- Email: [email protected]
Survival of Patients With Second Primary Hodgkin Lymphoma
Journal Pre-proof Survival of Patients with Second Primary Hodgkin Lymphoma Justin Budnik, MD, Christopher Doucette, MD, Michael T. Milano, MD, PhD, Louis S. Constine, MD PII:
S2152-2650(20)30002-1
DOI:
https://doi.org/10.1016/j.clml.2019.12.017
Reference:
CLML 1500
To appear in:
Clinical Lymphoma, Myeloma and Leukemia
Received Date: 4 September 2019 Revised Date:
27 December 2019
Accepted Date: 31 December 2019
Please cite this article as: Budnik J, Doucette C, Milano MT, Constine LS, Survival of Patients with Second Primary Hodgkin Lymphoma, Clinical Lymphoma, Myeloma and Leukemia (2020), doi: https:// doi.org/10.1016/j.clml.2019.12.017. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Elsevier Inc. All rights reserved.
1 Survival of Patients with Second Primary Hodgkin Lymphoma a
a
a
a
Justin Budnik MD* , Christopher Doucette MD , Michael T Milano MD, PhD , Louis S Constine MD *Corresponding author [email protected] (585)-275-5216 a
University of Rochester Medical Center Department of Radiation Oncology
601 Elmwood Avenue Rochester, NY 14642 (585)-275-2171 [email protected] [email protected] [email protected]
Abstract Introduction: The increased risk for second malignancies after Hodgkin lymphoma (HL) diagnosis is well-known. However, no study has investigated the outcomes of patients diagnosed with HL after an antecedent malignancy (HL-2). We aimed to investigate overall survival (OS), disease-specific survival (DSS) and correlates of survival in HL2, using the Surveillance, Epidemiology and End Results (SEER) database. Materials and Methods: HL-2 patients (n=821) identified from 2000-2014 SEER 18 registries were compared to first primary HL patients (HL-1, n=31,355) from the same registries. Multivariable, propensity score-matched (PSM), and competing risks regression (CRR) analyses were conducted to assess the effect of antecedent malignancy on survival. Results: Hematologic (n=309, 37.6%), prostate (n=169, 20.6%) and breast (n=76, 9.3%) malignancies were common antecedent malignancies in HL-2. Median latency between antecedent malignancy and HL diagnosis was 39 months. Median age at HL diagnosis for HL-1 and HL-2 were 36 and 66 years, respectively (p<0.001). The 5 year OS and HL-DSS rates for HL-2 vs HL-1 were 53.2% vs 82.7% and 79.1% vs 90.9%, respectively (p<0.001). On multivariable analysis, a history of antecedent malignancy was associated with decreased OS (HR 1.27, 95%CI 1.131.42, p<0.001). With PSM balancing across co-variables, a history of antecedent malignancy was associated with decrements in HL-DSS (HR 1.46, 95%CI 1.12-1.92, p=0.006), and OS (HR 2.09, 95%CI 1.74-2.51, p<0.001). Conclusions: The decrement in DSS in HL-2 relative to HL-1 may be related to biological differences in HL, age, and/or other unanalyzed factors. Further study of HL-2 patients is warranted.
2 Micro abstract: The outcomes of patients with a second primary Hodgkin lymphoma after an antecedent malignancy have never been described. We used cancer registry data to compare survival between 821 patients with second primary Hodgkin lymphoma and 31,355 patients with first primary Hodgkin lymphoma. We found that survival outcomes were worse in the second primary Hodgkin lymphoma group, a novel finding. Clinical practice points: • • • • • •
The characteristics and survival outcomes of patients with second primary Hodgkin lymphoma after an antecedent malignancy have never been described. We used a population-based approach to compare clinical and pathologic characteristics and survival outcomes between patients with second primary Hodgkin lymphoma and first primary Hodgkin lymphoma. We found a disproportionate number of antecedent hematologic malignancies in those with second primary Hodgkin lymphoma, compared to the general population incidence of hematologic malignancies. We show that the survival outcomes of those with second primary Hodgkin lymphoma are inferior to those with first primary Hodgkin lymphoma when accounting for shared variables (with multivariable regression and propensity score-matching models). These novel findings will inform clinicians of the prognosis of patients with second primary Hodgkin lymphoma. These results may also promote further study of second hematologic malignancies following antecedent cancers.
Keywords: Population-based; Hematologic malignancies; Antecedent malignancy; prognosis; SEER
Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or notfor-profit sectors. Conflicts of interest: None to disclose, all authors. 2,883 words (excluding abstract, references), 4 tables, 2 color figures, 1 supplemental table
3 Introduction: Hodgkin lymphoma (HL) is a relatively uncommon malignancy accounting for an estimated 8,110 new cases, and 1,000 deaths in the USA in 2019 [1]. The care of patients with HL has evolved over the past several decades such that it is now one of the most curable cancers—overall survival (OS) rates for Ann Arbor stage I and stage II HL exceed 90% at 5 years. For those diagnosed with stage III and stage IV HL, 5 year OS rates are in excess of 70% [2]. This is in stark contrast to other more common cancers such as non-Hodgkin lymphoma (NHL), breast, and lung cancers, where there is a more pronounced survival decrement associated with increasing stage [1].
From the study of long-term HL survivors an understanding of the increased risks of second malignancies and late morbidity after HL treatment has been cultivated and extensively described [3-7]. No study however, has investigated the incidence and survival implications of second primary HL after an antecedent malignancy; thus HL disease-specific survival (HL-DSS) and OS in this population are unknown, as are the clinical correlates of survival in this population. We aimed to investigate this using the Surveillance, Epidemiology and End Results (SEER) database.
Materials and Methods: Using the SEER-18 registries we identified patients diagnosed with HL after an antecedent malignancy between the years 2000-2014 (HL-2). For a comparison cohort we identified patients with first primary HL from the same registries (HL-1). Only patients with complete survival, and cause of death information were included. Cause of death information was taken from the “SEER cause-specific death classification” field. OS time was measured from date of HL diagnosis to death or last follow up. HL-DSS time was measured from date of HL diagnosis to death from HL or last follow up. Variables of interest were selected a priori and used consistently throughout all survival analysis models. Stage information for HL in SEER is given by the Ann Arbor staging system [8]. This system was used consistently throughout the study years with respect to anatomic extent of HL. The 2014 Lugano classification modifications to the Ann Arbor system could not be reconstituted from the information available in SEER [9]. HL pathologic subtype groupings were based on International Classification of Diseases for Oncology third edition (ICD-O-3) morphology codes: ‘HL not otherwise specified (NOS)’ (9650/3), lymphocyte rich HL (9651/3), mixed cellularity HL (9652/3), lymphocyte depleted (9653/3), nodular sclerosing HL (9663/3, 9664/3, 9665/3, 9667/3) and nodular lymphocyte-predominant HL (9659/3). Antecedent malignancies in the HL-2 group were ascertained by ICD-O-3 site codes for solid tumor histologies, and by a combination of site and morphology
4 codes for the hematologic malignancies (in order to avoid classifying a lymphoma or leukemia confined to a particular anatomic site as a solid primary tumor of that anatomic site). Information on treatment for HL included: receipt of HL-directed radiotherapy (RT) or no or unknown receipt of radiation; and receipt of chemotherapy (CT), or no or unknown receipt of chemotherapy. No and unknown receipt of therapy are grouped together in the SEER database due to underreporting of treatment (particularly chemotherapy) in the SEER registries [10]. Patients who received radiotherapy and chemotherapy for HL were coded as having received “combination therapy”. Patients coded as no/unknown receipt of radiotherapy and chemotherapy were labelled as “no treatment recorded”. HL patients receiving HL-directed surgical procedures in excess of biopsy were excluded from analyses. The ‘Collaborative Stage (CS) Site-Specific Factor 2’ field (http://web2.facs.org/cstage0205/lymphoma/Lymphoma_kpg.html) codes for the presence or absence of B symptoms (fever, night sweats, and weight loss) at diagnosis; this information was complete for the majority of patients, and was reconstituted as ‘present’, ‘absent’ or ‘unknown’ for the purposes of analysis. Minimum latency between antecedent malignancy and HL-2 diagnosis was set at 2 months in order to exclude patients with synchronous primary cancers. This is the methodology recommended for use in the SEER program [11].
Categorical patient and tumor characteristics were summarized as counts and frequencies and compared using chi square tests. Continuous variables were compared using t tests. OS and HL-DSS were compared between HL-1 and HL-2 using Kaplan-Meier (KM) curves and log rank tests. Multivariable Cox regression analysis was used to assess the impact of patient and tumor characteristics on OS and HL-DSS. Competing risks regression (CRR) and cumulative incidence plots were used to assess the hazard of HL mortality (HLM) while accounting for other cause mortality (OCM). Propensity score matching (PSM) is a technique that has been used extensively in retrospective analyses to reduce biases by balancing patients across covariables that could influence their subsequent inclusion in a group of interest [12]. In this case, PSM was used to confirm the significance of the average effect of antecedent malignancy on OS and HL-DSS as estimated with Cox regression. We used PSM with a one-to-one nearest neighbor matching paradigm without replacement and a fixed caliper width of 0.05 times the logit of the propensity score for HL group [13]. This particular model has been used in other contemporary registry based
5 studies [14-15]. Univariable Cox regression was then used to assess the survival impact of antecedent malignancy on the PSM cohort [12].
Results:
Patient and tumor characteristics:
A total of 821, and 31,355 patients were included in HL-2 and HL-1, respectively. Median follow up after HL diagnosis was 64 months, and 77 months among those alive at last follow-up. Median latency between antecedent malignancy diagnosis and HL-2 diagnosis was 39 months (range 2 to 174 months). Table 1 summarizes patient and tumor characteristics at the time of HL diagnosis. The age distributions of HL-1 and HL-2 differed significantly, with median age at HL diagnosis of 36 and 66 years, respectively (t-test p<0.001). The proportion of males was significantly higher in HL-2 (n=540, 65.8%), than in HL-1 (n=17,146, 54.7%). Nodular sclerosing HL was more frequently diagnosed in HL-1 (n=18,192, 58.1%) than HL-2 (n=267, 32.5%). Ann Arbor stage I-II disease was more common in HL-1 (n=17,825, 56.8%) than HL-2 (n=397, 48.4%). A higher proportion of patients in HL-1 received combination therapy (n=9,312, 29.7%) than HL-2 (n=117, 14.3%). The proportion of patients with B symptoms present at diagnosis were similar between the groups (n=8,996, 28.7% of HL-1 versus n=231, 28.1% of HL-2).
