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Cited by 37Lymphoma Diagnosis and TreatmentAcute Myeloid Leukemia ResearchCutaneous lymphoproliferative disorders research

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Mutation analysis links angioimmunoblastic T-cell lymphoma to clonal hematopoiesis and smoking

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Cheng, Shuhua, Zhang, Wei, Inghirami, Giorgio, Tam, Wayne·2021·eLife
DOI 10.7554/elife.66395PMC PMC8480981

Published version of a preprint

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Abstract

Background: Although advance has been made in understanding the pathogenesis of mature T-cell neoplasms, the initiation and progression of angioimmunoblastic T-cell lymphoma (AITL) and peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS), remain poorly understood. A subset of AITL/PTCL-NOS patients develop concomitant hematologic neoplasms (CHN), and a biomarker to predict this risk is lacking. Methods: We generated and analyzed the mutation profiles through 537-gene targeted sequencing of the primary tumors and matched bone marrow/peripheral blood samples in 25 patients with AITL and two with PTCL-NOS. Results: Clonal hematopoiesis (CH)-associated genomic alterations, found in 70.4% of the AITL/PTCL-NOS patients, were shared among CH and T-cell lymphoma, as well as concomitant myeloid neoplasms or diffuse large B-cell lymphoma (DLBCL) that developed before or after AITL. Aberrant AID/APOBEC activity-associated and tobacco smoking-associated mutational signatures were respectively enriched in the early CH-associated mutations and late non-CH-associated mutations during AITL/PTCL-NOS development. Moreover, analysis showed that the presence of CH harboring ≥2 pathogenic TET2 variants with ≥15% of allele burden conferred higher risk for CHN (p=0.0006, hazard ratio = 14.01, positive predictive value = 88.9%, negative predictive value = 92.1%). Conclusions: We provided genetic evidence that AITL/PTCL-NOS, CH, and CHN can frequently arise from common mutated hematopoietic precursor clones. Our data also suggests smoking exposure as a potential risk factor for AITL/PTCL-NOS progression. These findings provide insights into the cell origin and etiology of AITL and PTCL-NOS and provide a novel stratification biomarker for CHN risk in AITL patients. Funding: R01 grant (CA194547) from the National Cancer Institute to WT.

Overview

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TL;DR

In 27 patients with AITL/PTCL-NOS, 70.4% had clonal hematopoiesis (CH)-associated mutations shared with the lymphoma, supporting a common mutated hematopoietic precursor origin. Late mutations were enriched for smoking-associated C>A substitutions, and CH harboring ≥2 pathogenic TET2 variants at ≥15% allele burden predicted concomitant hematologic neoplasms.

Key findings

  • CH-associated genomic alterations were found in 70.4% (19/27) of AITL/PTCL-NOS patients and were shared between CH and T-cell lymphoma; shared mutations were primarily in TET2 (74% of cases) and DNMT3A (37%). CH-associated variant allele frequencies averaged 22.5% (median 16.5%) versus ~1% for lymphoma-involvement variants (p<0.0001).
  • Late non-CH mutations were enriched for missense changes (75.2% vs 36.1% in CH-associated mutations, p<0.0001) and C>A substitutions (mean 38.8% vs 5.95%, p=0.0024); the major late signature matched smoking-associated COSMIC Signature 4 (CCS ≈ 0.5). AITL/PTCL-NOS patients had a 172.3-fold higher lung cancer incidence than an age-matched general population (11,547 vs 67 per 100,000, p<0.00001).
  • CH with ≥2 pathogenic TET2 variants and ≥15% allele burden conferred higher risk for concomitant hematologic neoplasm (p=0.0006, hazard ratio=14.01, positive predictive value=88.9%, negative predictive value=92.1%, specificity 97.2%, sensitivity 72.7%).
  • Four AITL patients had additional hematologic neoplasms; shared truncal mutations supported divergent evolution from common precursors, including one case with CH, AITL, and DLBCL sharing TET2 and an EZH2 hotspot mutation.

