Abstract
Background
The association between clonal hematopoiesis (CH) and nonmyeloid subsequent malignant neoplasms (SMNs) after autologous hematopoietic cell transplantation (HCT) has not been explored.
Methods
This was a retrospective cohort study of 1931 consecutive patients who underwent HCT between 2010 and 2016 at a single center. DNA from pre-HCT mobilized blood products was sequenced to identify CH variants (variant allele frequency [VAF] ≥2%). The primary outcome was 8-year(y) cumulative incidence of nonmyeloid SMNs. Multivariable regression analysis was used to evaluate the association between CH and nonmyeloid SMNs, as well as cause-specific mortality.
Results
Median age at HCT was 58.8 y (range = 18.4-78.1 y); 389 patients (20.1% of the cohort) had at least 1 CH variant and 94 (4.9%) had ≥2 variants. The 8 y cumulative incidence of nonmyeloid SMNs was significantly higher in patients with CH compared with those without (15.1% vs 7.2%, P < .001), and increased by VAF: 7.2% (VAF <2%), 14.0% (VAF 2% to <10%), 19.4% (VAF ≥10%); P = .001. Patients with CH had a 2-fold increased risk of nonmyeloid SMNs (standardized incidence ratio = 1.9), compared with the general population. In multivariable analysis, CH was an independent and significant risk factor for nonmyeloid SMNs (hazard ratio [HR] = 1.72, 95% confidence interval [CI] = 1.15 to 2.59). Finally, patients with CH had significantly worse survival, primarily due to the higher risk of nonrelapse mortality (HR = 2.97, 95% CI = 1.90 to 4.64).
Conclusions
CH was significantly associated with the risk of nonmyeloid SMNs after HCT, and the magnitude of association increased by VAF. Clonal hematopoiesis may serve as a biomarker for identifying HCT survivors at higher risk for developing nonmyeloid SMNs.
Introduction
Autologous hematopoietic cell transplantation (HCT) is a highly effective treatment for eligible patients with plasma cell dyscrasias (PCD) or relapsed/refractory lymphoma, resulting in a growing number of long-term survivors.1,2 However, these HCT survivors are at higher risk of developing subsequent malignant neoplasms (SMNs) compared with age- and sex-matched individuals in the general population, attributed in part to the cumulative genotoxic effects of pre-HCT and HCT-related therapies (eg, chemotherapy [alkylators, topoisomerase inhibitors], radiotherapy [RT]).3,4 Among HCT survivors, SMNs have emerged as the leading cause of late nonrelapse mortality,5-9 and this has coincided with a notable increase in older patients referred for autologous HCT,10 a population enriched with individuals at risk of aging-related malignancy at baseline.
Clonal hematopoiesis (CH) has been recognized as an aging phenomenon and refers to the clonal expansion of hematopoietic stem cells harboring somatic mutations in leukemogenic genes.11,12 CH has been implicated in a wide spectrum of aging-related diseases, including cardiovascular diseases,13-18 endocrine disorders,19 organ dysfunctions such as fatty liver disease,20 and, in a minority of cases, clonal progression to myeloid malignancies.12 Emerging evidence also links CH to solid malignancies in the general population,21,22 potentially through mechanisms involving tumor microenvironment modulation, chronic inflammation, and immune dysfunction. In patients undergoing HCT, CH is common (prevalence = ∼20%-30%) and is an established risk factor for myeloid SMNs.23-27 A recent study involving patients with Hodgkin lymphoma undergoing autologous HCT reported that CH was associated with a 4.5-fold increased risk of developing therapy-related myeloid neoplasms.26 However, the role of CH in nonmyeloid SMNs, which represent the most common group of SMNs in long-term HCT survivors, has not been well characterized.
Methods
Study population and clinical variables
This retrospective cohort study included patients diagnosed with PCD or lymphoma who underwent their first autologous HCT at City of Hope (CoH) between 2010 and 2016. Patients were included if they had cryopreserved mobilized peripheral blood stem cell (PBSC) products available for DNA sequencing, representing 1931 of 1948 eligible patients (99.1%). Comprehensive clinical data including disease and HCT-related factors were abstracted from electronic medical records (EMRs), as previously described.28 Remission status at HCT was based on established clinical guidelines.29 The CoH Institutional Review Board approved the study and granted a waiver of informed consent and HIPAA authorization.