Antecedent malignancies in HL-2:
Figure 1 depicts the frequencies of antecedent malignancies encountered in HL-2 grouped by body site. Hematologic malignancies (n=309, 37.6%) were the most common antecedent malignancies overall, and of these most were non-Hodgkin lymphomas (n=224), or leukemias (n=69). The most common antecedent solid tumors were prostate cancer (n=169, 20.6%), and breast cancer (n=76, 9.3%).
Impact of patient and tumor characteristics on OS:
Multivariable Cox regression was performed to account for the effects of age at and year of diagnosis, sex, race, HL histology, Ann Arbor stage, HL therapy, B symptoms, and HL group on OS (table 2). On multivariable analysis, older age (hazard ratio [HR] 1.05 per year; 95% confidence interval [95% CI], 1.05-1.06; p<0.001), black vs. white race (HR 1.49, 95% CI 1.38-1.61, p<0.001), lymphocyte depleted vs. nodular sclerosing HL histology (HR 1.51, 95% CI
6 1.27-1.80, p<0.001), increasing Ann Arbor stage, and presence of B symptoms (HR 1.46, 95%CI 1.36-1.57, p<0.001), were all associated with an OS decrement. Female sex (HR 0.81, 95% CI 0.77-0.85, p<0.001), and nodular lymphocyte predominant vs. nodular sclerosing HL histology (HR 0.51, 95% CI 0.44-0.61, p<0.001) were associated with improved OS. For HL therapy, chemotherapy without radiotherapy (HR 0.76, 95% CI 0.71-0.82, p<0.001) and radiotherapy without chemotherapy (HR 0.70, 95% CI 0.61-0.80, p<0.001) were associated with improved OS compared to patients in the “no treatment recorded” group. Combination therapy (HR 0.45, 95% CI 0.46, 95% CI, p<0.001) was also associated with improved OS outcomes compared to “no treatment recorded”.
Impact of antecedent malignancy on OS and HL-DSS:
Deaths from HL were less frequent in HL-1 (n=2,609, 8.3% of HL-1 patients) compared to HL-2 (n=143, 17.4% of HL2 patients). Table 3 depicts the median OS survival estimates as well as the actuarial 12 month and 60 month OS rates (%) by HL group and Ann Arbor stage. The aggregate 60 month OS and HL-DSS rates for HL-2 vs HL-1 were 53.2% vs 82.7% and 79.1% vs 90.9%, respectively (log rank test p<0.001). Figure 2 depicts the KM OS estimates (panel A), and HL-DSS estimates (panel B) for the HL-1 and HL-2 groups. On multivariable Cox regression (table 2) a history of antecedent malignancy was associated with a significant OS decrement (HR 1.28, 95% CI 1.14-1.43, p<0.001). This was further investigated with PSM analysis in which 795 (97%) HL-2 patients were matched with an equal number of HL-1 controls balanced across covariables including age at and year of diagnosis, sex, race, HL histology, Ann Arbor stage, HL therapy received, and B symptoms. The PSM HL-1 and HL-2 cohorts were wellbalanced overall (supplemental table 1). Within the PSM cohorts a history of antecedent malignancy remained associated with significant OS (HR 2.09, 95% CI 1.74-2.51, p<0.001) and HL-DSS (HR 1.46, 95% CI 1.12-1.92, p=0.006) decrements (table 4).
Impact of antecedent malignancy on HLM:
Figure 2, panel C depicts the cumulative incidence plots for HLM by HL group. These are based on a CRR model for HLM accounting for OCM. Covariables in the model included age at and year of diagnosis, sex, race, HL histology, Ann Arbor stage, HL therapy, and B symptoms. The cumulative incidence of HLM was low in both groups. The sub-
7 distribution hazard ratio (SHR) for HLM was non-significantly inferior among HL-2 patients (SHR 1.19, 95% CI 0.991.44, p=0.06).
Discussion:
This is the first population-based study of survival outcomes in patients who develop second primary HL after an antecedent malignancy. Notable findings include more adverse prognostic factors in the HL=2 group (including more advanced stage and aggressive histologies), less aggressive treatment for HL, and worse disease-specific survival outcomes even when accounting for the differences in patient- cancer- and treatment-related factors. The methodology we have employed mirrors that used in other contemporary population-based analyses of second malignancies [15-19]. Of note, rather than relying solely on multivariable regression and censored time-to-event methodologies to ascertain the impact of antecedent malignancy on survival outcomes, we have utilized PSM and CRR, to minimize bias, and to account for competing mortality risks, respectively [12,20].
The presented data agree with the body of literature accumulated from retrospective studies and prospective trials in HL with respect to risk factors associated with increased mortality from HL [21-30]. Particularly, we redemonstrate that increasing age, male sex, African American race, increasing Ann Arbor stage, lymphocytedepleted HL histology, and presence of B symptoms are all associated with significant survival decrements (table 1). Conversely, nodular lymphocyte predominant HL, which is increasingly recognized as a singular entity with an indolent course, was associated with improved survival outcomes in our study. This is consistent with contemporary literature [31-32].
With respect to HL treatment, combination therapy was associated with improved survival outcomes on multivariable analysis, but was less frequently utilized in patients in HL-2 versus HL-1. The decreased utilization may indirectly point to a difference in ability to tolerate more aggressive HL therapy between the groups. This could be due to lower performance status in HL-2 associated with significantly increased median age, and/or from sequelae of prior cancer treatment. Treatment for an antecedent malignancy can also impose limits on subsequent therapeutic options for HL, such as in the case of cumulative dose tolerances of organs at risk in successive courses of radiotherapy, or lifetime anthracycline doses in successive courses of chemotherapy.
8 The differential patterns of combination therapy utilization may also result from the increased proportion of Ann Arbor stage I-II HL in HL-1 compared to HL-2. Early stage (Ann Arbor stage I or II) HL has established indications for the incorporation of radiotherapy in contemporary treatment paradigms, advanced stage HL does not [33-34]. Regardless of these suppositions, other retrospective literature suggests that decreased utilization of combined modality therapy for HL with advancing age is real [35].
The most common antecedent malignancies encountered in HL-2 were other hematologic malignancies (figure 1). The observed incidence of antecedent hematologic malignancies in HL-2 is out of proportion to the incidence of hematologic malignancies in the general population [1]. The reason for this increased relative incidence is not inferable in the present study, however, the risk of treatment-related second hematologic malignancy following antecedent hematologic malignancy has been established previously, and may be a partial cause [36-37]. The next most common antecedent malignancies encountered in HL-2 were other common solid tumors; prostate cancer and breast cancer, which are the most common cancers in males and females, respectively [1]. The predominance of prostate cancer (n=169, 31% of males in HL-2) and breast cancer (n=76, 27% of females in HL-2) were roughly equal in proportion to the number of male and female patients in HL-2, and similar to the estimated incidence rate of prostate and breast cancer in the general male and female populations, respectively [1]. This weighs against any particular causal association between antecedent prostate cancer or breast cancer and development of subsequent HL.
The overall results of the survival analyses performed in this study show that a history of antecedent malignancy is associated with both OS and HL-DSS decrements (figure 2, tables 3-4). This is consistent with the observation of survival decrements imposed by a history of antecedent malignancy in other population-based studies of second solid tumors [15]. There are exceptions from this trend in the second malignancies literature. Two of note are that of first primary HL followed by second primary breast cancer and that of first primary HL followed by thyroid cancer [18-19]. In the first case, a history of antecedent HL was shown to confer a minimal breast cancer causespecific survival decrement in those who subsequently developed localized stage breast cancer, and no significant cause-specific survival decrement in those who subsequently developed regional or distant stage breast cancer
9 [18]. In the second case, a history of first antecedent HL was associated with no significant survival decrement in patients who subsequently developed a second primary thyroid cancer [19].
As mentioned previously, the HL survivor population has been studied extensively with respect to late cancer treatment effects and second malignancy risks, and as a result screening guidelines for HL survivors have been established [38]. In the present study, owing to the heterogeneous mix of antecedent malignancies encountered in HL-2, there would not necessarily have been any heightened HL surveillance in survivors of solid tumors, which carry heterogeneous guidelines for follow up care.
On CRR we demonstrated a trend toward increased HLM hazard in HL-2 compared to HL-1 without meeting the threshold for statistical significance (figure 2). The results of the CRR analysis however, are in general agreement with the OS and HL-DSS decrements ascertained on multivariable and PSM analysis with respect to the magnitude of the HLM effect (figure 2, table 4). Overall, the CRR model used in this scenario may be limited by a low overall incidence of HLM in both HL-1 and HL-2, a significant surplus of OCM in HL-2 compared to HL-1, which was tied most strongly to the increased age in the cohort, and to missing co-variables which could have significant effect on the model.
Limitations:
Our study is subject to the inherent limitations of retrospective registry-based data. Unlike a prospective study, the observational nature of the registry data allow for significant biases related to imbalance of demographic and clinical factors amongst groups of interest, and missing covariables which could affect the inferred effects of other covariables on survival outcomes. Notable in the case of the present study are the absence of patient performance status, HL specific risk factors including prognostic biomarkers (e.g. erythrocyte sedimentation rate) and extent of disease at diagnosis (including the particular anatomic sites involved, presence or absence of ‘bulk’ disease, and number of nodal versus extra-nodal sites involved). Also absent are particular details of chemotherapy and radiotherapy treatment such as identity and doses of chemotherapy agents used, and number of chemotherapy cycles given, as well as radiotherapy dose/fractionation schedules and radiotherapy treatment volume.
