https://orcid.org/0000-0002-2358-4278
Inflammatory form of Waldenström macroglobulinemia (iWM) predicts outcomes after immuno-chemotherapy and Bruton tyrosine kinase inhibitors, but its origin is unknown. Here, we unravel increased clonal hematopoiesis in patients with iWM (61% vs 23% in noninflammatory WM), suggesting a contribution of environmental cells to iWM.
TO THE EDITOR:
Waldenström macroglobulinemia (WM) is an indolent, chronic mature B-cell lymphoma predominant in older patients, characterized by infiltrating lymphoplasmacytic cells into the bone marrow (BM) and a monoclonal immunoglobulin M (IgM).1 The most common somatic mutation in WM is the gain-of-function mutation in MYD88 (MYD88L265P; 90%),2 followed by gain-of-function CXCR4 mutations (∼40%)3 and a cytogenetic anomaly, a deletion on chromosome 6 (del6q; ∼40%).4 When patients with WM become symptomatic, immunochemotherapy and Bruton tyrosine kinase inhibitors (BTKis) are the 2 main therapeutic options.5
Recently, a focus has been placed on the inflammatory form of WM (iWM), marked by an unexplained inflammatory syndrome (C-reactive protein [CRP] ≥20 mg/L), present in one-third of patients at therapy initiation.6-8 The origin of this inflammation is still unknown and could encompass intrinsic and extrinsic factors to malignant cells.9,10 iWM is associated with a higher frequency of del6q and a lower occurrence of CXCR4 mutations.7,8,11 iWM is associated with a shorter time to next treatment when treated with immunochemotherapy6,12 and longer time to next treatment when treated with BTKi.8
Clonal hematopoiesis (CH) is a clonal expansion of hematopoietic stem and progenitor cells that increases with age and is related to somatic mutations in individual genes, as detected by next-generation sequencing (NGS). CH is a premalignant state that can progress into myeloid neoplasms.13 CH is also associated with nonmalignant inflammatory diseases and increased mortality.14,15 Recently, CH mutation in asymptomatic WM was associated with earlier progression into symptomatic WM.16 However, the CH status has never been evaluated in the context of iWM. To investigate this issue, a multicentric cohort was established.
Patients with a confirmed diagnosis of WM1 and with NGS data on bulk BM or peripheral blood samples were included between 2007 and 2024 in 7 French centers (n = 704): Cochin, Henri Mondor, Necker, Tenon, Pitié-Salpêtrière, Saint-Louis, and Lille hospitals. Patients with confirmed myeloid neoplasm, isolated TP53 mutation (impossibility to class between CH or WM mutation related), or active solid cancer were excluded. iWM was identified by at least 2 CRP measurements ≥20 mg/L without other explanations (eg, infection and active inflammatory complication).8 Sampling was performed at active relapse for patients who had already received WM therapy. The techniques for WM genomics have been previously described.8,17 Patient data were collected in accordance with the Declaration of Helsinki and approved by a French national protection committee (no.°2020-A01928-31). For Lille samples, the Institutional Review Board of the tumor bank of Lille hospital approved the collection (CSTMT292). CH mutations were detected using NGS of a panel of 62 genes involved in myeloid malignancies, with a cutoff of variant allele frequency (VAF) ≥2% (supplemental Material, available on the Blood website).18 Statistical analyses were conducted using R v4.0.4 (supplemental Material).
Among the 125 patients with WM with NGS, 18 patients (14%) were excluded due to concurrent myeloid neoplasm (n = 15) or active solid cancer/isolated TP53 mutation/unconfirmed WM diagnosis (one each; supplemental Figure 1). Of the 107 remaining patients (NGS cohort), 46 had iWM at the NGS timing (including 6 who did not present iWM at initial but only at active relapse), and 61 had noninflammatory WM. The median age was 68 years (interquartile [IQR], 59-74) at diagnosis and 73 years (IQR, 63-78) at NGS analysis. The median follow-up for the NGS cohort from diagnosis was 5 years (IQR, 3.1-12) and after NGS was 1.5 years (IQR, 0.6-3).