Nonmyeloid SMNs were defined as malignant skin cancers (excluding basal cell carcinoma and squamous cell carcinoma), all other nonskin solid malignancies, and de novo lymphomas. De novo lymphoma was defined as a new diagnosis of a lymphoma subtype distinct from the primary malignancy. Information on cancer diagnoses was obtained and validated through 2 primary sources: (1) the California Cancer Registry, which requires reporting of all cancers except nonmelanoma skin cancer under state law and (2) EMR abstraction. Nonmyeloid SMNs were classified based on anatomical locations, per the EMR. To ensure adequate and thorough follow-up after HCT, a standardized protocol was employed.30,31 Vital status and cause of death were ascertained through the National Death Index and review of the EMR. Relapse-related mortality was defined as deaths attributable to the primary disease (eg, lymphoma or PCD), while nonrelapse mortality (NRM) included deaths from all other causes, including SMN.
Next-generation sequencing and CH variant calling
Targeted sequencing of the DNA extracted from cryopreserved mobilized PBSC was conducted with a QIAseq amplicon-based panel covering 108 CH-associated genes, including 16 common cancer predisposition genes (eg, BRCA, GATA; Qiagen; Table S1), with an average read depth of 560 ×. Variant calling and annotation were done as described,30,31 following guidelines from the Catalog of Somatic Mutations in Cancer for variant classification and CH identification.32,33 Clonal hematopoiesis was defined as variant allele frequency (VAF) ≥2% based on established guidelines.11,32
Statistical analysis
We compared patients with and without CH, focusing on: patient demographics, clinical parameters, history of malignancy (unrelated to HCT indication), primary disease characteristics (subtypes, remission status at HCT), pre-HCT treatments associated with risk of nonmyeloid SMNs (eg, RT), and HCT-specific variables (conditioning, PBSC mobilization, and PBSC CD34+ cell count). Categorical variables were analyzed using χ2 tests, and continuous variables were evaluated with Wilcoxon rank-sum tests in univariable analyses. Multivariable logistic regression was performed to identify variables associated with the likelihood (odds ratio [OR]) of having CH, incorporating factors with P<.1 from the univariable analyses.
Given the latency of nonmyeloid SMNs, follow-up was extended to 8 years (y) post-HCT. Cumulative incidence of nonmyeloid SMNs was calculated taking into consideration the competing risk of death. Time to risk was calculated from the date of HCT to the earliest of the following: diagnosis of a nonmyeloid SMN, date of death, last known date the patient was alive or 8 y post-HCT (censor time). In addition, exploratory analyses were conducted to evaluate the relationship between nonmyeloid SMN and CH-related variables, including (1) specific CH-associated genes; (2) VAF categories, defined as VAF <2%, VAF 2% to ≤10%, and VAF >10%; (3) the number of CH variants categorized as having 0, 1, or ≥2 mutations. For patients harboring multiple CH mutations, VAF was categorized based on the highest observed VAF among all mutations identified.
We derived age- and sex-specific rates of cancers using the SEER*Stat software (version 8.4.3). These age- and sex-specific rates were then used to calculate the expected number of cancer cases in our cohort. The standardized incidence ratio (SIR) for nonmyeloid SMNs was calculated by obtaining the ratio of the observed and expected cases.34,35 The 95% confidence intervals (CIs) for SIR were estimated. Absolute excess incidence of cancer per 10 000 patients was calculated as (observed case−expected case)/total person-year × 10 000.
Fine–Gray subdistribution hazard model was used to estimate subdistribution hazard ratios (sHRs) and corresponding 95% confidence intervals (CIs) for risk of developing nonmyeloid SMN. Univariate analyses examined the risk associated with baseline clinical and HCT-related variables, as well as CH. Variables with a P < .1 in the univariable analysis were included in the multivariable model, which was refined using backward stepwise elimination to derive the final model. Additionally, we investigated the risk for developing specific cancer types by CH status, with other cancers or death treated as competing risk.
The Kaplan–Meier method was used to examine the impact of CH on overall survival; log-rank tests were used to compare survival curves. Multivariable Cox regression was used to assess the association between CH and overall survival, whereas Fine–Gray regression was used to assess the relationship between CH and cause-specific mortality, taking into consideration of death as competing risk. All analyses were performed using SAS Version 9.4 software (SAS Institute, Cary, NC). All statistical analyses were 2-sided, and a P < .05 was considered statistically significant.