10 SEER employs strict criteria for the establishment of multiple primary cancers (http://seer.cancer.gov/tools/mphrules/). Despite this stringency the misclassification of one HL subtype for another, or in the case or nodular lymphocyte predominant HL particularly, misclassification as NHL (given similar histopathologic features between nodular lymphocyte predominant HL and B cell NHL), are not precluded. However, we think the overall impact of any histopathologic misclassification in this study is likely small, given that the study years are well within the era of integrated morphological and immunohistochemical characterization of lymphomas.
Additionally, the years of our study straddle the widespread adoption of positron-emission computed tomography (PET-CT) in both HL staging and in assessing response to therapy [9, 3940]. No information about PET-CT utilization in staging, treatment decision making, or in disease response assessment are available in SEER.
Conclusions: HL-2 is rare compared to HL-1. The most common malignancies preceding HL-2 diagnosis were other hematologic malignancies and common solid tumors. On multivariable and PSM analysis a history of antecedent malignancy appears to adversely affect OS and DSS in those who subsequently develop HL. The HL-DSS decrement was a surprising finding, which may be related to biological differences in HL in this population, age, and/or the effect of missing co-variables known to influence survival such as performance status and specifics of treatment. Further study of this interesting sub-group of HL patients is warranted.
11 References:
1) Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA: A Cancer Journal for Clinicians. 2019;69:7-34. 2) Ries LA, Young JL, Keel GE, Eisner MP, Lin YD, Horner MJ, eds. SEER Survival Monograph: Cancer Survival Among Adults: U.S. SEER Program, 1988-2001, Patient and Tumor Characteristics. NIH Pub. No. 076215. Bethesda, MD: National Cancer Institute, SEER Program; 2007. 3) Mauch PM, Kalish LA, Marcus KC, et al. Long-Term Survival in Hodgkin's Disease. Cancer J Sci Am. 1995;1(1):33-42. 4) Aisenberg AC: Problems in Hodgkin's disease management. Blood. 1999;93(3):761-779. 5) Longo DL, Armitage JO. Controversies in the treatment of early-stage Hodgkin's lymphoma. N Engl J Med. 2015;372(17):1667-1669. 6) Aleman BM, van den Belt-Dusebout AW, Klokman WJ, et al. Long-term cause-specific mortality of patients treated for Hodgkin's disease. J Clin Oncol. 2003;21(18):3431-3439. 7) Sud A, Thomsen H, Sundquist K, et al. Risk of Second Cancer in Hodgkin Lymphoma Survivors and Influence of Family History. J Clin Oncol. 2017;35(14):1584-1590. 8) Carbone PP, Kaplan HS, Musshoff K, et al. Report of the committee on Hodgkin's disease staging classification. Cancer Res. 1971;31:1860–1861. 9) Cheson BD, Fisher RI, Barrington SF, et al. Recommendations for initial evaluation, staging, and response assessment of Hodgkin and non-Hodgkin lymphoma: the Lugano classification. J Clin Oncol. 2014;32:30593068. 10) Noone AM, Lund JL, Mariotto A, et al. Comparison of SEER Treatment Data With Medicare Claims. Med Care. 2016;54(9):e55–e64. 11) Curtis RE, Freedman DM, Ron E, et al. Chapter 2: methods. In: Curtis RE, Freedman DM, Ron E, et al., eds. New Malignancies Among Cancer Survivors: SEER Cancer Registries, 1973–2000 National Cancer Institute, NIH Publ No 05-5302. Bethesda, MD; 2006. 12) Austin PC. The use of propensity score methods with survival or time-to-event outcomes: reporting measures of effect similar to those used in randomized experiments. Stat Med. 2014;33(7):1242–58.
12 13) Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat. 2011;10:150–161. 14) Rusthoven CG, Lanning RM, Jones BL, et al. Metastatic nasopharyngeal carcinoma: Patterns of care and survival for patients receiving chemotherapy with and without local radiotherapy. Radiother Oncol. 2017;124(1):139-146. 15) Budnik J, DeNunzio NJ, Singh DP, Milano MT. Second primary non-small-cell lung cancer after head and neck cancer: A population-based study of clinical and pathologic characteristics and survival outcomes in 3597 patients. Clin Lung Cancer. 2019;Mar 4:pii: S1525-7304(19)30067-1. doi: 10.1016/j.cllc.2019.02.017. 16) Donin N, Filson C, Drakaki A, et al. Risk of second primary malignancies among cancer survivors in the United States, 1992 through 2008. Cancer. 2016;122(19):3075–3086. 17) Han SS, Rivera GA, Tammemägi MC, et al. Risk Stratification for Second Primary Lung Cancer. J Clin Oncol. 2017;35(25):2893–2899. 18) Milano MT, Li H, Gail MH, Constine LS, Travis LB. Long-term survival among patients with Hodgkin's lymphoma who developed breast cancer: a population-based study. J Clin Oncol. 2010;28(34):5088–5096. 19) Chowdhry AK, Fung C, Chowdhry VK, et al. A population-based study of prognosis and survival in patients with second primary thyroid cancer after Hodgkin lymphoma. Leuk Lymphoma. 2018;59(5):1180-1187. 20) Dignam JJ, Zhang Q, Kocherginsky M. The use and interpretation of competing risks regression models. Clin Cancer Res. 2012;18(8):2301–2308. 21) Cosset JM, Henry-Amar M, Meerwaldt JH, et al. The EORTC trials for limited stage Hodgkin's disease. The EORTC Lymphoma Cooperative Group. Eur J Cancer. 1992;28A(11):1847-1850. 22) Evens AM, Helenowski I, Ramsdale E, et al. A retrospective multicenter analysis of elderly Hodgkin lymphoma: outcomes and prognostic factors in the modern era. Blood. 2012;119(3):692-695. 23) Klimm B, Franklin J, Stein H, et al. Lymphocyte-depleted classical Hodgkin's lymphoma: a comprehensive analysis from the German Hodgkin study group. J Clin Oncol. 2011;29(29):3914-3920. 24) Evens AM, Antillón M, Aschebrook-Kilfoy B, Chiu BCH. Racial disparities in Hodgkin's lymphoma: a comprehensive population-based analysis. Ann Oncol. 2012;23(8):2128–2137.
13 25) Jost LM, Stahel RA. ESMO Guidelines Task Force: ESMO Minimum Clinical Recommendations for diagnosis, treatment and follow-up of Hodgkin's disease. Ann Oncol. 2005;16(Suppl 1):54-55. 26) Hasenclever D, Diehl V. A prognostic score for advanced Hodgkin's disease. International Prognostic Factors Project on Advanced Hodgkin's Disease. N Engl J Med. 1998;339(21):1506-1514. 27) Moccia AA, Donaldson J, Chhanabhai M, et al. International Prognostic Score in advanced-stage Hodgkin's lymphoma: altered utility in the modern era. J Clin Oncol. 2012;30(27):3383-3388. 28) Sasse S, Bröckelmann PJ, Goergen H, et al. Long-Term Follow-Up of Contemporary Treatment in EarlyStage Hodgkin Lymphoma: Updated Analyses of the German Hodgkin Study Group HD7, HD8, HD10, and HD11 Trials. J Clin Oncol. 2017;35(18):1999-2007. 29) Radford J, Illidge T, Counsell N, et al. Results of a trial of PET-directed therapy for early-stage Hodgkin's lymphoma. N Engl J Med. 2015;372(17):1598-1607. 30) Raemaekers JM, André MP, Federico M, et al. Omitting radiotherapy in early positron emission tomography-negative stage I/II Hodgkin lymphoma is associated with an increased risk of early relapse: Clinical results of the preplanned interim analysis of the randomized EORTC/LYSA/FIL H10 trial. J Clin Oncol. 2014;32(12):1188-1194. 31) Bodis S, Kraus MD, Pinkus G, et al. Clinical presentation and outcome in lymphocyte-predominant Hodgkin's disease. J Clin Oncol. 1997;15(9):3060-3066, 1997. 32) Orlandi E, Lazzarino M, Brusamolino E, et al. Nodular lymphocyte predominance Hodgkin's disease: longterm observation reveals a continuous pattern of recurrence. Leuk Lymphoma. 1997;26(3-4):359-368. 33) Bröckelmann PJ, Sasse S, Engert A. Balancing risk and benefit in early-stage classical Hodgkin lymphoma. Blood. 2018;131(15):1666-1678. 34) Loeffler M, Brosteanu O, Hasenclever D, et al. Meta-analysis of chemotherapy versus combined modality treatment trials in Hodgkin's disease. International Database on Hodgkin's Disease Overview Study Group. J Clin Oncol. 1998;16(3):818-829. 35) Olszewski AJ, Shrestha R, Castillo JJ. Treatment selection and outcomes in early-stage classical Hodgkin lymphoma: analysis of the National Cancer Data Base. J Clin Oncol. 2015;33(6):625-633.
14 36) Armitage JO, Carbone PP, Connors JM, et al. Treatment-related myelodysplasia and acute leukemia in non-Hodgkin's lymphoma patients. J Clin Oncol. 2003;21:897-906. 37) Koontz MZ, Horning SJ, Balise R, et al. Risk of therapy-related secondary leukemia in Hodgkin lymphoma: the Stanford University experience over three generations of clinical trials. J Clin Oncol. 2013;31:592-598. 38) Ng AK. Current survivorship recommendations for patients with Hodgkin lymphoma: focus on late effects. Blood. 2014;124(23):3373-3379. 39) El-Galaly TC, Hutchings M, Mylam KJ, et al. Impact of 18F-fluorodeoxyglucose positron emission tomography/computed tomography staging in newly diagnosed classical Hodgkin lymphoma: fewer cases with stage I disease and more with skeletal involvement. Leuk Lymphoma. 2014;55(10):2349-2355. 40) Cheson BD, Pfistner B, Juweid ME, et al. International Harmonization Project on Lymphoma. Revised response criteria for malignant lymphoma. J Clin Oncol. 2007;25(5):579-586.
