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10019 Background: The reported incidence of second malignant neoplasms (SMN) in long-term survivors of Hodgkin lymphoma (HL) is derived from patients treated with outdated radiation therapy (RT) techniques. We modeled the risk of SMN in pediatric patients with high-risk classic HL treated with modern mediastinal RT. Methods: In patients who received mediastinal RT on the Children’s Oncology Group study AHOD1331, we modeled the lifetime attributable risk (LAR) at 70 years of age of thyroid, lung, and breast carcinoma. This value indicates the absolute increased risk of SMN at 70 years of age due to RT, above the baseline population risk (mean baseline rates at 70 years of age are 0.7%, 3.3%, and 8.9% for thyroid, lung, and breast carcinoma, respectively, https://seer.cancer.gov/data/ ). Results: In 296 patients who received protocol-directed mediastinal RT, median age at diagnosis was 15.1 years, 55% were female, and 98% had large mediastinal adenopathy. Following 5 cycles of chemotherapy, patients received RT targeting the mediastinum based on criteria of this study; in addition, some patients received RT to other supradiaphragmatic sites that contained slowly responding lesions, including the axilla (n = 3, 1%), lung (n = 7, 2.4%), upper neck (n = 4, 1.3%), and lower neck (n = 8, 2.7%). The RT modality was proton therapy in 25.3%, photon intensity modulated RT (IMRT) in 45.6%, and photon 3-dimensional conformal RT (3D-CRT) in 27.7%. The RT prescription dose was 21 Gy in 83% and 30 Gy in 16% who had a partial metabolic response at the completion of chemotherapy. The mean (range) doses to the thyroid, lungs, and breasts were 12.8 Gy (0-30.3), 8.0 Gy (0.1-15.2), and 4.2 Gy (0.2-14.5), respectively. For the complete cohort, the mean LAR at 70 years of age of thyroid carcinoma was 0.063% and of lung carcinoma was 5.34%. For females, the mean LAR at 70 years of age of breast carcinoma was 2.92%. The Table summarizes the LAR for each SMN, stratified by RT modality. Conclusions: In patients treated with mediastinal RT on a recent multi-institutional study of pediatric HL, the predicted long-term risk of SMN is substantially lower than in historical cohorts. Clinicians should consider the toxicity associated with a current RT approach when selecting therapies and counseling patients. Clinical trial information: NCT02166463 , this is a post hoc modeling study that includes patients enrolled on this study. Mean [range] lifetime attributable risk (%) at an attained age of 70 years for thyroid, lung, and breast carcinoma. Thyroid Lung Breast (females) 3D-CRT 0.046 [0.002-0.332]N=81 5.07 [0.12-10.82]N=82 1.82 [0.67-5.98]N=46 IMRT 0.064 [0.001-0.386]N=135 6.24 [2.22-11.79]N=135 4.36 [0.66-8.43]N=76 Proton 0.086 [0-1.160]N=74 4.15 [0.94-7.77]N=75 1.45 [0.17-7.23]N=40

ImportanceRetrieval strategies for children, adolescents, and young adults with relapsed classic Hodgkin lymphoma (cHL) aim to maintain efficacy while minimizing long-term toxic effects. Children, adolescents, and young adults with low-risk, relapsed cHL may benefit from replacing high-dose chemotherapy and autologous stem cell transplant with less intensive involved-site radiotherapy (ISRT).ObjectiveTo evaluate a risk-stratified, response-adapted, transplant-free approach for treatment of children, adolescents, and young adults with low-risk relapsed cHL with nivolumab plus brentuximab vedotin (BV) followed by BV plus bendamustine for patients with suboptimal response and ISRT (30.0 to 30.6 Gy).Design, Setting, and ParticipantsCheckMate 744 (R1 cohort) was a phase 2, nonrandomized, single-arm study enrolling children, adolescents, and young adults aged 5 to 30 years with low-risk cHL between September 25, 2017, and December 16, 2020, across the US, Canada, and Europe. Data were analyzed from September 2017 to November 2022.ExposuresPatients received 4 cycles of nivolumab plus BV induction; patients with complete metabolic response (CMR) received an additional 2 cycles of nivolumab plus BV while patients with suboptimal response received 2 cycles of BV plus bendamustine