Methods

  • Targeted sequencing of 27 paired diagnostic lymph node and matched bone marrow/peripheral blood samples from 25 AITL and 2 PTCL-NOS patients, using a 537-gene T-cell lymphoma panel; a 45-gene myeloid NGS panel was also described.
  • CH-associated mutations were distinguished from lymphoma-involvement mutations using tumor burden estimates from histology, flow cytometry, TCRG rearrangement, and VAF comparisons; mutational signatures were extracted with MutSignatures and compared with COSMIC signatures.
  • Survival analysis used Kaplan–Meier curves and Cox proportional hazards models; the TET2 biomarker analysis combined the study cohort with an external AITL cohort for 47 total cases.

Limitations

  • The paper states no clear association was demonstrated between smoking and AITL/PTCL-NOS development; C>A or overall Signature 4 mutation burden did not differ significantly between evaluable smokers and non-smokers (average C>A mutations 1 vs 0.92, p=0.61), and secondhand smoke exposure was inferred rather than directly documented.
  • The paper states single-cell methodology will be needed to definitively determine clonal architecture in lymphoma and bone marrow with multiple mutations.
  • Evident from the text: the primary cohort is small (27 patients; 47 for the survival analysis with an external cohort), the study is retrospective, and the targeted panel covers selected genes rather than the whole genome; smoking history was missing for 2 patients.