Results
Patient characteristics
The demographic and clinical characteristics of the overall cohort (n = 1931) are summarized in Table 1. The median age at HCT was 58.8 years (range = 18.4-78.1); the majority were male (59.1%) and had a high (≥3) HCT-CI score (46.1%). The racial and ethnic distribution of the cohort was: 51.5% non-Hispanic White, 25.4% Hispanic, 10.9% Asian, and 12.2% Black or other. There were 1070 (55.4%) patients who underwent HCT for PCD and the remaining 881 (44.6%) were treated for lymphoma (9.9% Hodgkin lymphoma, 34.8% non-Hodgkin lymphoma); 18.5% had previously received RT, and 37.4% were in complete remission (CR). Overall, 8.8% of the cohort had a history of another malignancy that was unrelated to the HCT indication.
Table 1.
Demographic and clinical characteristics of patients with hematopoietic cell transplant (HCT).
| Total (N = 1931) No. (%) | CH (n = 389) No. (%) | No CH (n = 1542) No. (%) | P | |
|---|---|---|---|---|
| Median age at HCT (range), y | 58.8 (18.4-78.1) | 63.4 (34.2-77.3) | 57.3 (18.4-78.1) | <.001 |
| Sex | ||||
| Female | 789 (40.9%) | 159 (40.9%) | 630 (40.9%) | 1.0 |
| Male | 1142 (59.1%) | 230 (59.1%) | 912 (59.1%) | |
| Race/Ethnicity | ||||
| Asian | 211 (10.9%) | 54 (13.9%) | 157 (10.2%) | <.001 |
| Hispanic | 490 (25.4%) | 70 (18.0%) | 420 (27.2%) | |
| Non-Hispanic White | 994 (51.5%) | 229 (58.9%) | 765 (49.6%) | |
| Black/other | 236 (12.2%) | 36 (9.3%) | 200 (13.0%) | |
| Median BMI (kg/m2, range) | 27.8 (15.8-56.6) | 27.1 (17.4-56.6) | 28.0 (15.8-53.6) | .005 |
| HCT-CI | ||||
| 0 | 419 (21.7%) | 61 (15.7%) | 358 (23.2%) | .005 |
| 1-2 | 603 (31.2%) | 128 (32.9%) | 475 (30.8%) | |
| 3 and above | 909 (47.1%) | 200 (51.4%) | 709 (46.0%) | |
| Diagnosis | ||||
| Hodgkin lymphoma | 189 (9.8%) | 8 (2.1%) | 181 (11.7%) | <.001 |
| Non-Hodgkin lymphoma | 672 (34.8%) | 178 (45.8%) | 494 (32.0%) | |
| Plasma dyscrasia | 1070 (55.4%) | 203 (52.2%) | 867 (56.2%) | |
| Multiple myeloma | 1027 (53.2%) | 199 (51.2%) | 828 (53.7%) | |
| Other | 43 (2.2%) | 4 (1.0%) | 39 (2.5%) | |
| Pre-HCT radiation | ||||
| Yes | 356 (18.5%) | 70 (18.0%) | 286 (18.6%) | .793 |
| No | 1573 (81.5%) | 319 (82.0%) | 1254 (81.4%) | |
| Remission status at HCT | ||||
| CR | 726 (37.6%) | 146 (37.5%) | 580 (37.6%) | 1.0 |
| Not in CR | 1205 (62.4%) | 243 (62.5%) | 962 (62.4%) | |
| Conditioning regimen | ||||
| Melphalan | 1070 (55.4%) | 203 (52.2%) | 867 (56.2%) | <.001 |
| BEAM | 607 (31.4%) | 159 (40.9%) | 448 (29.1%) | |
| CBV | 205 (10.6%) | 20 (5.1%) | 185 (12.0%) | |
| Other | 49 (2.5%) | 7 (1.8%) | 42 (2.7%) | |
| PBSC CD34+ count | ||||
| >3 × 106 cells/kg | 1787 (92.5%) | 354 (91.0%) | 1433 (92.9%) | 236 |
| ≤3 × 106 cells/kg | 144 (7.5%) | 35 (9.0%) | 109 (7.1%) | |
| Pre-HCT history of malignancy | 170 (8.8%) | 59 (15.2%) | 111 (7.2%) | <.001 |
Abbreviations: BEAM = carmustine (BCNU), etoposide, aracytin and melphalan; BMI = body mass index (calculated from height and weight at the time of HCT); CBV = cyclophosphamide, BCNU (carmustine), and VP-16 (etoposide); CH = clonal hematopoiesis; CI = comorbidity index; CR = complete remission; KPS = Karnofsky Performance Status; PBSC = peripheral blood stem cell.