intensification. Patients with CMR after induction or intensification received ISRT consolidation.Main Outcomes and MeasuresPrespecified coprimary end points were CMR rate (Lugano 2014 classification) any time before ISRT and 3-year event-free survival (EFS) rate, per blinded independent central review (BICR).ResultsOf 28 included patients treated in the low-risk cohort, 18 (64%) were female, and the median (range) age was 17 (6-27) years. At a median (range) follow-up of 31.9 (2.2-55.3) months, CMR per BICR any time before ISRT was 93% (26 of 28; 90% CI, 79.2-98.7; objective response rate [ORR], 100%), and 23 of 28 (82%) achieved CMR per BICR after 4 cycles of nivolumab plus BV (ORR, 96.4%). Kaplan-Meier estimates of EFS and progression-free survival rates at 3 years were 87% (3 of 18; 90% CI, 69.5-94.7) and 95% (1 of 18; 90% CI, 76.7-99.0), respectively. During induction, 22 patients (79%) had treatment-related adverse events, including 7 with grade 3 or 4 adverse events, 2 with anemia, 1 with neutropenia, and 6 with immune-mediated adverse events. Serious adverse events leading to discontinuation occurred in 2 patients.Conclusions and RelevanceThis nonrandomized clinical trial found that for children, adolescents, and young adults with low-risk, relapsed cHL, a transplant-free, risk-adapted, response-based approach with nivolumab plus BV and ISRT offered high CMR rates and high 3-year EFS rate, with a safety profile consistent with that of each agent used.Trial RegistrationClinicalTrials.gov Identifier: NCT02927769

This nonrandomized clinical trial reports outcomes for children with low-risk relapsed Hodgkin lymphoma treated with second-line chemotherapy and involved-field radiotherapy without autologous stem cell transplant.

Background: Although advanced stage cHL typically has excellent disease outcomes, 20-30% of patients (pts) experience treatment failure. In addition, decision-making is challenging given varying treatment options and incomplete prognostic data across the disease course. Pre-treatment factors, such as those incorporated into the Advanced Stage Hodgkin Lymphoma International Prognostication Index (A-HIPI), predict 5 year (y) overall survival (OS) and progression-free survival (PFS) (Rodday JCO 2023). Other than interim PET (iPET) results, it is unknown what factors influence cHL disease states mid-therapy. Using multistate modeling (MSM) and individual pt data from the HoLISTIC Consortium, we refined prognostication across the cHL disease course, specifically assessing the relationships between A-HIPI, iPET and end of treatment (EOT) response, and whether the A-HIPI and iPET provide independent prognostic information. Methods: We analyzed 1240 pts aged 18-65y with newly diagnosed Stage IIB, III or IV cHL treated on 2 advanced stage PET-adapted trials (RATHL, SWOG0816). All pts first received 2 cycles of ABVD; those with a negative iPET (Deauville score (DS) ≤3) received ABVD or AVD, and those with a positive iPET (DS >3) received BEACOPP. In contrast to other methods, MSMs incorporate multiple disease states into one model, account for censoring and competing risks, and estimate transitions between disease states using different covariates. Our MSM had 6 states: diagnosis, negative iPET, positive iPET, EOT non-progression (e.g., DS ≤ 3), treatment failure, and death. Treatment failure included progression or relapse. All pts start at the diagnosis state and can transition to other states without return to prior states. Transition states with <5 events were excluded. The MSM was estimated using a multivariable Cox model with censoring at 5y. Covariates were the 5y PFS A-HIPI (comprised of stage and continuous age, lymphocyte count and albumin), which was included for all transitions, and iPET, which was included for transitions from EOT non-progression and from treatment failure. Higher scores on the 5y PFS A-HIPI indicate lower risk of progression or death; the A-HIPI was modeled for a 1 standard deviation (SD) increase. We calculated probabilities of 5y treatment failure for 4 sample pts based on low (70) and high (85) A-HIPI scores, positive and negative