A total of 61 CH mutations were identified in 41 patients (38%; Figure 1A-C; supplemental Table 1), with the majority having 1 (n = 27 [66%]) or 2 different mutations (n = 10 [24%]). DNMT3A, TET2, and ASXL1 (DTA) mutations accounted for 67% of mutations and were detected in 31 patients (29%). iWM was significantly associated with CH mutations (iWM 61% vs noninflammatory WM 23%; P < .001; Table 1). The same held true when restricted to DTA mutations (43% vs 18%; P = .02) and among patients without exposure to cytotoxic agents (54% vs 18%; P = .003). The median CRP levels at the NGS timing were 25 mg/L for patients with CH vs 10 mg/L for those without CH (P = .007; Figure 1A). No difference was observed for CRP levels between patients with iWM with or without CH (35 vs 41 mg/L; P = .74). CH prevalence was associated with higher age (median, 77 vs 70 years; P < .001) and previous exposure to cytotoxic agents (P = .01), especially for PPM1D mutations (5/6 exposed; supplemental Figure 2A). There was no other feature (clinical, demographic, or WM characteristics) associated with the presence of CH mutations. The median VAF of CH mutations was 5% (IQR, 3%-12%; Figure 1D), with no direct correlation observed between CRP levels at sequencing and the VAF of the higher mutation (r = –0.04; P = .79). There was no association between WM infiltration and CH VAF or detection in BM (r = –0.06; P = .82) and peripheral blood samples (r = –0.48; P = .27; supplemental Figure 2B). In a generalized linear model with age (odds ratio [OR], 1.05; P = .04), sample type (OR, 3.3; P = .02), and exposure to cytotoxic agents (OR, 1.5; P = .43), iWM was still associated with CH (OR, 4.8; P < .001; Figure 1E). No overall survival difference was observed based on the presence of CH (P = .79).
iWM is associated with CH. Red represents patients with iWM; and blue, patients with noninflammatory WM (non-iWM). (A) CRP level at sequencing between patients with/without CH detected. The shape represents the type of CH mutation: triangle for DTA, square for the other one, and round for the absence of CH mutation detected. (B) Number of CH mutations detected based on the inflammatory status of patients with WM. (C) Type of CH mutation detected on multiple patients based on the inflammatory status of patients with WM. Genes with a mutation that occurred only once were CBL, SETBP1, RUNX1, NRAS, KRAS, and KDM6A. (D) VAF of each CH mutation detected in iWM and non-iWM. (E) The generalized linear model of CH presence is based on age, prior chemotherapy/radiotherapy exposure, type of sample, and inflammatory status of WM. (F) CRP level at sequencing based on CH mutation and del6q in WM (17 iWM and 24 non-iWM with follow-up). The size of points represents the VAF of CH mutations. The red number represents the proportion of iWM in each subcategory.
iWM is associated with CH. Red represents patients with iWM; and blue, patients with noninflammatory WM (non-iWM). (A) CRP level at sequencing between patients with/without CH detected. The shape represents the type of CH mutation: triangle for DTA, square for the other one, and round for the absence of CH mutation detected. (B) Number of CH mutations detected based on the inflammatory status of patients with WM. (C) Type of CH mutation detected on multiple patients based on the inflammatory status of patients with WM. Genes with a mutation that occurred only once were CBL, SETBP1, RUNX1, NRAS, KRAS, and KDM6A. (D) VAF of each CH mutation detected in iWM and non-iWM. (E) The generalized linear model of CH presence is based on age, prior chemotherapy/radiotherapy exposure, type of sample, and inflammatory status of WM. (F) CRP level at sequencing based on CH mutation and del6q in WM (17 iWM and 24 non-iWM with follow-up). The size of points represents the VAF of CH mutations. The red number represents the proportion of iWM in each subcategory.