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Figures

Figure 1. Analysis of genomic alterations by target sequencing panel for primary lymphomas and paired bone marrow/peripheral blood (BM/PB) in patients with angioimmunoblastic T-cell lymphoma (AITL) and peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS). (A) Presence of clonal hematopoiesis (CH) in patients with AITL and PTCL-NOS. Dot plots showing the detected variants and their variant allele frequencies (VAFs) in the AITL and PTCL-NOS (lymph node [LN]) and their matched BM/PB in representative AITL and PTCL-NOS cases with CH. The black circles indicate variants specific to the lymphomas, and the variants shared between the primary lymphomas and CH are highlighted in red. The variants attributed to lymphoma only are boxed. Additional detailed descriptions of these illustrative cases are provided in Appendix 1. (B) Venn diagram showing the distribution of the shared, lymphoma or BM/PB-specific variants identified in the diagnostic LN and paired BM/PB samples. The shared variants are defined as variants identified in both the primary lymphoma and the BM/PB, the latter as CH-related variants. The variants predicted to be due only to lymphoma involvement in BM/PB have been excluded (see also Figure 1—figure supplement 1A for the distribution of all variants). (C) Summary of the CH-associated mutations in the BM/PB and LN, and the mutations postulated to accumulate at a later stage of lymphoma development (late mutations). The CH-associated mutations are shared between the primary lymphomas and the BM/PB and can be considered as early lesions in AITL/PTCL. The heatmaps show the top recurrent mutations in both categories. Stacked bar plots show the type of variants and the mutation frequency (relative to our cohort) for each of the major mutated genes in the LN and BM/PB samples. Pt: patient; T: tumor. (D) Comparison of the distribution of disruptive and missense mutations in the CH-associated and late mutations. Statistical significance was determined by (D) z test measuring proportion difference. *p<0.05; **p<0.01; ***p<0.001; ****p,0.0001; NS, not significant. P-Value<0.05 is considered statistically significant.
Figure 1. Analysis of genomic alterations by target sequencing panel for primary lymphomas and paired bone marrow/peripheral blood (BM/PB) in patients with angioimmunoblastic T-cell lymphoma (AITL) and peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS). (A) Presence of clonal hematopoiesis (CH) in patients with AITL and PTCL-NOS. Dot plots showing the detected variants and their variant allele frequencies (VAFs) in the AITL and PTCL-NOS (lymph node [LN]) and their matched BM/PB in representative AITL and PTCL-NOS cases with CH. The black circles indicate variants specific to the lymphomas, and the variants shared between the primary lymphomas and CH are highlighted in red. The variants attributed to lymphoma only are boxed. Additional detailed descriptions of these illustrative cases are provided in Appendix 1. (B) Venn diagram showing the distribution of the shared, lymphoma or BM/PB-specific variants identified in the diagnostic LN and paired BM/PB samples. The shared variants are defined as variants identified in both the primary lymphoma and the BM/PB, the latter as CH-related variants. The variants predicted to be due only to lymphoma involvement in BM/PB have been excluded (see also Figure 1—figure supplement 1A for the distribution of all variants). (C) Summary of the CH-associated mutations in the BM/PB and LN, and the mutations postulated to accumulate at a later stage of lymphoma development (late mutations). The CH-associated mutations are shared between the primary lymphomas and the BM/PB and can be considered as early lesions in AITL/PTCL. The heatmaps show the top recurrent mutations in both categories. Stacked bar plots show the type of variants and the mutation frequency (relative to our cohort) for each of the major mutated genes in the LN and BM/PB samples. Pt: patient; T: tumor. (D) Comparison of the distribution of disruptive and missense mutations in the CH-associated and late mutations. Statistical significance was determined by (D) z test measuring proportion difference. *p<0.05; **p<0.01; ***p<0.001; ****p,0.0001; NS, not significant. P-Value<0.05 is considered statistically significant.CC BY · PubMed Central
Figure 1—figure supplement 1. Angioimmunoblastic T-cell lymphoma (AITL) variants involving bone marrow/peripheral blood (BM/PB). (A) Venn diagram illustrates the distribution of the shared, and the lymph node (LN) or BM/PB-specific mutations. Note that the variants detected in the BM/PB due to lymphoma involvement are included in this diagram, in contrast to Figure 1B presented in the main text. (B) The type of variants and mutation frequencies (relative to the total number of cases) for the top six mutated genes identified in AITL/peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS) and matched BM/PB are shown. Different colors represent variant classifications as indicated. (C) Dot plot comparing variant allele frequencies (VAFs) of the clonal hematopoiesis (CH)-associated variants and those related to lymphoma involvement in the BM/PB specimens. Mean ± SEM of the VAFs is shown for each subgroup. (D) The proportion of the cases with or without BM or PB involvement by the neoplastic T-cells (IVS), or with/without CH, respectively, is summarized. Statistical significance in (C) was determined by t test. p-Value<0.05 is considered statistically significant.