iPET, and EOT non-progression. Results: Median age was 33y (Q1=25, Q3=45), 56% were male, 23% were stage IIB, 40% III and 37% IV, and mean A-HIPI was 77 (SD=7). Median follow-up was 75 months (Q1=53, Q3=92). At iPET, 80% (95% CI 78%, 83%) were negative and 16% (95% CI 14%, 18%) were positive. At EOT, 90% (95% CI 89%, 92%) were in the non-progression state. At 5y, 75% (95% CI 73%, 78%) were in the non-progression state, 16% (95% CI 14%, 19%) had experienced treatment failure, and 9% (7%, 11%) had died. Better A-HIPI scores (per SD) were associated with lower rates of transitioning from diagnosis to positive iPET (HR=0.80, p<0.01) and from positive iPET to treatment failure (HR=0.58, p=0.04). Better A-HIPI scores (per SD) were also associated with lower rates of transitioning from EOT non-progression to treatment failure (HR=0.78, p<0.01) and from treatment failure to death (HR=0.64, p<0.01), adjusting for iPET. Positive iPET was associated with an increased rate of transitioning from EOT non-progression to treatment failure (HR=1.90, p<0.01), adjusting for A-HIPI. The probability of 5y treatment failure for 4 sample pts was: 25% for low A-HIPI and positive iPET; 20% for high A-HIPI and positive iPET; 13% for low A-HIPI and negative iPET; and 12% for high A-HIPI and negative iPET. Conclusions: We created a novel MSM that refines prognostication across the cHL disease course by incorporating pre- and post-treatment factors (e.g., iPET) to estimate transitions to future disease states. Although the A-HIPI was developed using pre-treatment factors to predict 5y OS and PFS, we found that it was also associated with transitions to interim disease states, including iPET and EOT response. The A-HIPI and iPET each provided independent prognostic information, supporting the use of both in estimating cHL outcomes. Future analyses will develop clinical prediction models using MSMs that also incorporate varying frontline and salvage treatments and treatment-specific late effects, with the goal of providing individualized information across the disease course.

PURPOSE Nodular lymphocyte–predominant Hodgkin lymphoma (NLPHL) is a rare cancer, and large international cooperative efforts are needed to evaluate the significance of clinical risk factors and immunoarchitectural patterns (IAPs) for all stages of pediatric and adult patients with NLPHL. METHODS Thirty-eight institutions participated in the Global nLPHL One Working Group retrospective study of NLPHL cases from 1992 to 2021. We measured progression-free survival (PFS), overall survival (OS), transformation rate, and lymphoma-specific death rate. We performed uni- and multivariable (MVA) Cox regression stratified by management to select factors for the lymphocyte-predominant international prognostic score (LP-IPS) validated by five-fold cross-validation. RESULTS We identified 2,243 patients with a median age of 37 years (IQR, 23-51). The median follow-up was 6.3 years (IQR, 3.4-10.8). Most had stage I to II (72.9%) and few B symptoms (9.9%) or splenic involvement (5.4%). IAP was scored for 916 (40.8%). Frontline management included chemotherapy alone (32.4%), combined modality therapy (30.5%), radiotherapy alone (24.0%), observation after excision (4.6%), rituximab alone (4.0%), active surveillance (3.4%), and rituximab and radiotherapy (1.1%). The PFS, OS, transformation, and lymphoma-specific death rates at 10 years were 70.8%, 91.6%, 4.8%, and 3.3%, respectively. On MVA, IAPs were not associated with PFS or OS, but IAP E had higher risk of transformation (hazard ratio [HR], 1.81; P < .05). We developed the LP-IPS with 1 point each for age ≥45 years, stage III-IV, hemoglobin <10.5 g/dL, and splenic involvement. Increasing LP-IPS was significantly associated with worse PFS (HR, 1.52) and OS (HR, 2.31) and increased risk of lymphoma-specific death (HR, 2.63) and transformation (HR, 1.41). CONCLUSION In this comprehensive study of all ages of patients with NLPHL, we develop the LP-IPS to identify high-risk patients and inform upcoming prospective clinical trials evaluating de-escalation of therapy for patients with low LP-IPS scores (<2).