15 Table 1. Patient and tumor characteristics by HL group. Variable
HL-1 n=31,355 n (column %)
HL-2 n=821 n (column %)
Total n=32,176 n (column %)
p value^
Age <0.001 ≤39 17,568 (56.1) 66 (8.0) 17,634 (54.8) 40-49 4,576 (14.6) 68 (8.3) 4,644 (14.4) 50-59 3,463 (11.1) 115 (14.0) 3,578 (11.1) 60-69 2,616 (8.4) 243 (29.6) 2,859 (8.9) 70-79 1,972 (6.3) 209 (25.5) 2,181 (6.7) ≥80 1,140 (3.6) 120 (14.6) 1,260 (3.9) Sex <0.001 Male 17,146 (54.7) 540 (65.8) 17,686 (55.0) Female 14,189 (45.3) 281 (34.2) 14,470 (45.0) Race <0.001 White 25,548 (81.5) 705 (85.9) 26,253 (81.6) Black 3,797 (12.1) 83 (10.1) 3,880 (12.1) Other* 1,990 (6.4) 33 (4.0) 2,023 (6.3) HL histology <0.001 Classic HL NOS 6,588 (21.0) 327 (39.8) 6,915 (21.5) Nodular sclerosing 18,192 (58.1) 267 (32.5) 18,459 (57.4) Lymphocyte rich 949 (3.0) 38 (4.6) 987 (3.1) Mixed cellularity 3,693 (11.8) 117 (14.3) 3,810 (11.9) Lymphocyte depleted 351 (1.1) 15 (1.8) 366 (1.1) Nodular lymphocyte predominant 1,562 (5.0) 57 (6.9) 1,619 (5.0) Ann Arbor stage <0.001 I 5,351 (17.0) 182 (22.2) 5,533 (17.0) II 12,474 (39.8) 215 (26.2) 12,689 (39.5) III 6,261 (20.0) 178 (21.7) 6,439 (20.0) IV 5,678 (18.1) 188 (22.9) 5,866 (18.2) Unknown 1,571 (5.0) 58 (7.1) 1,629 (5.1) B symptoms <0.001 Absent 11,020 (35.2) 360 (43.9) 11,380 (35.4) Present 8,996 (28.7) 231 (28.1) 9,227 (28.7) Unknown 11,319 (36.1) 230 (28.0) 11,549 (35.9) Radiotherapy <0.001 No/Unknown 20,869 (66.6) 654 (79.7) 21,523 (66.9) Yes 10,466 (33.4) 167 (20.3) 10,633 (33.1) Chemotherapy <0.001 No/Unknown 5,619 (17.9) 245 (29.8) 5,864 (18.2) Yes 25,716 (82.1) 576 (70.2) 26,292 (81.8) Therapy combination <0.001 No treatment recorded 4,478 (14.3) 197 (24) 4,675 (14.5) Radiotherapy 1,141 (3.6) 48 (5.9) 1,189 (3.7) Chemotherapy 16,404 (52.4) 459 (55.9) 16,863 (52.4) Radiotherapy + chemotherapy 9,312 (29.7) 117 (14.3) 9,429 (29.3) Legend. Hodgkin lymphoma (HL). *Other includes Asian, Alaska native/Pacific Islander, and unknown race. ^p values are from Pearson’s chi square test.
16 Table 2. Multivariable regression analysis^. Variable HR 95% CI p value HL group HL-1 HL-2 1.28 1.14-1.43 <0.001 Age (per year increase) 1.05 1.05-1.06 <0.001 Sex Male Female 0.81 0.77-0.85 <0.001 Race White Black 1.49 1.38-1.61 <0.001 Other* 1.19 1.05-1.35 0.009 Year of diagnosis (per year increase) 0.97 0.96-0.97 <0.001 HL Histology Classic HL NOS 1.20 1.12-1.27 <0.001 Nodular sclerosing Lymphocyte rich 0.77 0.66-0.90 0.001 Mixed cellularity 1.03 0.96-1.11 0.438 Lymphocyte depleted 1.51 1.27-1.80 <0.001 Nodular lymphocyte predominant 0.51 0.44-0.61 <0.001 Ann Arbor stage I II 1.19 1.09-1.29 <0.001 III 1.61 1.47-1.75 <0.001 IV 2.06 1.89-2.25 <0.001 Unknown 1.21 1.07-1.37 0.002 B symptoms Absent Present 1.46 1.36-1.58 <0.001 Unknown 1.19 1.10-1.29 <0.001 Therapy combination No treatment recorded Radiotherapy 0.70 0.61-0.80 <0.001 Chemotherapy 0.76 0.71-0.82 <0.001 Radiotherapy + chemotherapy 0.46 0.41-0.50 <0.001 Legend. Hodgkin lymphoma (HL). Hazard ratio (HR). 95% confidence interval (95% CI). *Other includes Asian, Alaska native/Pacific Islander, and unknown race. ^HR, 95% CI, and p values are from multivariable Cox regression accounting for age at and year of diagnosis, sex, race, histology, Ann Arbor stage, B symptoms, and therapy combination.
17 Table 3. OS outcomes by HL group and Ann Arbor stage.
Ann Arbor stage
I
II
III
IV
Unknown
HL-1
HL-2
Median OS (months)
Median OS (months)
12 month OS%
12 month OS%
60 month OS%
60 month OS%
NR
100
94.1
80.8
86.9
60.9
NR
86
96.4
77.0
89.9
63.8
NR
67
88.2
71.2
77.3
49.9
NR
38
81.9
62.7
69.2
39.1
NR
51
88.7
71.1
79.5
45.9
p value^
<0.001
<0.001
<0.001
<0.001
0.002
Legend. Overall survival (OS). Hodgkin lymphoma (HL). Not reached (NR). ^p values are from log rank test comparing HL-1 to HL-2.
18 Table 4. Propensity score-matched survival analysis: Impact of antecedent malignancy on OS and HL-DSS. Survival outcome
HR
95% CI
p value^
OS
2.09
1.74-2.51
<0.001
HL-DSS
1.46
1.12-1.92
0.006
Legend. Propensity score-matched (PSM). Overall survival (OS). Hodgkin lymphoma disease-specific survival (HLDSS). Hazard ratio (HR). 95% confidence interval (95% CI). ^HR, 95% CI, and p values are from univariable Cox regression accounting for impact of antecedent malignancy history on survival outcomes in the PSM sample of 795 patients from the HL-2 group matched to 795 HL-1 patients, balanced across covariables including age at and year of diagnosis, sex, race, histology, Ann Arbor stage, B symptoms, and therapy combination. .
19 Supplemental table 1. Propensity score-matched patient characteristics. Variable
Age ≤39 40-49 50-59 60-69 70-79 ≥80 Sex Male Female Race White Black Other* Year of diagnosis 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 HL histology Classic HL NOS Nodular sclerosing Lymphocyte rich Mixed cellularity Lymphocyte depleted Nodular lymphocyte predominant Ann Arbor stage I II III IV Unknown B symptoms Absent Present Unknown
HL-1 n=795 n
HL-2 n=795 n
Total n=1,590 n
67 67 110 238 201 112
63 67 110 236 205 114
130 134 220 474 406 226
527 268
527 268
1,054 536
718 73 4
705 83 7
1,423 156 11
5 15 25 20 37 43 60 69 59 64 81 81 69 76 91
7 13 23 20 29 38 55 77 79 72 78 66 81 77 80
12 28 48 40 66 81 115 146 138 136 159 147 150 153 171
343 252 32 104 11 53
311 262 36 116 15 55
654 514 68 220 26 108
171 204 173 189 58
170 210 173 185 57
341 414 346 374 115
349 233 213
345 223 227
694 456 440
p value^
0.99
1.00
0.65
0.82
0.65
0.99
0.69
20 Therapy combination 0.32 No treatment recorded 182 190 372 Radiotherapy 32 46 78 Chemotherapy 475 449 924 Radiotherapy + chemotherapy 106 110 216 Legend. Hodgkin lymphoma (HL). *Other includes Asian, Alaska native/Pacific Islander, and unknown race. ^p values are from Pearson’s chi square test.
Figure legends: Figure 1. Antecedent malignancies in HL-2. Legend. Hodgkin lymphoma (HL). Central nervous system (CNS). Genitourinary (GU). Gynecological (GYN). Head and neck (HN). Gastrointestinal (GI). Skin does not include cutaneous basal cell or squamous cell carcinomas which are not recorded in SEER. *Hematologic malignancies includes non-Hodgkin lymphomas (n=224), leukemias (n=69), and other miscellaneous hematologic cancers (n=16), categorized per the International Classification of Diseases for Oncology third edition (ICD-O-3) morphological coding schema.
Figure 2. Kaplan-Meier survival estimates and competing risks regression analysis by HL group. Legend. Hodgkin lymphoma (HL). Overall survival (OS). Hodgkin lymphoma disease-specific survival (HL-DSS). Hodgkin lymphoma mortality (HLM). Panel A; OS estimates. Panel B; HL-DSS estimates. Panel C; Cumulative incidence smoothed plot of HLM based on a competing risks regression model for HLM accounting for other-cause mortality and including age at an year of diagnosis, sex, race, HL histology, Ann Arbor stage, B symptoms and HL therapy as covariables.
n=821
CNS (n=5, 0.6%)
GYN (n=21, 2.6%)
Lung (n=27, 3.3%)
HN (n=43, 5.2%)
Skin (n=55, 6.7%)
GI (n=56, 6.8%)
Non-prostate GU (n=60, 7.3%)
Breast (n=76, 9.3%)
Prostate (n=169, 20.6%)
Hematologic malignancies* (n=309, 37.6%)
C .08 Cumulative incidence of HLM .02 .04 .06
1.00
B
25
50
75 100 125 Survival time (months)
HL-1
HL-2
150
175
0
Proportion surviving 0.25 0.50 0.75 0
0.00
0.00
Proportion surviving 0.25 0.50 0.75
1.00
A
0
25
50
75 100 125 Survival time (months)
HL-1
HL-2
150
175
0
25
50
75 100 125 Analysis time (months) HL-1
HL-2
150
175
S2152-2650(20)30002-1
DOI:
https://doi.org/10.1016/j.clml.2019.12.017
Reference:
CLML 1500
To appear in:
Clinical Lymphoma, Myeloma and Leukemia
Received Date: 4 September 2019 Revised Date:
27 December 2019
Accepted Date: 31 December 2019
Please cite this article as: Budnik J, Doucette C, Milano MT, Constine LS, Survival of Patients with Second Primary Hodgkin Lymphoma, Clinical Lymphoma, Myeloma and Leukemia (2020), doi: https:// doi.org/10.1016/j.clml.2019.12.017. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Elsevier Inc. All rights reserved.