Comparison between patients with WM with and without CH detected
| No CH detected (n = 66) | CH detected (n = 41) | P value | |
|---|---|---|---|
| Men/women | 42 (64%)/24 (36%) | 27 (66%)/14 (34%) | .98 |
| Prior cardiovascular history∗ | 7 (11%) | 8 (20%) | .36 |
| Prior solid cancer history | 10 (15%) | 3 (7%) | .36 |
| WM characteristics | |||
| Year of diagnosis | 2017 (2009-2021) | 2017 (2011-2020) | .83 |
| Age at diagnosis, y | 68 (59-72) | 69 (59-75) | .78 |
| Kappa/Lambda | 84%/16% | 83%/17% | 1 |
| MYD88 mutation | 58/61 (95%) | 34/35 (97%) | 1 |
| CXCR4 mutation | 16/50 (32%) | 5/23 (22%) | .53 |
| Del6q presence | 19/51 (37%) | 12/27 (44%) | .71 |
| Age at first line of treatment†, y | 71 (67-75) | 71 (63-77) | .68 |
| Inflammatory WM status at first line‡ | 7 (22%) | 17 (61%) | .005 |
| First-line used† | 27/32 (84%) | 23/29 (79%) | .59 |
| Rituximab + alkylants | 18 (67%) | 15 (65%) | |
| BTKi | 1 (4%) | 1 (4%) | |
| Chlorambucil | 3 (11%) | 5 (22%) | |
| Others | 5 (18%)§ | 2 (9%)|| | |
| Characteristics at NGS | |||
| Age at NGS, y | 70 (62-75) | 77 (68-80) | <.001 |
| Type of sample, BM/PB | 12 (19%)/54 (82%) | 18 (44%)/23 (56%) | .008 |
| Timing of NGS | .05 | ||
| Asymptomatic WM | 26 (39%) | 13 (32%) | |
| Before first treatment | 27 (42%) | 11 (27%) | |
| After cytotoxic treatment | 13 (20%) | 17 (41%) | .01 |
| CRP at sequencing, mg/L | 10 (2-22) | 25 (11-56) | .007 |
| Inflammatory WM status | 18 (27%) | 28 (68%) | <.001 |
| No CH detected (n = 66) | CH detected (n = 41) | P value | |
|---|---|---|---|
| Men/women | 42 (64%)/24 (36%) | 27 (66%)/14 (34%) | .98 |
| Prior cardiovascular history∗ | 7 (11%) | 8 (20%) | .36 |
| Prior solid cancer history | 10 (15%) | 3 (7%) | .36 |
| WM characteristics | |||
| Year of diagnosis | 2017 (2009-2021) | 2017 (2011-2020) | .83 |
| Age at diagnosis, y | 68 (59-72) | 69 (59-75) | .78 |
| Kappa/Lambda | 84%/16% | 83%/17% | 1 |
| MYD88 mutation | 58/61 (95%) | 34/35 (97%) | 1 |
| CXCR4 mutation | 16/50 (32%) | 5/23 (22%) | .53 |
| Del6q presence | 19/51 (37%) | 12/27 (44%) | .71 |
| Age at first line of treatment†, y | 71 (67-75) | 71 (63-77) | .68 |
| Inflammatory WM status at first line‡ | 7 (22%) | 17 (61%) | .005 |
| First-line used† | 27/32 (84%) | 23/29 (79%) | .59 |
| Rituximab + alkylants | 18 (67%) | 15 (65%) | |
| BTKi | 1 (4%) | 1 (4%) | |
| Chlorambucil | 3 (11%) | 5 (22%) | |
| Others | 5 (18%)§ | 2 (9%)|| | |
| Characteristics at NGS | |||
| Age at NGS, y | 70 (62-75) | 77 (68-80) | <.001 |
| Type of sample, BM/PB | 12 (19%)/54 (82%) | 18 (44%)/23 (56%) | .008 |
| Timing of NGS | .05 | ||
| Asymptomatic WM | 26 (39%) | 13 (32%) | |
| Before first treatment | 27 (42%) | 11 (27%) | |
| After cytotoxic treatment | 13 (20%) | 17 (41%) | .01 |
| CRP at sequencing, mg/L | 10 (2-22) | 25 (11-56) | .007 |
| Inflammatory WM status | 18 (27%) | 28 (68%) | <.001 |
Values shown in bold type are P < .05 (significant threshold). PB, peripheral blood.
Coronary heart disease or cerebrovascular disease or peripheral arterial disease.
Missing data for 46 patients.
Exclusion of noninflammatory WM with NGS at inflammatory relapse (n = 6).
Rituximab alone (n = 3) and bortezomib-based regimen (n = 2).
Rituximab alone (n = 1) and cladribine (n = 1).