There were 389 patients (20.1% of the cohort) with at least 1 CH variant and 95 (4.9%) with ≥2 variants (Figure 1, A). The prevalence of CH increased with age at HCT: 4.8% (<50 y), 17.6% (50-59 y), 28.7% (60-69 y), and 42.5% (≥70 y); Figure 1, B. The most frequently mutated gene was DNMT3A (37.8%), followed by TET2 (15.7%), PPM1D (13.6%), ASXL1 (8.3%), and TP53 (6.5%; Figures 1, C, D and S1). The comutational pattern across genes, color-coded by VAF categories, is depicted in Figure 1, E. In the multivariable analysis, older (>58.8 y [median]) age at HCT (OR = 2.93; 95% CI = 2.27 to 3.80), a diagnosis of non-Hodgkin lymphoma (OR = 1.62; 95% CI = 1.28 to 2.07), and a history of malignancy unrelated to the transplant indication (OR = 1.77; 95% CI = 1.23 to 2.55) were independently associated with higher odds of having CH; Table S2.
Figure 1.
Characteristics of clonal hematopoiesis (CH) mutations. A) Number of patients harboring CH mutations in 1, 2, and 3+ different genes; B) Prevalence of CH according to age groups at transplantation; C) Number of patients with specific gene mutations; D) Spectrum of variant allele frequency (VAF) in top 5 most frequently mutated genes; E) Comutation oncoplot showing mutations present in all 186 patients: each column represents a single patient. Green denotes VAF 2% to ≤5%, blue denotes VAF 5% to ≤10%, and red denotes >10%. VAF cutoff used to call mutations was 0.02.
CH and the incidence of nonmyeloid SMN
Overall, HCT survivors had a higher nonmyeloid SMN incidence rate compared with the general population (SIR = 1.44; 95% CI = 1.18 to 1.71), and the rate was higher among patients with CH (SIR = 1.89; 95% CI = 1.28 to 2.48) compared with those without CH (SIR = 1.30; 95% CI = 1.01 to 1.59); Table S3. This trend of higher incidence rate among patients with CH was consistently observed across individual cancer categories; Table S3. Among patients with CH, there were 108 excess cases per 10 000 patients, compared with 25 excess cases per 10 000 patients among those without CH.
The 8 y-cumulative incidence of nonmyeloid SMNs among HCT survivors was 8.7% (95% CI = 7.2% to 10.5%), and there was a significant difference in the cumulative incidence among patients with CH compared with those without (15.1% vs 7.2%, P < .001; Figure 2, A). There was a progressive increase in the incidence of nonmyeloid SMNs by VAF: 7.2% (VAF < 2%), 14.0% (VAF 2% to ≤10%), 19.4% (VAF >10%); P = .001; Figure 2, B. There was no comparable trend by number of CH variants (data not shown). Gene-specific analyses of the 5 most frequently mutated CH-associated genes (DNMT3A, TET2, PPM1D, ASXL1, and TP53) revealed an overall higher incidence among those with TP53 mutations (VAF range = 2%-26.1%); Table S4.
Figure 2.
Eight-year cumulative incidence of nonmyeloid subsequent malignant neoplasm. According to A) the presence of CH and B) CH VAF categories: no CH, VAF≤10%, and VAF >10%. Abbreviations: CHIP = clonal hematopoiesis of indeterminate potential; HCT = hematopoietic cell transplantation; VAF = variant allele frequency.
In univariable analysis, CH was associated with a 2-fold increased risk of nonmyeloid SMN (sHR = 1.96; 95% CI = 1.33 to 2.88). Additional significant risk factors for developing nonmyeloid SMNs included older age (>58.8 years; sHR = 1.70; 95% CI = 1.17 to 2.46) and male sex (sHR = 1.55; 95% CI = 1.05 to 2.30); Table 2. Conversely, Black patients and those from other racial or ethnic groups had a lower risk compared with non-Hispanic whites (sHR = 0.50; 95% CI = 0.25 to 0.99). In multivariable analysis, CH remained significantly and independently associated with risk of nonmyeloid SMN after HCT (sHR = 1.72; 95% CI = 1.15 to 2.59); Table 2.