1 Survival of Patients with Second Primary Hodgkin Lymphoma a
a
a
a
Justin Budnik MD* , Christopher Doucette MD , Michael T Milano MD, PhD , Louis S Constine MD *Corresponding author [email protected] (585)-275-5216 a
University of Rochester Medical Center Department of Radiation Oncology
601 Elmwood Avenue Rochester, NY 14642 (585)-275-2171 [email protected] [email protected] [email protected]
Abstract Introduction: The increased risk for second malignancies after Hodgkin lymphoma (HL) diagnosis is well-known. However, no study has investigated the outcomes of patients diagnosed with HL after an antecedent malignancy (HL-2). We aimed to investigate overall survival (OS), disease-specific survival (DSS) and correlates of survival in HL2, using the Surveillance, Epidemiology and End Results (SEER) database. Materials and Methods: HL-2 patients (n=821) identified from 2000-2014 SEER 18 registries were compared to first primary HL patients (HL-1, n=31,355) from the same registries. Multivariable, propensity score-matched (PSM), and competing risks regression (CRR) analyses were conducted to assess the effect of antecedent malignancy on survival. Results: Hematologic (n=309, 37.6%), prostate (n=169, 20.6%) and breast (n=76, 9.3%) malignancies were common antecedent malignancies in HL-2. Median latency between antecedent malignancy and HL diagnosis was 39 months. Median age at HL diagnosis for HL-1 and HL-2 were 36 and 66 years, respectively (p<0.001). The 5 year OS and HL-DSS rates for HL-2 vs HL-1 were 53.2% vs 82.7% and 79.1% vs 90.9%, respectively (p<0.001). On multivariable analysis, a history of antecedent malignancy was associated with decreased OS (HR 1.27, 95%CI 1.131.42, p<0.001). With PSM balancing across co-variables, a history of antecedent malignancy was associated with decrements in HL-DSS (HR 1.46, 95%CI 1.12-1.92, p=0.006), and OS (HR 2.09, 95%CI 1.74-2.51, p<0.001). Conclusions: The decrement in DSS in HL-2 relative to HL-1 may be related to biological differences in HL, age, and/or other unanalyzed factors. Further study of HL-2 patients is warranted.
2 Micro abstract: The outcomes of patients with a second primary Hodgkin lymphoma after an antecedent malignancy have never been described. We used cancer registry data to compare survival between 821 patients with second primary Hodgkin lymphoma and 31,355 patients with first primary Hodgkin lymphoma. We found that survival outcomes were worse in the second primary Hodgkin lymphoma group, a novel finding. Clinical practice points: • • • • • •
The characteristics and survival outcomes of patients with second primary Hodgkin lymphoma after an antecedent malignancy have never been described. We used a population-based approach to compare clinical and pathologic characteristics and survival outcomes between patients with second primary Hodgkin lymphoma and first primary Hodgkin lymphoma. We found a disproportionate number of antecedent hematologic malignancies in those with second primary Hodgkin lymphoma, compared to the general population incidence of hematologic malignancies. We show that the survival outcomes of those with second primary Hodgkin lymphoma are inferior to those with first primary Hodgkin lymphoma when accounting for shared variables (with multivariable regression and propensity score-matching models). These novel findings will inform clinicians of the prognosis of patients with second primary Hodgkin lymphoma. These results may also promote further study of second hematologic malignancies following antecedent cancers.
Keywords: Population-based; Hematologic malignancies; Antecedent malignancy; prognosis; SEER
Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or notfor-profit sectors. Conflicts of interest: None to disclose, all authors. 2,883 words (excluding abstract, references), 4 tables, 2 color figures, 1 supplemental table
3 Introduction: Hodgkin lymphoma (HL) is a relatively uncommon malignancy accounting for an estimated 8,110 new cases, and 1,000 deaths in the USA in 2019 [1]. The care of patients with HL has evolved over the past several decades such that it is now one of the most curable cancers—overall survival (OS) rates for Ann Arbor stage I and stage II HL exceed 90% at 5 years. For those diagnosed with stage III and stage IV HL, 5 year OS rates are in excess of 70% [2]. This is in stark contrast to other more common cancers such as non-Hodgkin lymphoma (NHL), breast, and lung cancers, where there is a more pronounced survival decrement associated with increasing stage [1].
From the study of long-term HL survivors an understanding of the increased risks of second malignancies and late morbidity after HL treatment has been cultivated and extensively described [3-7]. No study however, has investigated the incidence and survival implications of second primary HL after an antecedent malignancy; thus HL disease-specific survival (HL-DSS) and OS in this population are unknown, as are the clinical correlates of survival in this population. We aimed to investigate this using the Surveillance, Epidemiology and End Results (SEER) database.
Materials and Methods: Using the SEER-18 registries we identified patients diagnosed with HL after an antecedent malignancy between the years 2000-2014 (HL-2). For a comparison cohort we identified patients with first primary HL from the same registries (HL-1). Only patients with complete survival, and cause of death information were included. Cause of death information was taken from the “SEER cause-specific death classification” field. OS time was measured from date of HL diagnosis to death or last follow up. HL-DSS time was measured from date of HL diagnosis to death from HL or last follow up. Variables of interest were selected a priori and used consistently throughout all survival analysis models. Stage information for HL in SEER is given by the Ann Arbor staging system [8]. This system was used consistently throughout the study years with respect to anatomic extent of HL. The 2014 Lugano classification modifications to the Ann Arbor system could not be reconstituted from the information available in SEER [9]. HL pathologic subtype groupings were based on International Classification of Diseases for Oncology third edition (ICD-O-3) morphology codes: ‘HL not otherwise specified (NOS)’ (9650/3), lymphocyte rich HL (9651/3), mixed cellularity HL (9652/3), lymphocyte depleted (9653/3), nodular sclerosing HL (9663/3, 9664/3, 9665/3, 9667/3) and nodular lymphocyte-predominant HL (9659/3). Antecedent malignancies in the HL-2 group were ascertained by ICD-O-3 site codes for solid tumor histologies, and by a combination of site and morphology
4 codes for the hematologic malignancies (in order to avoid classifying a lymphoma or leukemia confined to a particular anatomic site as a solid primary tumor of that anatomic site). Information on treatment for HL included: receipt of HL-directed radiotherapy (RT) or no or unknown receipt of radiation; and receipt of chemotherapy (CT), or no or unknown receipt of chemotherapy. No and unknown receipt of therapy are grouped together in the SEER database due to underreporting of treatment (particularly chemotherapy) in the SEER registries [10]. Patients who received radiotherapy and chemotherapy for HL were coded as having received “combination therapy”. Patients coded as no/unknown receipt of radiotherapy and chemotherapy were labelled as “no treatment recorded”. HL patients receiving HL-directed surgical procedures in excess of biopsy were excluded from analyses. The ‘Collaborative Stage (CS) Site-Specific Factor 2’ field (http://web2.facs.org/cstage0205/lymphoma/Lymphoma_kpg.html) codes for the presence or absence of B symptoms (fever, night sweats, and weight loss) at diagnosis; this information was complete for the majority of patients, and was reconstituted as ‘present’, ‘absent’ or ‘unknown’ for the purposes of analysis. Minimum latency between antecedent malignancy and HL-2 diagnosis was set at 2 months in order to exclude patients with synchronous primary cancers. This is the methodology recommended for use in the SEER program [11].
Categorical patient and tumor characteristics were summarized as counts and frequencies and compared using chi square tests. Continuous variables were compared using t tests. OS and HL-DSS were compared between HL-1 and HL-2 using Kaplan-Meier (KM) curves and log rank tests. Multivariable Cox regression analysis was used to assess the impact of patient and tumor characteristics on OS and HL-DSS. Competing risks regression (CRR) and cumulative incidence plots were used to assess the hazard of HL mortality (HLM) while accounting for other cause mortality (OCM). Propensity score matching (PSM) is a technique that has been used extensively in retrospective analyses to reduce biases by balancing patients across covariables that could influence their subsequent inclusion in a group of interest [12]. In this case, PSM was used to confirm the significance of the average effect of antecedent malignancy on OS and HL-DSS as estimated with Cox regression. We used PSM with a one-to-one nearest neighbor matching paradigm without replacement and a fixed caliper width of 0.05 times the logit of the propensity score for HL group [13]. This particular model has been used in other contemporary registry based
5 studies [14-15]. Univariable Cox regression was then used to assess the survival impact of antecedent malignancy on the PSM cohort [12].
Results:
Patient and tumor characteristics:
A total of 821, and 31,355 patients were included in HL-2 and HL-1, respectively. Median follow up after HL diagnosis was 64 months, and 77 months among those alive at last follow-up. Median latency between antecedent malignancy diagnosis and HL-2 diagnosis was 39 months (range 2 to 174 months). Table 1 summarizes patient and tumor characteristics at the time of HL diagnosis. The age distributions of HL-1 and HL-2 differed significantly, with median age at HL diagnosis of 36 and 66 years, respectively (t-test p<0.001). The proportion of males was significantly higher in HL-2 (n=540, 65.8%), than in HL-1 (n=17,146, 54.7%). Nodular sclerosing HL was more frequently diagnosed in HL-1 (n=18,192, 58.1%) than HL-2 (n=267, 32.5%). Ann Arbor stage I-II disease was more common in HL-1 (n=17,825, 56.8%) than HL-2 (n=397, 48.4%). A higher proportion of patients in HL-1 received combination therapy (n=9,312, 29.7%) than HL-2 (n=117, 14.3%). The proportion of patients with B symptoms present at diagnosis were similar between the groups (n=8,996, 28.7% of HL-1 versus n=231, 28.1% of HL-2).