Figure 1—figure supplement 1. Angioimmunoblastic T-cell lymphoma (AITL) variants involving bone marrow/peripheral blood (BM/PB). (A) Venn diagram illustrates the distribution of the shared, and the lymph node (LN) or BM/PB-specific mutations. Note that the variants detected in the BM/PB due to lymphoma involvement are included in this diagram, in contrast to Figure 1B presented in the main text. (B) The type of variants and mutation frequencies (relative to the total number of cases) for the top six mutated genes identified in AITL/peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS) and matched BM/PB are shown. Different colors represent variant classifications as indicated. (C) Dot plot comparing variant allele frequencies (VAFs) of the clonal hematopoiesis (CH)-associated variants and those related to lymphoma involvement in the BM/PB specimens. Mean ± SEM of the VAFs is shown for each subgroup. (D) The proportion of the cases with or without BM or PB involvement by the neoplastic T-cells (IVS), or with/without CH, respectively, is summarized. Statistical significance in (C) was determined by t test. p-Value<0.05 is considered statistically significant.CC BY · PubMed Central
Figure 1—figure supplement 2. Comparison of variant allele frequencies (VAFs) of the variants found in paired angioimmunoblastic T-cell lymphoma (AITL) and bone marrow/peripheral blood (BM/PB) samples. Red color highlights the clonal hematopoiesis (CH)-associated variants shared between the lymph node (LN) and BM/PB compartments. The black circles indicate variants specific to the lymphomas, the pink circles represent mutations specific to BM/PB. The cases that were not presented in Figures 1 and 3 are shown in this figure supplement. In patient #7, three mutations specific to BM were found in PLCG1, ACSL3, and CTTNBP2. These may represent novel CH-associated mutations. In patient #8, DNMT3A p.W330*, likely representing a minor CH clone, was also found specifically in the BM. In patient #24, an additional ASXL1 frameshift mutation was identified in the BM, which showed hypercellularity with increased granulopoiesis. In this patient, we appeared to capture the acquisition of a subclonal JAK2 V617F mutation during tumor progression, which was identified by the myeloid panel in the purified granulocytes from the PB but not detected in the BM 1 year before.
Figure 1—figure supplement 2. Comparison of variant allele frequencies (VAFs) of the variants found in paired angioimmunoblastic T-cell lymphoma (AITL) and bone marrow/peripheral blood (BM/PB) samples. Red color highlights the clonal hematopoiesis (CH)-associated variants shared between the lymph node (LN) and BM/PB compartments. The black circles indicate variants specific to the lymphomas, the pink circles represent mutations specific to BM/PB. The cases that were not presented in Figures 1 and 3 are shown in this figure supplement. In patient #7, three mutations specific to BM were found in PLCG1, ACSL3, and CTTNBP2. These may represent novel CH-associated mutations. In patient #8, DNMT3A p.W330*, likely representing a minor CH clone, was also found specifically in the BM. In patient #24, an additional ASXL1 frameshift mutation was identified in the BM, which showed hypercellularity with increased granulopoiesis. In this patient, we appeared to capture the acquisition of a subclonal JAK2 V617F mutation during tumor progression, which was identified by the myeloid panel in the purified granulocytes from the PB but not detected in the BM 1 year before.CC BY · PubMed Central
Figure 1—figure supplement 3. Overall and clonal hematopoiesis (CH)-related genomic alterations in the matched bone marrow/peripheral blood (BM/PB) samples. (A) The overall mutation profile in the BM/PB is shown. (B) The CH-related mutation profile identified in the BM/PB is shown after excluding the variants due to angioimmunoblastic T-cell lymphoma (AITL) involvement. Top bars on each plot indicate the numbers of the variants detected per sample. The percentage showing gene mutation frequency in the patient cohort is shown at the right. Sample IDs are indicated at the bottom of each plot. Variant classifications are indicated with different colors.
Figure 1—figure supplement 3. Overall and clonal hematopoiesis (CH)-related genomic alterations in the matched bone marrow/peripheral blood (BM/PB) samples. (A) The overall mutation profile in the BM/PB is shown. (B) The CH-related mutation profile identified in the BM/PB is shown after excluding the variants due to angioimmunoblastic T-cell lymphoma (AITL) involvement. Top bars on each plot indicate the numbers of the variants detected per sample. The percentage showing gene mutation frequency in the patient cohort is shown at the right. Sample IDs are indicated at the bottom of each plot. Variant classifications are indicated with different colors.CC BY · PubMed Central
Figure 1—figure supplement 4. Overall and lymphoma-specific genomic alterations in primary angioimmunoblastic T-cell lymphoma/peripheral T-cell lymphoma, not otherwise specified (AITL/PTCL-NOS). (A) Mutation plot showing the overall mutation profile in the lymph node (LN) tissues involved by AITL/PTCL-NOS. (B) Mutation plot showing the AITL/PTCL-NOS-specific mutations identified in lymphoma samples after excluding the clonal hematopoiesis (CH)-associated variants. Top bars on each plot indicate the numbers of variants detected per sample. The percentage at right shows the gene mutation frequency in the cohort of the patients. Sample IDs are indicated at the bottom of each plot. Variant classifications are indicated with different colors.
Figure 1—figure supplement 4. Overall and lymphoma-specific genomic alterations in primary angioimmunoblastic T-cell lymphoma/peripheral T-cell lymphoma, not otherwise specified (AITL/PTCL-NOS). (A) Mutation plot showing the overall mutation profile in the lymph node (LN) tissues involved by AITL/PTCL-NOS. (B) Mutation plot showing the AITL/PTCL-NOS-specific mutations identified in lymphoma samples after excluding the clonal hematopoiesis (CH)-associated variants. Top bars on each plot indicate the numbers of variants detected per sample. The percentage at right shows the gene mutation frequency in the cohort of the patients. Sample IDs are indicated at the bottom of each plot. Variant classifications are indicated with different colors.CC BY · PubMed Central