Table 2.
Univariable and multivariable analyses for risk of nonmyeloid subsequent malignant neoplasms.
| Variable | Univariable analysis |
Multivariable analysis |
||
|---|---|---|---|---|
| HR (95% confidence interval) | P | HR (95% confidence interval) | P | |
| CH | 1.96 (1.33 to 2.88) | <.001 | 1.72 (1.15 to 2.59) | .009 |
| Median age (58.8 yo) | ||||
| <58.8 yo | 1.00 (Referent) | – | ||
| ≥58.8 yo | 1.70 (1.17 to 2.46) | .005 | 1.41 (0.95 to 2.10) | .086 |
| Sex | ||||
| Female | 1.00 (Referent) | – | ||
| Male | 1.55 (1.05 to 2.30) | .028 | 1.50 (1.02 to 2.22) | .040 |
| Race and ethnicity | ||||
| Non-Hispanic White | 1.00 (Referent) | – | ||
| Asian | 0.54 (0.26 to 1.11) | .094 | 0.56 (0.27 to 1.17) | .12 |
| Hispanic | 0.65 (0.41 to 1.03) | .064 | 0.74 (0.46 to 1.18) | .21 |
| Black/Other | 0.50 (0.25 to 0.99) | .047 | 0.55 (0.28 to 1.09) | .088 |
| BMI (kg/m2) | 1.01 (0.98 to 1.04) | .45 | ||
| HCT-CI | ||||
| <3 | 1.00 (Referent) | – | ||
| ≥3 | 0.83 (0.58 to 1.20) | .32 | ||
| Diagnosis | ||||
| Plasma dyscrasia | 1.00 (Referent) | – | ||
| Non-Hodgkin lymphoma | 1.13 (0.76 to 1.66) | .69 | ||
| Hodgkin lymphoma | 1.13 (0.62 to 2.06) | 0.55 | ||
| Pre-HCT radiation | ||||
| No | 1.00 (Referent) | – | ||
| Yes | 0.78 (0.48 to 1.29) | 0.34 | ||
| Conditioning regimen | ||||
| Melphalan | 1.00 (Referent) | – | ||
| BEAM | 1.20 (0.80 to 1.78) | 0.38 | ||
| CBV | 0.95 (0.52 to 1.73) | 0.85 | ||
| Other | 1.18 (0.37 to 3.73) | 0.78 | ||
| Remission status at HCT | ||||
| CR | 1.00 (Referent) | – | ||
| Not in CR | 1.00 (0.79 to 1.25) | 0.98 | ||
| CD34 count | 1.02 (0.99 to 1.04) | 0.19 | ||
| Pre-HCT other malignancy | ||||
| No | 1.00 (Referent) | – | ||
| Yes | 1.44 (0.83 to 2.52) | 0.20 | ||
Abbreviations: BEAM = carmustine (BCNU), etoposide, aracytin and melphalan; BMI = body mass index (calculated from body weight and height); CBV = cyclophosphamide, BCNU (carmustine), and VP-16 (etoposide); CH = clonal hematopoiesis; CI = comorbidity index; HCT = hematopoietic cell transplantation; HR = hazard ratio; KPS = Karnofsky Performance Status; PBSC = peripheral blood stem cell.
Among the 117 patients who developed nonmyeloid SMNs, the most frequent cancer types were prostate (24.8%), followed by malignant skin (12.0%), and lung (9.4%). Clonal hematopoiesis was associated with an especially high risk of lung cancer (sHR = 4.77; 95% CI = 1.46 to 15.53) and this association remained significant in the age-adjusted analysis (sHR = 5.03; 95% CI = 1.70 to 14.94).