Antecedent malignancies in HL-2:
Figure 1 depicts the frequencies of antecedent malignancies encountered in HL-2 grouped by body site. Hematologic malignancies (n=309, 37.6%) were the most common antecedent malignancies overall, and of these most were non-Hodgkin lymphomas (n=224), or leukemias (n=69). The most common antecedent solid tumors were prostate cancer (n=169, 20.6%), and breast cancer (n=76, 9.3%).
Impact of patient and tumor characteristics on OS:
Multivariable Cox regression was performed to account for the effects of age at and year of diagnosis, sex, race, HL histology, Ann Arbor stage, HL therapy, B symptoms, and HL group on OS (table 2). On multivariable analysis, older age (hazard ratio [HR] 1.05 per year; 95% confidence interval [95% CI], 1.05-1.06; p<0.001), black vs. white race (HR 1.49, 95% CI 1.38-1.61, p<0.001), lymphocyte depleted vs. nodular sclerosing HL histology (HR 1.51, 95% CI
6 1.27-1.80, p<0.001), increasing Ann Arbor stage, and presence of B symptoms (HR 1.46, 95%CI 1.36-1.57, p<0.001), were all associated with an OS decrement. Female sex (HR 0.81, 95% CI 0.77-0.85, p<0.001), and nodular lymphocyte predominant vs. nodular sclerosing HL histology (HR 0.51, 95% CI 0.44-0.61, p<0.001) were associated with improved OS. For HL therapy, chemotherapy without radiotherapy (HR 0.76, 95% CI 0.71-0.82, p<0.001) and radiotherapy without chemotherapy (HR 0.70, 95% CI 0.61-0.80, p<0.001) were associated with improved OS compared to patients in the “no treatment recorded” group. Combination therapy (HR 0.45, 95% CI 0.46, 95% CI, p<0.001) was also associated with improved OS outcomes compared to “no treatment recorded”.
Impact of antecedent malignancy on OS and HL-DSS:
Deaths from HL were less frequent in HL-1 (n=2,609, 8.3% of HL-1 patients) compared to HL-2 (n=143, 17.4% of HL2 patients). Table 3 depicts the median OS survival estimates as well as the actuarial 12 month and 60 month OS rates (%) by HL group and Ann Arbor stage. The aggregate 60 month OS and HL-DSS rates for HL-2 vs HL-1 were 53.2% vs 82.7% and 79.1% vs 90.9%, respectively (log rank test p<0.001). Figure 2 depicts the KM OS estimates (panel A), and HL-DSS estimates (panel B) for the HL-1 and HL-2 groups. On multivariable Cox regression (table 2) a history of antecedent malignancy was associated with a significant OS decrement (HR 1.28, 95% CI 1.14-1.43, p<0.001). This was further investigated with PSM analysis in which 795 (97%) HL-2 patients were matched with an equal number of HL-1 controls balanced across covariables including age at and year of diagnosis, sex, race, HL histology, Ann Arbor stage, HL therapy received, and B symptoms. The PSM HL-1 and HL-2 cohorts were wellbalanced overall (supplemental table 1). Within the PSM cohorts a history of antecedent malignancy remained associated with significant OS (HR 2.09, 95% CI 1.74-2.51, p<0.001) and HL-DSS (HR 1.46, 95% CI 1.12-1.92, p=0.006) decrements (table 4).
Impact of antecedent malignancy on HLM:
Figure 2, panel C depicts the cumulative incidence plots for HLM by HL group. These are based on a CRR model for HLM accounting for OCM. Covariables in the model included age at and year of diagnosis, sex, race, HL histology, Ann Arbor stage, HL therapy, and B symptoms. The cumulative incidence of HLM was low in both groups. The sub-
7 distribution hazard ratio (SHR) for HLM was non-significantly inferior among HL-2 patients (SHR 1.19, 95% CI 0.991.44, p=0.06).
Discussion:
This is the first population-based study of survival outcomes in patients who develop second primary HL after an antecedent malignancy. Notable findings include more adverse prognostic factors in the HL=2 group (including more advanced stage and aggressive histologies), less aggressive treatment for HL, and worse disease-specific survival outcomes even when accounting for the differences in patient- cancer- and treatment-related factors. The methodology we have employed mirrors that used in other contemporary population-based analyses of second malignancies [15-19]. Of note, rather than relying solely on multivariable regression and censored time-to-event methodologies to ascertain the impact of antecedent malignancy on survival outcomes, we have utilized PSM and CRR, to minimize bias, and to account for competing mortality risks, respectively [12,20].
The presented data agree with the body of literature accumulated from retrospective studies and prospective trials in HL with respect to risk factors associated with increased mortality from HL [21-30]. Particularly, we redemonstrate that increasing age, male sex, African American race, increasing Ann Arbor stage, lymphocytedepleted HL histology, and presence of B symptoms are all associated with significant survival decrements (table 1). Conversely, nodular lymphocyte predominant HL, which is increasingly recognized as a singular entity with an indolent course, was associated with improved survival outcomes in our study. This is consistent with contemporary literature [31-32].
With respect to HL treatment, combination therapy was associated with improved survival outcomes on multivariable analysis, but was less frequently utilized in patients in HL-2 versus HL-1. The decreased utilization may indirectly point to a difference in ability to tolerate more aggressive HL therapy between the groups. This could be due to lower performance status in HL-2 associated with significantly increased median age, and/or from sequelae of prior cancer treatment. Treatment for an antecedent malignancy can also impose limits on subsequent therapeutic options for HL, such as in the case of cumulative dose tolerances of organs at risk in successive courses of radiotherapy, or lifetime anthracycline doses in successive courses of chemotherapy.
8 The differential patterns of combination therapy utilization may also result from the increased proportion of Ann Arbor stage I-II HL in HL-1 compared to HL-2. Early stage (Ann Arbor stage I or II) HL has established indications for the incorporation of radiotherapy in contemporary treatment paradigms, advanced stage HL does not [33-34]. Regardless of these suppositions, other retrospective literature suggests that decreased utilization of combined modality therapy for HL with advancing age is real [35].
The most common antecedent malignancies encountered in HL-2 were other hematologic malignancies (figure 1). The observed incidence of antecedent hematologic malignancies in HL-2 is out of proportion to the incidence of hematologic malignancies in the general population [1]. The reason for this increased relative incidence is not inferable in the present study, however, the risk of treatment-related second hematologic malignancy following antecedent hematologic malignancy has been established previously, and may be a partial cause [36-37]. The next most common antecedent malignancies encountered in HL-2 were other common solid tumors; prostate cancer and breast cancer, which are the most common cancers in males and females, respectively [1]. The predominance of prostate cancer (n=169, 31% of males in HL-2) and breast cancer (n=76, 27% of females in HL-2) were roughly equal in proportion to the number of male and female patients in HL-2, and similar to the estimated incidence rate of prostate and breast cancer in the general male and female populations, respectively [1]. This weighs against any particular causal association between antecedent prostate cancer or breast cancer and development of subsequent HL.
The overall results of the survival analyses performed in this study show that a history of antecedent malignancy is associated with both OS and HL-DSS decrements (figure 2, tables 3-4). This is consistent with the observation of survival decrements imposed by a history of antecedent malignancy in other population-based studies of second solid tumors [15]. There are exceptions from this trend in the second malignancies literature. Two of note are that of first primary HL followed by second primary breast cancer and that of first primary HL followed by thyroid cancer [18-19]. In the first case, a history of antecedent HL was shown to confer a minimal breast cancer causespecific survival decrement in those who subsequently developed localized stage breast cancer, and no significant cause-specific survival decrement in those who subsequently developed regional or distant stage breast cancer
9 [18]. In the second case, a history of first antecedent HL was associated with no significant survival decrement in patients who subsequently developed a second primary thyroid cancer [19].
As mentioned previously, the HL survivor population has been studied extensively with respect to late cancer treatment effects and second malignancy risks, and as a result screening guidelines for HL survivors have been established [38]. In the present study, owing to the heterogeneous mix of antecedent malignancies encountered in HL-2, there would not necessarily have been any heightened HL surveillance in survivors of solid tumors, which carry heterogeneous guidelines for follow up care.
On CRR we demonstrated a trend toward increased HLM hazard in HL-2 compared to HL-1 without meeting the threshold for statistical significance (figure 2). The results of the CRR analysis however, are in general agreement with the OS and HL-DSS decrements ascertained on multivariable and PSM analysis with respect to the magnitude of the HLM effect (figure 2, table 4). Overall, the CRR model used in this scenario may be limited by a low overall incidence of HLM in both HL-1 and HL-2, a significant surplus of OCM in HL-2 compared to HL-1, which was tied most strongly to the increased age in the cohort, and to missing co-variables which could have significant effect on the model.
Limitations:
Our study is subject to the inherent limitations of retrospective registry-based data. Unlike a prospective study, the observational nature of the registry data allow for significant biases related to imbalance of demographic and clinical factors amongst groups of interest, and missing covariables which could affect the inferred effects of other covariables on survival outcomes. Notable in the case of the present study are the absence of patient performance status, HL specific risk factors including prognostic biomarkers (e.g. erythrocyte sedimentation rate) and extent of disease at diagnosis (including the particular anatomic sites involved, presence or absence of ‘bulk’ disease, and number of nodal versus extra-nodal sites involved). Also absent are particular details of chemotherapy and radiotherapy treatment such as identity and doses of chemotherapy agents used, and number of chemotherapy cycles given, as well as radiotherapy dose/fractionation schedules and radiotherapy treatment volume.