Figure 2. Late mutations in angioimmunoblastic T-cell lymphoma/peripheral T-cell lymphoma, not otherwise specified (AITL/PTCL-NOS) are enriched for C>A transversion substitutions possibly associated with smoking. Transition and transverse (Titv) plot showing overall distribution of the six types of substitutions in the clonal hematopoiesis (CH) (A) and late (B) missense mutations acquired during AITL/PTCL development, as well as fraction of these substitutions in each sample. The median is indicated by a horizontal line. Bar plot on the left showing single-nucleotide variant (SNV) classes and fraction of each substitution class among all missense mutations. (C) Side-by-side comparison of transition and transversion base substitutions acquired between the early CH-associated and late mutations. (D) Bar plot comparing the incidence rate of lung cancer between two age-matched/-adjusted populations indicated. Statistical significance was determined by (C, D) z test. *p<0.05; **p<0.01; ***p<0.001; ****p<0.00001; NS, not significant.
Figure 2. Late mutations in angioimmunoblastic T-cell lymphoma/peripheral T-cell lymphoma, not otherwise specified (AITL/PTCL-NOS) are enriched for C>A transversion substitutions possibly associated with smoking. Transition and transverse (Titv) plot showing overall distribution of the six types of substitutions in the clonal hematopoiesis (CH) (A) and late (B) missense mutations acquired during AITL/PTCL development, as well as fraction of these substitutions in each sample. The median is indicated by a horizontal line. Bar plot on the left showing single-nucleotide variant (SNV) classes and fraction of each substitution class among all missense mutations. (C) Side-by-side comparison of transition and transversion base substitutions acquired between the early CH-associated and late mutations. (D) Bar plot comparing the incidence rate of lung cancer between two age-matched/-adjusted populations indicated. Statistical significance was determined by (C, D) z test. *p<0.05; **p<0.01; ***p<0.001; ****p<0.00001; NS, not significant.CC BY · PubMed Central
Figure 2—figure supplement 1. Comparison two de novo mutational signatures from clonal hematopoiesis (CH)-associated angioimmunoblastic T-cell lymphoma (AITL) mutations with COSMIC Signatures. (A) Profile of two de novo mutational signatures extracted from CH-associated AITL mutations. (B) Heatmap showing similarity of two identified de novo mutation signatures with COSMIC Signature by analysis of cosine distance among these mutation patterns. Numbers on the bottom of the heatmap represent the number of COSMIC Signatures (SBS30, v2). (C) Estimated exposures of each AITL sample to the identified mutational patterns (Sign.01 and Sign.02).
Figure 2—figure supplement 1. Comparison two de novo mutational signatures from clonal hematopoiesis (CH)-associated angioimmunoblastic T-cell lymphoma (AITL) mutations with COSMIC Signatures. (A) Profile of two de novo mutational signatures extracted from CH-associated AITL mutations. (B) Heatmap showing similarity of two identified de novo mutation signatures with COSMIC Signature by analysis of cosine distance among these mutation patterns. Numbers on the bottom of the heatmap represent the number of COSMIC Signatures (SBS30, v2). (C) Estimated exposures of each AITL sample to the identified mutational patterns (Sign.01 and Sign.02).CC BY · PubMed Central
Figure 2—figure supplement 2. Similarity of two de novo mutational signatures from angioimmunoblastic T-cell lymphoma (AITL) late mutations with COSMIC Signatures. (A) Profiles of two de novo mutational signatures extracted from AITL late mutations. (B) Heatmap showing similarity of two identified de novo mutation signatures with COSMIC Signature by analysis of cosine similarity. (C) Estimated activities of the identified mutational signatures (LM_Sign01 and LM_Sign02) per sample according to estimated number of individual signature-associated mutations. LM: late mutations; Pt: patient.
Figure 2—figure supplement 2. Similarity of two de novo mutational signatures from angioimmunoblastic T-cell lymphoma (AITL) late mutations with COSMIC Signatures. (A) Profiles of two de novo mutational signatures extracted from AITL late mutations. (B) Heatmap showing similarity of two identified de novo mutation signatures with COSMIC Signature by analysis of cosine similarity. (C) Estimated activities of the identified mutational signatures (LM_Sign01 and LM_Sign02) per sample according to estimated number of individual signature-associated mutations. LM: late mutations; Pt: patient.CC BY · PubMed Central
Figure 2—figure supplement 3. Similarity of two de novo mutational signatures from the published T-follicular helper-peripheral T-cell lymphoma (TFH-PTCL) mutation dataset with COSMIC Signatures. (A) Profiles of two de novo mutational signatures extracted from published single-nucleotide variant (SNV) mutations from TFH-PTCL tumors (n = 44). (B) Heatmap showing cosine similarity of two identified de novo mutation signatures with 30 COSMIC Signatures. (C) Estimated exposures of each AITL sample to the identified mutational patterns (Kyoto_Sign01 and Kyoto_Sign02).
Figure 2—figure supplement 3. Similarity of two de novo mutational signatures from the published T-follicular helper-peripheral T-cell lymphoma (TFH-PTCL) mutation dataset with COSMIC Signatures. (A) Profiles of two de novo mutational signatures extracted from published single-nucleotide variant (SNV) mutations from TFH-PTCL tumors (n = 44). (B) Heatmap showing cosine similarity of two identified de novo mutation signatures with 30 COSMIC Signatures. (C) Estimated exposures of each AITL sample to the identified mutational patterns (Kyoto_Sign01 and Kyoto_Sign02).CC BY · PubMed Central