Background: Prognostic modeling allows personalized risk prediction for individual patients (pt). The A-HIPI model in advanced stage classical Hodgkin lymphoma (AS-HL), developed and validated by the HoLISTIC Consortium (www.hodgkinconsortium.org), generates the individualized probability of a progression-free survival (PFS) event or death (OS) within the first 5 years (y) from diagnosis in pts based on continuous variables (www.qxmd.com/calculate/calculator_869/a-hipi). Clinical prognostic tools in lymphoma (eg, IPS, IPI, FLIPI, etc) typically use groupings of categorical values to define risk. Grouping a continuous value often results in loss of information, and most tools are not predictive for individual pts. However, discrete groupings have clinical utility & practicality for a) defining pt populations for clinical trials & real world studies, b) stratification within clinical trials, and c) crafting treatment guidelines. We studied potential approaches for utilizing the A-HIPI model to generate risk groups with input on strengths & limitations from the HoLISTIC modeling team & clinical experts. Methods: The A-HIPI model was developed via TRIPOD guidelines on 4,022 pts treated on 8 international clinical trials for AS-HL (Rodday. JCO 2023). External validation was performed on a dataset of 1,431 pts from 4 prospective registries. The 5y PFS (PFS5) in the A-HIPI development dataset was 77% (95% CI: 76-78); the 5y OS (OS5) was 92% (95% CI: 91-93). This represented the average outcome for a pt with AS-HL pt naïve to other clinical data at presentation. The distribution of PFS5 & OS5 predictions were heavily skewed (ie, asymmetric distribution) in both the A-HIPI discovery and validation dataset. While not unexpected due to the excellent PFS & OS in this disease setting, this presents challenges in the delineation of risk groups as depicted below . Three approaches were examined for the generation of A-HIPI risk groups. Proposed cutoffs were defined using the distribution of A-HIPI risk scores and data from the model-building cohort (ie, clinical trials). Validation was done using the A-HIPI validation cohort (ie, HL registries). Results: Approach 1: Risk groups based on clinical thresholds. Clinicians were queried what estimates of PFS5 would constitute high vs low risk. The positive, right-skewed distribution of A-HIPI risk scores limited this approach ( Figure), as cutoffs of PFS5 &lt;70 and PFS5&gt; 90 would only identify 15% and &lt;1% of the population, respectively. Approach 2: Risk groups based on deviation from “average” pt. The 5-y PFS was 77% (95% CI: 76-78). We explored defining “standard risk” based on this confidence interval as well as clinical boundaries, with pts above or below this classified as decreased or increased risk, respectively. Results of a +/-5% clinical boundary are presented in the Table. Approach 3: Risk groups based on “ranking” of pts. Here we ranked the A-HIPI risk scores of the 4022 AS-HL pts in the model building cohort and used the distribution of the risk scores as a benchmark. The risk profile for a future pt is then compared to this distribution (eg, how do you rank compared to your peers). Continuous results are often presented this way (eg, tertiles or quartiles). It also allows flexibility for the user to define the size of the risk groups and/or clinical threshold of interest. Application of this approach showed good alignment between the predicted model percentiles and the observed distribution of scores in the validation cohort ( Table). This is also reflected in calibration curves presented in the primary manuscript. An online application (eg, R-Shiny) will be provided at the meeting to allow users to define their own cutoffs to aid in pt prognostication as well as identify populations for clinical trial development. Conclusions: There are challenges with defining risk groups from individual risk prediction modeling in AS-HL. Different applications and purposes, the skewed distribution of events/risk estimates, as well as varying clinical definitions of high risk, make it challenging to define consensus expert-based risk groupings for AS-HL. A flexible “rank-based” approach appeared to provide the most clinical utility & data granularity, which may be leveraged for clinical trial design and pt stratification. Further analysis and discussion of how to optimally define high-risk and low-risk populations in AS-HL will be needed as new therapeutic options emerge.