10 SEER employs strict criteria for the establishment of multiple primary cancers (http://seer.cancer.gov/tools/mphrules/). Despite this stringency the misclassification of one HL subtype for another, or in the case or nodular lymphocyte predominant HL particularly, misclassification as NHL (given similar histopathologic features between nodular lymphocyte predominant HL and B cell NHL), are not precluded. However, we think the overall impact of any histopathologic misclassification in this study is likely small, given that the study years are well within the era of integrated morphological and immunohistochemical characterization of lymphomas.
Additionally, the years of our study straddle the widespread adoption of positron-emission computed tomography (PET-CT) in both HL staging and in assessing response to therapy [9, 3940]. No information about PET-CT utilization in staging, treatment decision making, or in disease response assessment are available in SEER.
Conclusions: HL-2 is rare compared to HL-1. The most common malignancies preceding HL-2 diagnosis were other hematologic malignancies and common solid tumors. On multivariable and PSM analysis a history of antecedent malignancy appears to adversely affect OS and DSS in those who subsequently develop HL. The HL-DSS decrement was a surprising finding, which may be related to biological differences in HL in this population, age, and/or the effect of missing co-variables known to influence survival such as performance status and specifics of treatment. Further study of this interesting sub-group of HL patients is warranted.
11 References:
1) Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA: A Cancer Journal for Clinicians. 2019;69:7-34. 2) Ries LA, Young JL, Keel GE, Eisner MP, Lin YD, Horner MJ, eds. SEER Survival Monograph: Cancer Survival Among Adults: U.S. SEER Program, 1988-2001, Patient and Tumor Characteristics. NIH Pub. No. 076215. Bethesda, MD: National Cancer Institute, SEER Program; 2007. 3) Mauch PM, Kalish LA, Marcus KC, et al. Long-Term Survival in Hodgkin's Disease. Cancer J Sci Am. 1995;1(1):33-42. 4) Aisenberg AC: Problems in Hodgkin's disease management. Blood. 1999;93(3):761-779. 5) Longo DL, Armitage JO. Controversies in the treatment of early-stage Hodgkin's lymphoma. N Engl J Med. 2015;372(17):1667-1669. 6) Aleman BM, van den Belt-Dusebout AW, Klokman WJ, et al. Long-term cause-specific mortality of patients treated for Hodgkin's disease. J Clin Oncol. 2003;21(18):3431-3439. 7) Sud A, Thomsen H, Sundquist K, et al. Risk of Second Cancer in Hodgkin Lymphoma Survivors and Influence of Family History. J Clin Oncol. 2017;35(14):1584-1590. 8) Carbone PP, Kaplan HS, Musshoff K, et al. Report of the committee on Hodgkin's disease staging classification. Cancer Res. 1971;31:1860–1861. 9) Cheson BD, Fisher RI, Barrington SF, et al. Recommendations for initial evaluation, staging, and response assessment of Hodgkin and non-Hodgkin lymphoma: the Lugano classification. J Clin Oncol. 2014;32:30593068. 10) Noone AM, Lund JL, Mariotto A, et al. Comparison of SEER Treatment Data With Medicare Claims. Med Care. 2016;54(9):e55–e64. 11) Curtis RE, Freedman DM, Ron E, et al. Chapter 2: methods. In: Curtis RE, Freedman DM, Ron E, et al., eds. New Malignancies Among Cancer Survivors: SEER Cancer Registries, 1973–2000 National Cancer Institute, NIH Publ No 05-5302. Bethesda, MD; 2006. 12) Austin PC. The use of propensity score methods with survival or time-to-event outcomes: reporting measures of effect similar to those used in randomized experiments. Stat Med. 2014;33(7):1242–58.
12 13) Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat. 2011;10:150–161. 14) Rusthoven CG, Lanning RM, Jones BL, et al. Metastatic nasopharyngeal carcinoma: Patterns of care and survival for patients receiving chemotherapy with and without local radiotherapy. Radiother Oncol. 2017;124(1):139-146. 15) Budnik J, DeNunzio NJ, Singh DP, Milano MT. Second primary non-small-cell lung cancer after head and neck cancer: A population-based study of clinical and pathologic characteristics and survival outcomes in 3597 patients. Clin Lung Cancer. 2019;Mar 4:pii: S1525-7304(19)30067-1. doi: 10.1016/j.cllc.2019.02.017. 16) Donin N, Filson C, Drakaki A, et al. Risk of second primary malignancies among cancer survivors in the United States, 1992 through 2008. Cancer. 2016;122(19):3075–3086. 17) Han SS, Rivera GA, Tammemägi MC, et al. Risk Stratification for Second Primary Lung Cancer. J Clin Oncol. 2017;35(25):2893–2899. 18) Milano MT, Li H, Gail MH, Constine LS, Travis LB. Long-term survival among patients with Hodgkin's lymphoma who developed breast cancer: a population-based study. J Clin Oncol. 2010;28(34):5088–5096. 19) Chowdhry AK, Fung C, Chowdhry VK, et al. A population-based study of prognosis and survival in patients with second primary thyroid cancer after Hodgkin lymphoma. Leuk Lymphoma. 2018;59(5):1180-1187. 20) Dignam JJ, Zhang Q, Kocherginsky M. The use and interpretation of competing risks regression models. Clin Cancer Res. 2012;18(8):2301–2308. 21) Cosset JM, Henry-Amar M, Meerwaldt JH, et al. The EORTC trials for limited stage Hodgkin's disease. The EORTC Lymphoma Cooperative Group. Eur J Cancer. 1992;28A(11):1847-1850. 22) Evens AM, Helenowski I, Ramsdale E, et al. A retrospective multicenter analysis of elderly Hodgkin lymphoma: outcomes and prognostic factors in the modern era. Blood. 2012;119(3):692-695. 23) Klimm B, Franklin J, Stein H, et al. Lymphocyte-depleted classical Hodgkin's lymphoma: a comprehensive analysis from the German Hodgkin study group. J Clin Oncol. 2011;29(29):3914-3920. 24) Evens AM, Antillón M, Aschebrook-Kilfoy B, Chiu BCH. Racial disparities in Hodgkin's lymphoma: a comprehensive population-based analysis. Ann Oncol. 2012;23(8):2128–2137.
13 25) Jost LM, Stahel RA. ESMO Guidelines Task Force: ESMO Minimum Clinical Recommendations for diagnosis, treatment and follow-up of Hodgkin's disease. Ann Oncol. 2005;16(Suppl 1):54-55. 26) Hasenclever D, Diehl V. A prognostic score for advanced Hodgkin's disease. International Prognostic Factors Project on Advanced Hodgkin's Disease. N Engl J Med. 1998;339(21):1506-1514. 27) Moccia AA, Donaldson J, Chhanabhai M, et al. International Prognostic Score in advanced-stage Hodgkin's lymphoma: altered utility in the modern era. J Clin Oncol. 2012;30(27):3383-3388. 28) Sasse S, Bröckelmann PJ, Goergen H, et al. Long-Term Follow-Up of Contemporary Treatment in EarlyStage Hodgkin Lymphoma: Updated Analyses of the German Hodgkin Study Group HD7, HD8, HD10, and HD11 Trials. J Clin Oncol. 2017;35(18):1999-2007. 29) Radford J, Illidge T, Counsell N, et al. Results of a trial of PET-directed therapy for early-stage Hodgkin's lymphoma. N Engl J Med. 2015;372(17):1598-1607. 30) Raemaekers JM, André MP, Federico M, et al. Omitting radiotherapy in early positron emission tomography-negative stage I/II Hodgkin lymphoma is associated with an increased risk of early relapse: Clinical results of the preplanned interim analysis of the randomized EORTC/LYSA/FIL H10 trial. J Clin Oncol. 2014;32(12):1188-1194. 31) Bodis S, Kraus MD, Pinkus G, et al. Clinical presentation and outcome in lymphocyte-predominant Hodgkin's disease. J Clin Oncol. 1997;15(9):3060-3066, 1997. 32) Orlandi E, Lazzarino M, Brusamolino E, et al. Nodular lymphocyte predominance Hodgkin's disease: longterm observation reveals a continuous pattern of recurrence. Leuk Lymphoma. 1997;26(3-4):359-368. 33) Bröckelmann PJ, Sasse S, Engert A. Balancing risk and benefit in early-stage classical Hodgkin lymphoma. Blood. 2018;131(15):1666-1678. 34) Loeffler M, Brosteanu O, Hasenclever D, et al. Meta-analysis of chemotherapy versus combined modality treatment trials in Hodgkin's disease. International Database on Hodgkin's Disease Overview Study Group. J Clin Oncol. 1998;16(3):818-829. 35) Olszewski AJ, Shrestha R, Castillo JJ. Treatment selection and outcomes in early-stage classical Hodgkin lymphoma: analysis of the National Cancer Data Base. J Clin Oncol. 2015;33(6):625-633.
14 36) Armitage JO, Carbone PP, Connors JM, et al. Treatment-related myelodysplasia and acute leukemia in non-Hodgkin's lymphoma patients. J Clin Oncol. 2003;21:897-906. 37) Koontz MZ, Horning SJ, Balise R, et al. Risk of therapy-related secondary leukemia in Hodgkin lymphoma: the Stanford University experience over three generations of clinical trials. J Clin Oncol. 2013;31:592-598. 38) Ng AK. Current survivorship recommendations for patients with Hodgkin lymphoma: focus on late effects. Blood. 2014;124(23):3373-3379. 39) El-Galaly TC, Hutchings M, Mylam KJ, et al. Impact of 18F-fluorodeoxyglucose positron emission tomography/computed tomography staging in newly diagnosed classical Hodgkin lymphoma: fewer cases with stage I disease and more with skeletal involvement. Leuk Lymphoma. 2014;55(10):2349-2355. 40) Cheson BD, Pfistner B, Juweid ME, et al. International Harmonization Project on Lymphoma. Revised response criteria for malignant lymphoma. J Clin Oncol. 2007;25(5):579-586.