Figure 2—figure supplement 4. Comparing de novo signatures extracted from angioimmunoblastic T-cell lymphoma (AITL) late mutations and published T-follicular helper-peripheral T-cell lymphoma (TFH-PTCL) mutations. Heatmap showing cosine similarity of the de novo mutation patterns identified in the indicated mutation data set. LM: AITL late mutations.
Figure 2—figure supplement 4. Comparing de novo signatures extracted from angioimmunoblastic T-cell lymphoma (AITL) late mutations and published T-follicular helper-peripheral T-cell lymphoma (TFH-PTCL) mutations. Heatmap showing cosine similarity of the de novo mutation patterns identified in the indicated mutation data set. LM: AITL late mutations.CC BY · PubMed Central
Figure 2—figure supplement 5. Comparing de novo signatures extracted from angioimmunoblastic T-cell lymphoma (AITL) late mutations in the non-smokers to COSMIC Signatures. Heatmap showing cosine similarity of the de novo mutation patterns with the COSMIC Signatures.
Figure 2—figure supplement 5. Comparing de novo signatures extracted from angioimmunoblastic T-cell lymphoma (AITL) late mutations in the non-smokers to COSMIC Signatures. Heatmap showing cosine similarity of the de novo mutation patterns with the COSMIC Signatures.CC BY · PubMed Central
Figure 3. Angioimmunoblastic T-cell lymphoma (AITL) and concomitant hematologic neoplasms develop from common mutated hematopoietic precursor cells. (A–C) Dot plots comparing variant allele frequencies (VAFs) of the mutations identified in the AITL and the concomitant hematologic malignancies. Red dots show the variants shared between different hematologic neoplasms or entity in the same patient. Dark blue dots in (A) indicate the variants specially related to chronic myelomonocytic leukemia (CMML), and the black dots in (A) denote the AITL-specific variants. Schematic diagrams depicting hypothetical clonal evolution models of the tumors deriving from mutated hematopoietic stem cells (HSC) are also presented. In patient #5 (A), additional mutations besides the clonal hematopoiesis (CH)-associated mutations were identified and implicated in the disease progression to AITL and CMML, respectively. In patient #20, no additional mutations besides those mutated in HSC are identified. In patient #14, a mutated EZH2, indicated by green dot, is implicated in the progression to diffuse large B-cell lymphoma (DLBCL). In all three cases, there are mutations that are shared between the AITL and the concomitant myeloid or B lymphomas, supporting evolution of these neoplasms from a common precursor. PV: polycythemia vena; post-PV PMF, post-PV primary myelofibrosis; ITP: immune thrombocytopenia.
Figure 3. Angioimmunoblastic T-cell lymphoma (AITL) and concomitant hematologic neoplasms develop from common mutated hematopoietic precursor cells. (A–C) Dot plots comparing variant allele frequencies (VAFs) of the mutations identified in the AITL and the concomitant hematologic malignancies. Red dots show the variants shared between different hematologic neoplasms or entity in the same patient. Dark blue dots in (A) indicate the variants specially related to chronic myelomonocytic leukemia (CMML), and the black dots in (A) denote the AITL-specific variants. Schematic diagrams depicting hypothetical clonal evolution models of the tumors deriving from mutated hematopoietic stem cells (HSC) are also presented. In patient #5 (A), additional mutations besides the clonal hematopoiesis (CH)-associated mutations were identified and implicated in the disease progression to AITL and CMML, respectively. In patient #20, no additional mutations besides those mutated in HSC are identified. In patient #14, a mutated EZH2, indicated by green dot, is implicated in the progression to diffuse large B-cell lymphoma (DLBCL). In all three cases, there are mutations that are shared between the AITL and the concomitant myeloid or B lymphomas, supporting evolution of these neoplasms from a common precursor. PV: polycythemia vena; post-PV PMF, post-PV primary myelofibrosis; ITP: immune thrombocytopenia.CC BY · PubMed Central
Figure 4. Pathogenic TET2 mutation status in the bone marrow/peripheral blood (BM/PB) samples is a predictive biomarker for concomitant hematologic neoplasms in angioimmunoblastic T-cell lymphoma (AITL) patients. Kaplan–Meier analysis of concomitant hematologic neoplasm-free survival in AITL or AITL-related patients based on TET2 mutation status in the BM/PB. Concomitant hematologic neoplasm-free survival of AITL patients can be stratified based on absent/low or high TET2 mutation burden subgroups. p-Value was calculated by log-rank test, and p-value<0.05 is considered statistically significant. In one case, the second hematologic malignancy (PV) preceded the development of AITL.
Figure 4. Pathogenic TET2 mutation status in the bone marrow/peripheral blood (BM/PB) samples is a predictive biomarker for concomitant hematologic neoplasms in angioimmunoblastic T-cell lymphoma (AITL) patients. Kaplan–Meier analysis of concomitant hematologic neoplasm-free survival in AITL or AITL-related patients based on TET2 mutation status in the BM/PB. Concomitant hematologic neoplasm-free survival of AITL patients can be stratified based on absent/low or high TET2 mutation burden subgroups. p-Value was calculated by log-rank test, and p-value<0.05 is considered statistically significant. In one case, the second hematologic malignancy (PV) preceded the development of AITL.CC BY · PubMed Central
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  1. Cheng, 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