CH and survival after HCT
The overall survival rate for the cohort was 51.4% (95% CI = 47.9% to 54.8%) at 8 y after HCT. Patients with CH had significantly worse overall survival (41.0% vs 54.1%, P < .001) compared with those without CH; Figure 3, A. Patients with CH had a significantly greater cumulative incidence of NRM (14.3% vs 6.3%, P < .001), with a comparable cumulative incidence of relapse-related mortality (42.4% vs 37.1%, P = .58); Figure 3, B and C. Among the 85 patients who died of nonrelapse causes, the proportions of deaths attributable to nonhematologic or hematologic SMNs was higher in patients with CH, compared with those without CH; Table S5. In the multivariable model, CH was significantly and independently associated with an increased risk of all-cause mortality (HR = 1.24; 95% CI = 1.03 to 1.50), primarily driven by the much higher risk of NRM (sHR = 2.97; 95% CI = 1.90 to 4.64); Table S6.
Figure 3.
Survival outcomes. A) Kaplan–Meier survival curve following HCT. B, C) Cumulative incidence (%) of cause-specific mortality following HCT according to the relapse status: B) relapse-related mortality, C) nonrelapse mortality. Abbreviations: CHIP = clonal hematopoiesis of indeterminate potential; HCT = hematopoietic cell transplantation; VAF = variant allele frequency.
Discussion
In this large and demographically diverse cohort of patients undergoing autologous HCT, CH was significantly and independently associated with risk of developing nonmyeloid SMNs after HCT, with a direct relationship by clone size, as indicated by VAF. Compared with patients without CH, those with CH exhibited both a higher SIR and a greater absolute excess incidence of cancer, relative to the general population. Furthermore, patients with CH experienced significantly worse overall survival after HCT compared with those without CH, which was largely attributable to a higher risk of NRM. These findings underscore CH as a biologically plausible and clinically significant biomarker to characterize the risk of nonmyeloid SMNs in HCT survivors.
In the general population, there is a growing body of evidence for a significant association between CH and solid malignancies, with lung cancer being the most well documented. In a nested case–control study within the UK Biobank,21 CH was associated with a 1.4-fold risk of lung cancer (HR = 1.40; 95% CI = 1.27 to 1.54), independent of established risk factors such as smoking. Furthermore, a graded relationship was observed between cancer risk and clonal size. Similarly, a meta-analysis including 88 studies demonstrated that CH carriers face an elevated risk of cancer related mortality (HR = 1.46; 95% CI = 1.13 to 1.88) as well as lung cancer incidence (HR = 1.40; 95% CI = 1.27 to 1.54).36 Studies have also linked CH to other cancer types such as colorectal, prostate, and breast cancer.37 Our findings are aligned with these reports, further establishing the significant and independent association between CH and solid cancers.
Although a causal relationship between CH and solid cancers is yet to be established, emerging evidence and proposed mechanisms suggest a potential role of myeloid progenitors and clonal hematopoiesis in solid cancer tumorigenesis.22,38,39 One mechanism involves chronic systemic inflammation driven by myeloid progenitors, with elevated proinflammatory cytokines, such as interleukin-1α and β (IL-1α/β) and tumor necrosis factor-α, promoting a microenvironment that supports cancer cell proliferation, survival, and immune evasion.22,40 Notably, a study by Park et al. demonstrated that hematopoietic aging induces emergency myelopoiesis, leading to the accumulation of myeloid progenitor-like cells in lung tumors.41 The age-associated decline of DNMT3A in these cells exhibited enhanced IL-1α production, promoting lung cancer development and tumor progression. More recently, tumor-infiltrating clonal hematopoiesis, defined by the presence of CH mutations in tumors, has been linked to worse outcomes in non-small cell lung cancer, suggesting its potential role in tumor immune microenvironment and solid tumor progression.42 Mutations in CH-associated genes such as TET2 and TP53 have also been shown to impair macrophage polarization and T-cell activation, further compromising the immune system’s ability to detect and eliminate nascent tumor cells, thereby facilitating immune evasion and tumor progression.43-45 Finally, CH may potentiate the effects of carcinogenic exposures, accelerating tumorigenesis.46 Collectively, these findings highlight CH as a potential mediator of tumorigenesis through mechanisms such as promoting a pro-inflammatory microenvironment, disrupting normal immune responses, and compromising antitumor immunity. Further mechanistic studies are needed to elucidate the precise pathways underlying these effects.
