Hodgson, David

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.

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 <70 and PFS5> 90 would only identify 15% and <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.

Background: There are a lack of clinical prediction tools in early-stage Hodgkin lymphoma (E-HL). We developed and validated a modern-day model, known as E-HIPI, to predict progression-free survival (PFS) within the first 5 years (y) incorporating detailed individual patient (pt) data from international clinical trials and prospective registry data that were standardized, normalized & harmonized as part of the HoLISTIC Consortium (www.hodgkinconsortium.org). Methods: Model development utilized Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines (Moons, Ann Intern Med 2015) on a cohort of 3,080 newly-diagnosed E-HL pts treated on 4 seminal phase 3 clinical trials (NCIC: Meyer, NEJM 2012; RAPID: Radford, NEJM 2015; EORTC-GELA H9U: Ferme, E ur J Cancer 2017; EORTC/LYSA/FIL H10: Andre, JCO 2017). External validation was done in an independent cohort of 462 E-HL pts from a large registry (Princess Margaret, Toronto). Pts with classic HL, stage I or II disease, and ages 18-65 y were included. The primary outcome was PFS. Cox models were utilized with follow-up truncated at 5 years. Baseline predictors were: sex, stage, B symptoms, histology, number & location of nodal sites, and continuous data values of age, maximum tumor diameter (MTD), white blood cell count (WBC), lymphocyte count, hemoglobin, albumin & erythrocyte sedimentation rate (ESR). Multiple imputation was used for missing data. To consider possible non-linear relationships for continuous variables we examined plots & cubic splines. We used backward elimination ( P<0.1) to develop the models & internal validation with 200 bootstrap samples was conducted to estimate optimism & correct for overfitting. The final prediction equations applied optimism corrections to beta coefficients & hazard ratios (HR). Sensitivity analyses included model results stratified by PET staging (vs CT) and each HL trial. The models were evaluated in the external validation cohort & compared using c-statistics. Model scores were also grouped by tertiles for visualization in Kaplan-Meier (KM) curves. Results: Median age in the development cohort was 35y; 51% were female; 81% had nodular sclerosis & 77% stage II disease; 26% had B symptoms; 32% had mediastinal bulk (≥10cm or >1/3 diameter) and the mean MTD was 6.4 cm (interquartile range (IQR), 3.9-8.2). Median follow-up was 60 months (IQR 45-75). KM estimates at 5y was 90.7% (264 events) for PFS, and 97.1% (78 events) for overall survival (OS). After backward elimination, significant variables retained were age, sex, nodal location (cervical region), MTD & albumin. Stage, ESR, or the number of nodal sites were not significant. Age was modeled with piecewise linear splines due to its non-linear relationship. See the Table for the final prediction model and optimism-corrected HRs. Optimism-corrected c-statistics in the development model was 0.63. Sensitivity analyses of stratification by type of imaging at staging or by each clinical trial demonstrated no change in model parameters or c-statistics. Most baseline characteristics and outcomes of the external validation cohort were similar besides median follow-up time (74 months, IQR 37-129). C-statistics for the E-HIPI model in external validation was 0.63. In addition, observed 5y outcomes stratified by tertile of predicted risk in the external cohort separated pts into a low & higher-risk group, which comprised 1/3 & 2/3 of pts, respectively ( Figure). Finally, model diagnostics revealed the potential for time dependency in the retained covariates. Further assessment of differential prediction between early (<2y) vs. later (2-5y) PFS events is ongoing. Conclusion: Following TRIPOD methodology, we rigorously developed and externally validated the E-HIPI among >3,500 E-HL pts.Discrete risk groups were delineated, and we identified several novel factors, including nodal location, sex & key continuous variables that predict 5y PFS. Additional model refinement is underway, including formal calibration & external validation via other HL registries (Stanford, Mayo/Iowa SPORE). A web-based calculator to simplify application of the E-HIPI will also be presented at ASH. Future planned modeling includes integration of differing treatments & response-adapted imaging via multistate modeling to enrich individualized survival estimates and simulation modeling to predict late consequences.

AbstractAdolescents and young adults (AYA) with lymphoma experience treatment‐related effects in the short and long term that impact their quality of life and survivorship experience. The effort to improve outcomes for AYA lymphoma survivors requires understanding the available literature, identifying current knowledge deficits, designing better clinical trials incorporating the patient perspective, using novel tools to bridge data gaps and building survivorship guidelines that translate research to clinical practice. This review article summarizes the current state of lymphoma treatment‐related outcomes in AYAs and provides future direction.

