing – review and editing

   For correspondence

   wtam@med.cornell.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4283-0005

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