15 Table 1. Patient and tumor characteristics by HL group. Variable
HL-1 n=31,355 n (column %)
HL-2 n=821 n (column %)
Total n=32,176 n (column %)
p value^
Age <0.001 ≤39 17,568 (56.1) 66 (8.0) 17,634 (54.8) 40-49 4,576 (14.6) 68 (8.3) 4,644 (14.4) 50-59 3,463 (11.1) 115 (14.0) 3,578 (11.1) 60-69 2,616 (8.4) 243 (29.6) 2,859 (8.9) 70-79 1,972 (6.3) 209 (25.5) 2,181 (6.7) ≥80 1,140 (3.6) 120 (14.6) 1,260 (3.9) Sex <0.001 Male 17,146 (54.7) 540 (65.8) 17,686 (55.0) Female 14,189 (45.3) 281 (34.2) 14,470 (45.0) Race <0.001 White 25,548 (81.5) 705 (85.9) 26,253 (81.6) Black 3,797 (12.1) 83 (10.1) 3,880 (12.1) Other* 1,990 (6.4) 33 (4.0) 2,023 (6.3) HL histology <0.001 Classic HL NOS 6,588 (21.0) 327 (39.8) 6,915 (21.5) Nodular sclerosing 18,192 (58.1) 267 (32.5) 18,459 (57.4) Lymphocyte rich 949 (3.0) 38 (4.6) 987 (3.1) Mixed cellularity 3,693 (11.8) 117 (14.3) 3,810 (11.9) Lymphocyte depleted 351 (1.1) 15 (1.8) 366 (1.1) Nodular lymphocyte predominant 1,562 (5.0) 57 (6.9) 1,619 (5.0) Ann Arbor stage <0.001 I 5,351 (17.0) 182 (22.2) 5,533 (17.0) II 12,474 (39.8) 215 (26.2) 12,689 (39.5) III 6,261 (20.0) 178 (21.7) 6,439 (20.0) IV 5,678 (18.1) 188 (22.9) 5,866 (18.2) Unknown 1,571 (5.0) 58 (7.1) 1,629 (5.1) B symptoms <0.001 Absent 11,020 (35.2) 360 (43.9) 11,380 (35.4) Present 8,996 (28.7) 231 (28.1) 9,227 (28.7) Unknown 11,319 (36.1) 230 (28.0) 11,549 (35.9) Radiotherapy <0.001 No/Unknown 20,869 (66.6) 654 (79.7) 21,523 (66.9) Yes 10,466 (33.4) 167 (20.3) 10,633 (33.1) Chemotherapy <0.001 No/Unknown 5,619 (17.9) 245 (29.8) 5,864 (18.2) Yes 25,716 (82.1) 576 (70.2) 26,292 (81.8) Therapy combination <0.001 No treatment recorded 4,478 (14.3) 197 (24) 4,675 (14.5) Radiotherapy 1,141 (3.6) 48 (5.9) 1,189 (3.7) Chemotherapy 16,404 (52.4) 459 (55.9) 16,863 (52.4) Radiotherapy + chemotherapy 9,312 (29.7) 117 (14.3) 9,429 (29.3) Legend. Hodgkin lymphoma (HL). *Other includes Asian, Alaska native/Pacific Islander, and unknown race. ^p values are from Pearson’s chi square test.
16 Table 2. Multivariable regression analysis^. Variable HR 95% CI p value HL group HL-1 HL-2 1.28 1.14-1.43 <0.001 Age (per year increase) 1.05 1.05-1.06 <0.001 Sex Male Female 0.81 0.77-0.85 <0.001 Race White Black 1.49 1.38-1.61 <0.001 Other* 1.19 1.05-1.35 0.009 Year of diagnosis (per year increase) 0.97 0.96-0.97 <0.001 HL Histology Classic HL NOS 1.20 1.12-1.27 <0.001 Nodular sclerosing Lymphocyte rich 0.77 0.66-0.90 0.001 Mixed cellularity 1.03 0.96-1.11 0.438 Lymphocyte depleted 1.51 1.27-1.80 <0.001 Nodular lymphocyte predominant 0.51 0.44-0.61 <0.001 Ann Arbor stage I II 1.19 1.09-1.29 <0.001 III 1.61 1.47-1.75 <0.001 IV 2.06 1.89-2.25 <0.001 Unknown 1.21 1.07-1.37 0.002 B symptoms Absent Present 1.46 1.36-1.58 <0.001 Unknown 1.19 1.10-1.29 <0.001 Therapy combination No treatment recorded Radiotherapy 0.70 0.61-0.80 <0.001 Chemotherapy 0.76 0.71-0.82 <0.001 Radiotherapy + chemotherapy 0.46 0.41-0.50 <0.001 Legend. Hodgkin lymphoma (HL). Hazard ratio (HR). 95% confidence interval (95% CI). *Other includes Asian, Alaska native/Pacific Islander, and unknown race. ^HR, 95% CI, and p values are from multivariable Cox regression accounting for age at and year of diagnosis, sex, race, histology, Ann Arbor stage, B symptoms, and therapy combination.
17 Table 3. OS outcomes by HL group and Ann Arbor stage.
Ann Arbor stage
I
II
III
IV
Unknown
HL-1
HL-2
Median OS (months)
Median OS (months)
12 month OS%
12 month OS%
60 month OS%
60 month OS%
NR
100
94.1
80.8
86.9
60.9
NR
86
96.4
77.0
89.9
63.8
NR
67
88.2
71.2
77.3
49.9
NR
38
81.9
62.7
69.2
39.1
NR
51
88.7
71.1
79.5
45.9
p value^
<0.001
<0.001
<0.001
<0.001
0.002
Legend. Overall survival (OS). Hodgkin lymphoma (HL). Not reached (NR). ^p values are from log rank test comparing HL-1 to HL-2.
18 Table 4. Propensity score-matched survival analysis: Impact of antecedent malignancy on OS and HL-DSS. Survival outcome
HR
95% CI
p value^
OS
2.09
1.74-2.51
<0.001
HL-DSS
1.46
1.12-1.92
0.006
Legend. Propensity score-matched (PSM). Overall survival (OS). Hodgkin lymphoma disease-specific survival (HLDSS). Hazard ratio (HR). 95% confidence interval (95% CI). ^HR, 95% CI, and p values are from univariable Cox regression accounting for impact of antecedent malignancy history on survival outcomes in the PSM sample of 795 patients from the HL-2 group matched to 795 HL-1 patients, balanced across covariables including age at and year of diagnosis, sex, race, histology, Ann Arbor stage, B symptoms, and therapy combination. .
19 Supplemental table 1. Propensity score-matched patient characteristics. Variable
Age ≤39 40-49 50-59 60-69 70-79 ≥80 Sex Male Female Race White Black Other* Year of diagnosis 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 HL histology Classic HL NOS Nodular sclerosing Lymphocyte rich Mixed cellularity Lymphocyte depleted Nodular lymphocyte predominant Ann Arbor stage I II III IV Unknown B symptoms Absent Present Unknown
HL-1 n=795 n
HL-2 n=795 n
Total n=1,590 n
67 67 110 238 201 112
63 67 110 236 205 114
130 134 220 474 406 226
527 268
527 268
1,054 536
718 73 4
705 83 7
1,423 156 11
5 15 25 20 37 43 60 69 59 64 81 81 69 76 91
7 13 23 20 29 38 55 77 79 72 78 66 81 77 80
12 28 48 40 66 81 115 146 138 136 159 147 150 153 171
343 252 32 104 11 53
311 262 36 116 15 55
654 514 68 220 26 108
171 204 173 189 58
170 210 173 185 57
341 414 346 374 115
349 233 213
345 223 227
694 456 440
p value^
0.99
1.00
0.65
0.82
0.65
0.99
0.69
20 Therapy combination 0.32 No treatment recorded 182 190 372 Radiotherapy 32 46 78 Chemotherapy 475 449 924 Radiotherapy + chemotherapy 106 110 216 Legend. Hodgkin lymphoma (HL). *Other includes Asian, Alaska native/Pacific Islander, and unknown race. ^p values are from Pearson’s chi square test.
Figure legends: Figure 1. Antecedent malignancies in HL-2. Legend. Hodgkin lymphoma (HL). Central nervous system (CNS). Genitourinary (GU). Gynecological (GYN). Head and neck (HN). Gastrointestinal (GI). Skin does not include cutaneous basal cell or squamous cell carcinomas which are not recorded in SEER. *Hematologic malignancies includes non-Hodgkin lymphomas (n=224), leukemias (n=69), and other miscellaneous hematologic cancers (n=16), categorized per the International Classification of Diseases for Oncology third edition (ICD-O-3) morphological coding schema.
Figure 2. Kaplan-Meier survival estimates and competing risks regression analysis by HL group. Legend. Hodgkin lymphoma (HL). Overall survival (OS). Hodgkin lymphoma disease-specific survival (HL-DSS). Hodgkin lymphoma mortality (HLM). Panel A; OS estimates. Panel B; HL-DSS estimates. Panel C; Cumulative incidence smoothed plot of HLM based on a competing risks regression model for HLM accounting for other-cause mortality and including age at an year of diagnosis, sex, race, HL histology, Ann Arbor stage, B symptoms and HL therapy as covariables.
n=821
CNS (n=5, 0.6%)
GYN (n=21, 2.6%)
Lung (n=27, 3.3%)
HN (n=43, 5.2%)
Skin (n=55, 6.7%)
GI (n=56, 6.8%)
Non-prostate GU (n=60, 7.3%)
Breast (n=76, 9.3%)
Prostate (n=169, 20.6%)
Hematologic malignancies* (n=309, 37.6%)
C .08 Cumulative incidence of HLM .02 .04 .06
1.00
B
25
50
75 100 125 Survival time (months)
HL-1
HL-2
150
175
0
Proportion surviving 0.25 0.50 0.75 0
0.00
0.00
Proportion surviving 0.25 0.50 0.75
1.00
A
0
25
50
75 100 125 Survival time (months)
HL-1
HL-2
150
175
0
25
50
75 100 125 Analysis time (months) HL-1
HL-2
150
175