It is important to contextualize the findings from the current study within the broader cancer survivorship literature. Studies during the past several decades have highlighted how cancer patients and survivors, including HCT recipients, face a markedly increased risk of developing subsequent cancers compared with the general population.47-49 A recent study of 196 848 adult cancer survivors found an elevated risk of subsequent cancers in both men (SIR = 1.50) and women (SIR = 1.58),49 representing relative risks that were similar to that seen in our overall cohort. These increased risks have been attributed to the mutagenic effects of cancer treatment exposures, as well as shared etiologic factors such as tobacco use and viral infections.50 However, the precise mechanisms driving the development and progression of subsequent cancer among cancer survivors remain poorly understood, as evidenced by lack of association between RT exposure and nonmyeloid SMNs in our cohort. Given our findings of a higher SIR in patients with CH compared with those without, CH may represent a critical missing link in cancer risk, serving as both a mechanistic link and a biologically plausible biomarker for predicting SMNs.
Our observation of worse overall survival among patients with CH compared with those without CH aligns well with prior studies demonstrating that CH is associated with poorer survival outcomes following HCT. This disparity has been largely attributed to a higher burden of NRM in patients with CH, as reported in earlier investigations.23,31,51 In our cohort, NRM accounted for 14.2% of all deaths (n = 85), representing a relatively low proportion that limited the statistical power for adjusted analyses of cause-specific risks. Nevertheless, the strong association between CH and NRM raises the possibility that CH-associated nonmyeloid SMNs may play a role in mediating this risk. These findings underscore the importance of monitoring CH as a potential risk factor for subsequent malignancies in HCT survivors and highlight the need for tailored cancer surveillance strategies to mitigate this risk. Larger studies are needed to further clarify these associations and to inform the development of targeted screening and therapeutic strategies aimed at reducing the long-term impact of CH in this high-risk population.
Our study findings should be interpreted considering several limitations. First, although our CH panel was restricted to leukemogenic variants, some mutations could represent circulating tumor DNA from the primary disease rather than true CH. However, most CH mutations identified in our study are not characteristic of the primary diseases being transplanted for (PCD and lymphoma), and there was no statistically significant association between remission status at HCT and CH risk. We also included 16 common cancer predisposition genes in our panel and, except for TP53, none were associated with SMN risk. With regard to TP53 CH (2.1% of those with a single CH variant), the VAF ranged from 2% to 26.1%, suggesting somatic rather than germline mutations. Second, although we were able to examine the association between pre-HCT RT exposure and risk of nonmyeloid SMNs, lack of granularity on pre-HCT chemotherapy exposures limited our ability to perform comparable analyses. It is noteworthy that carcinogenic chemotherapy exposures (eg, alkylators, topoisomerase inhibitors) are typically associated with myeloid SMNs which were not our outcomes of interest. Similarly, detailed smoking history, which could further influence solid cancer risk, particularly for lung cancer, was unavailable. Additional studies are needed to examine the interplay between shared lifestyle etiologic factors and CH on subsequent cancer risk in long-term survivors. Finally, we did not have follow-up samples at the time of nonmyeloid SMN diagnosis, limiting our ability to assess the role of clonal evolution in tumorigenesis.
In conclusion, we demonstrate a significant and independent association between CH and risk of nonmyeloid SMN after HCT. Importantly, patients with CH had significantly worse survival outcomes, primarily driven by the high risk of NRM. These findings highlight the potential of CH as a biomarker to guide personalized cancer screening and risk assessment strategies for HCT recipients, including consideration of tailored monitoring and risk mitigation approaches for individuals with high clonal burden. The growing population of increasingly older patients referred for HCT makes development of such strategies imperative, to ensure that they live long and healthy lives well after their HCT.
Supplementary Material
Acknowledgments
This study was presented in part at the 2024 American Hematology Society Annual Meeting as an Oral Abstract. The authors would like to acknowledge the work provided by the Leadership and Staff of the CoH Center for Informatics most notably Research Informatics, and the utilization of the POSEIDON data exploration, visualization, and analysis platform including the Honest Broker process.
Contributor Information
June-Wha Rhee, Department of Medicine, City of Hope Comprehensive Cancer Center, Duarte, CA, United States.
Sitong Chen, Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte, CA, United States.
Raju Pillai, Department of Pathology, City of Hope Comprehensive Cancer Center, Duarte, CA, United States.
Alysia Bosworth, Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte, CA, United States.
Artem Oganesyan, Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte, CA, United States.
Emma Grigorian, Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte, CA, United States.
Liezl Atencio, Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte, CA, United States.
Caitlyn Estrada, Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte, CA, United States.