Del6q was previously associated with iWM7,8,11 and was confirmed in our NGS cohort (iWM, 58% vs noninflammatory WM, 28%; P < .001). However, the presence of CH, either considering all genes or only DTA, was not associated with del6q (P = .71 and P = .54). Investigating the interaction between iWM, del6q, and CH, CRP levels were higher in patients with both del6q and CH than in patients with only del6q or CH (P = .005; Figure 1F). This suggests a possible synergistic effect between del6q and CH in terms of inflammation in patients with WM involving the contribution of both malignant and environmental cells. An increase of proinflammatory cytokines (interleukin-1b and interleukin-6) was previously observed in CH carriers14 and in WM; these cytokines are associated with del6q.11,19 Moreover, these cytokines were reported to activate the MYD88-NFκB pathway, promoting immunoglobulin M production as well as cell survival and proliferation.20
To our knowledge, this is the first study associating CH, especially DTA mutations, with iWM. This association, especially with TET2 mutations, was also found in other neoplasms, for example, inflammatory myelodysplastic syndromes,21 but not in Schnitzler syndrome, an immunoglobulin M autoinflammatory disease.22 In a previous study of NGS in WM from the Dana-Farber, the prevalence of DTA mutations was 14%.16 Our analysis reveals a heightened prevalence of DTA mutations (29%), possibly attributable to the higher age at NGS in our cohort (median of 73 years, compared with 67 years in the Dana-Farber study), as also described in healthy older patients14,23 and possibly the enrichment of iWM in our cohort (CRP not evaluated in the Dana-Farber cohort). Both studies are retrospective, with limited follow-up, heterogeneous sampling, and a lack of multiple time point assessments. Prospective studies, such as clinical trials or biobank analyses with longitudinal sampling, should be performed to validate these findings. Such studies would also allow for sequential analyses to evaluate CH dynamics under therapy in iWM, assess the relationship with cytokine level modifications, and explore the impact of CH on therapeutic response.
In conclusion, we identified a high prevalence of CH (61%) in iWM. Recently, the inflammation was highlighted in the progression of early WM,24 and CH could be a key in the early phase of WM. Given the improved BTKi response in iWM,8 there is a need to integrate this entity with other predictive factors of therapeutic response to BTKi (eg, extracellular signal-regulated kinase pathway).25
Acknowledgments
The authors thank Biobank for Research in Translational Hematology, Saint-Louis Hospital (Assistance Publique-Hôpitaux de Paris) for helping with biomolecular analysis; and the patients with WM and their families, as well as the nurses and physicians who cared for them.
P.-E.D. received financial support from the Institut Thématique Multi-Organismes Cancer of Aviesan within the 2021-2030 Cancer Control Strategy framework on funds administered by INSERM (grant FRFT-Doc-2022). K.B. was a recipient of a Fondation pour la Recherche Médicale grant (EQU202203014627).
Authorship
Contribution: P.-E.D., S.P., S.H., M.P., B.A., E.C., M.E., and K.B. contributed to conception and design; P.-E.D., S.P., S.H., M.P., M.T., C.F., N.F., D.E., W.P., L.C., G.L., L.W., B.R., A. Talbot, T.V., F.T., A. Terré, A.B., M.M., D.K., L.F., F.D., C.B., F.N.-K., J.D., W.C., D.B., O.H., D.R.-W., O.K., E.C., and B.A. contributed to the collection and assembly of data; P.-E.D., S.P., M.P., M.T., B.A., E.C., M.E., and K.B. contributed to data analysis and interpretation; P.-E.D., S.H., N.F., D.E., L.W., B.R., A. Talbot, T.V., F.T., A.B., M.M., D.K., L.F., J.D., D.B., D.R.-W., O.H., and B.A. took care of patients; P.-E.D., S.P., E.C., D.R.-W., B.A., M.E., and K.B. wrote the manuscript; M.P., M.T., C.F., W.P., L.C., G.L., F.D., C.B., E.C., and S.P. conducted biomolecular analysis; and all authors read and approved the final version of this manuscript.
Conflict-of-interest disclosure: The authors declare no competing financial interests.
Correspondence: Pierre-Edouard Debureaux, INSERM U1160, Institut de Recherche Saint-Louis, 1 Avenue Claude Vellefaux, 75010 Paris, France; email: pierre-edouard.debureaux@aphp.fr.
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