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Metadata

Authors
Cheng, S., Zhang, W., Inghirami, G., & Tam, W.
Affiliations
Weill Cornell Medicine (US)
Corresponding author
Tam, Wayne
ORCID
Venue
eLife
Publisher
eLife Sciences Publications, Ltd
Published
2021-09-29
Volume
10
ISSN
2050-084X (ISSN-L)
DOI
10.7554/elife.66395
PMC
PMC8480981
OpenAlex
W3202146560
Cited by
37
References
50
Citations by year
2026: 5 · 2025: 6 · 2024: 9 · 2023: 12 · 2022: 5
FWCI
5.70
Type
journal article
Language
en
Open access
Licence
Locations
  • eLife (publishedVersion, cc-by)
  • medRxiv (submittedVersion)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Cancer Institute (R01 CA194547)
Topics
  • Health Sciences › Medicine › Pathology and Forensic Medicine › Lymphoma Diagnosis and Treatment
  • Health Sciences › Medicine › Hematology › Acute Myeloid Leukemia Research
  • Health Sciences › Medicine › Dermatology › Cutaneous lymphoproliferative disorders research
Keywords
angioimmunoblastic T-cell lymphoma, peripheral T-cell lymphoma, not otherwise specified, clonal hematopoiesis, smoking, mutational signatures, APOBEC mutagenesis, allele burden, diffuse large B-cell lymphoma, myeloid neoplasms, lymphomagenesis, risk stratification biomarkers
MeSH terms
Tobacco Smoking, Clonal Hematopoiesis, DNA Mutational Analysis, Female, Humans, Immunoblastic Lymphadenopathy, Male, Mutation, Lymphoma, T-Cell, Gene Expression Profiling

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Cheng, S., Zhang, W., Inghirami, G., & Tam, W. (2021). Mutation analysis links angioimmunoblastic T-cell lymphoma to clonal hematopoiesis and smoking. eLife. https://doi.org/10.7554/elife.66395