Mareen Kassabian, Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte, CA, United States.
Lanie Lindenfeld, Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte, CA, United States.
Rusha Bhandari, Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte, CA, United States.
Scott Goldsmith, Department of Hematology & Hematopoietic Transplantation, City of Hope Comprehensive Cancer Center, Duarte, CA, United States.
Michael Rosenzweig, Department of Hematology & Hematopoietic Transplantation, City of Hope Comprehensive Cancer Center, Duarte, CA, United States.
Alex F Herrera, Department of Hematology & Hematopoietic Transplantation, City of Hope Comprehensive Cancer Center, Duarte, CA, United States.
Matthew G Mei, Department of Hematology & Hematopoietic Transplantation, City of Hope Comprehensive Cancer Center, Duarte, CA, United States.
Ryotaro Nakamura, Department of Hematology & Hematopoietic Transplantation, City of Hope Comprehensive Cancer Center, Duarte, CA, United States.
F Lennie Wong, Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte, CA, United States.
Stephen J Forman, Department of Hematology & Hematopoietic Transplantation, City of Hope Comprehensive Cancer Center, Duarte, CA, United States.
Saro H Armenian, Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte, CA, United States.
Author contributions
June-Wha Rhee (Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Writing—original draft, Writing—review & editing), Sitong Chen (Formal analysis, Investigation, Writing—original draft, Writing—review & editing), Raju Pillai (Data curation, Formal analysis, Investigation, Methodology, Writing—review & editing), Alysia Bosworth (Data curation, Formal analysis, Investigation, Writing—review & editing), Artem Oganesyan (Data curation, Investigation, Writing—original draft, Writing—review & editing), Emma Grigorian (Data curation, Investigation, Writing—review & editing), Liezl Atencio (Data curation, Investigation, Project administration, Writing—review & editing), Caitlyn Estrada (Data curation, Investigation, Writing—review & editing), Mareen Kassabian (Data curation, Investigation, Writing—review & editing), Lanie Lindenfeld (Investigation, Project administration, Supervision, Writing—review & editing), Rusha Bhandari (Formal analysis, Investigation, Writing—original draft, Writing—review & editing), Scott Goldsmith (Formal analysis, Investigation, Writing—review & editing), Michael Rosenzweig (Formal analysis, Investigation, Writing—review & editing), Alex F. Herrera (Formal analysis, Investigation, Writing—review & editing), Matthew G. Mei (Formal analysis, Investigation, Writing—review & editing), Ryotaro Nakamura (Formal analysis, Investigation, Writing—review & editing), F. Lennie Wong (Formal analysis, Investigation, Methodology, Supervision, Writing—review & editing), Stephen Forman (Conceptualization, Formal analysis, Investigation, Writing—review & editing), and Saro H. Armenian (Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing—original draft, Writing—review & editing)
Supplementary material
Supplementary material is available at JNCI: Journal of the National Cancer Institute online.
Funding
This study was supported by the V Foundation for Cancer Research (grant no. DT2019-006 to S.H.A.).
Conflicts of interest
J.-W.R. and S.H.A. report a research grant from Pfizer, unrelated to the present work. S.G. reports consultancy and research funding from BMS and consultancy from Johnson and Johnson. A.F.H. reports consultancy and research funding from AstraZeneca, ADC Therapeutics, Genentech, Bristol Myers Squibb, Seagen, and Merck; consultancy from Tubulis, Takeda, and Karyopharm; and research funding from Kite, a Gilead Company and Gilead Sciences. No disclosures were reported by the other authors.
Data availability
Data supporting the findings of this study, including genetic variant information, demographic data, and clinical outcomes, are available upon reasonable request. All shared data will be deidentified and codified to protect participant anonymity. Access will be granted under conditions that ensure participant confidentiality, with no direct identifiers included. Requests for data access and analysis will be reviewed and considered by the authors. To request data, please contact the corresponding author. If data cannot be shared, a justification will be provided.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data supporting the findings of this study, including genetic variant information, demographic data, and clinical outcomes, are available upon reasonable request. All shared data will be deidentified and codified to protect participant anonymity. Access will be granted under conditions that ensure participant confidentiality, with no direct identifiers included. Requests for data access and analysis will be reviewed and considered by the authors. To request data, please contact the corresponding author. If data cannot be shared, a justification will be provided.