7515 Background: Outcomes are poor for patients (pts) with cHL who develop R/R disease after first-line (1L) chemotherapy ± radiotherapy (RT). Salvage tx that attain high event-free survival (EFS) rates and minimize late toxicity by omitting high-dose chemotherapy (HDCT)/auto-HCT are needed. CheckMate 744 (NCT02927769) is the first multicenter phase 2 study evaluating a risk-stratified, response-adapted salvage tx with nivo + BV in CAYA with R/R cHL. In the standard-risk cohort, complete molecular response (CMR) rate before consolidation with HDCT/auto-HCT was 94% (Harker-Murray et al. Blood 2022). With highly active salvage tx, pts in low-risk cohort could achieve high EFS without HDCT/auto-HCT; we report data from this cohort. Methods: Pts were aged 5–30 y and had ≤ 3 cycles (C) of 1L anthracycline-based systemic tx. Risk stratification was described in Harker-Murray et al. Pts received 4C of nivo + BV induction. Pts with CMR received additional 2C nivo + BV before involved-site radiation therapy (ISRT) consolidation (dose, 30–30.6 Gy). Pts with suboptimal response received 2C BV + bendamustine intensification; pts with CMR proceeded to ISRT consolidation. Primary endpoints: CMR rate (Lugano 2014) any time before ISRT and 3-y EFS rate, both per blinded independent central review (BICR). For CMR and overall response rate (ORR), a 90% confidence interval (CI) was used per statistical plan. Results: Among 28 pts treated with nivo + BV, median (range) age was 17 (6–27) y; 64% of pts were aged < 18 y. Most (64%) pts had stage II disease at relapse; 82% had relapse ≥ 12 mo after 1L tx, 2 pts had prior RT. Median (range) follow-up was 31.9 (2.2–55.3) mo. CMR, ORR, 3-y EFS and PFS rates are shown in Table. Median duration of response was not reached; 87% of pts had sustained response at 36 mo’ follow-up. Efficacy outcomes were comparable in the pediatric population (CMR per BICR before ISRT, 88.9%; 3-y EFS rate, 78.3%). During induction, 22 (78.6%) pts had treatment-related adverse events (TRAEs; grade 3/4, 25.0% pts; hematologic, < 10% pts; immune-mediated, 21.4%). Serious AEs leading to discontinuation included rash, pyrexia, and acute kidney injury (1 pt each). Conclusions: The findings demonstrate that most CAYA with low-risk R/R cHL can be salvaged with chemoimmunotherapy with a favorable toxicity profile, and do not require HDCT/auto-HCT. Clinical trial information: NCT02927769 . [Table: see text]

AbstractPurpose/objectiveWe compared the prognostic value of chest radiograph (CXR)‐ and computed tomography (CT)‐derived definition of large mediastinal adenopathy (LMA) in pediatric Hodgkin lymphoma (HL).Materials/methodsTotal 143 patients treated for stage IIIB/IVB HL on COG AHOD0831 were included in this study. Six definitions of LMA were investigated: (i) mediastinal mass ratio on CXR (MRCXR) > 1/3; (ii) mediastinal mass ratio on CT (MRCT) > 1/3; (iii) mediastinal mass volume on CT (MVCT) > 200 mL; (iv) normalized mediastinal mass volume (MVCT/thoracic diameter [TD]) > 1 mL/mm; (v) mediastinal mass diameter on CT (MDCT) > 10 cm; and (vi) normalized mediastinal mass diameter (MDCT/TD) > 1/3.ResultsMedian age at diagnosis was 15.8 years (range: 5.2–21.3 years). In patients with a slow early response (SER) to chemotherapy, MVCT > 200 mL, MDCT > 10 cm, and MDCT/TD > 1/3 were associated with worse relapse‐free survival (RFS) on MVA, while MRCXR > 1/3, MRCT > 1/3, and MVCT/TD > 1 mL/mm trended toward worse RFS; MDCT/TD was the most strongly prognostic for inferior RFS, with a hazard ratio of 6.41 for MDCT/TD > 1/3 versus ≤1/3 on MVA (p = .02).ConclusionLMA according to MVCT > 200 mL, MDCT > 10 cm, and MDCT/TD > 1/3 is associated with poor prognosis in advanced‐stage HL patients with SER. The normalized mediastinal diameter, MDCT/TD > 1/3 appears to be the strongest predictor of inferior RFS.

SummaryThe Children's Oncology Group AHOD0831 study used a positron emission tomography (PET) response‐adapted approach in high‐risk Hodgkin lymphoma, whereby slow early responders (SERs) received more intensive therapy than rapid early responders (RERs). We explored if baseline PET‐based characteristics would improve risk stratification. Of 166 patients enrolled in the COG AHOD0831 study, 94 (57%) had baseline PET scans evaluable for quantitative analysis. For these patients, total body metabolic tumour volume (MTV), total lesion glycolysis (TLG), maximum standardized uptake value (SUVmax) and peak SUV (SUVpeak) were obtained. MTV/TLG thresholds were an SUV of 2.5 (MTV2.5/TLG2.5) and 40% of the tumour SUVmax (MTV40%/TLG40%). TLG2.5 was associated with event‐free survival (EFS) in the complete cohort (p = 0.04) and in RERs (p = 0.01), but not in SERs (p = 0.8). The Youden index cut‐off for TLG2.5 was 1841. Four‐year EFS was 92% for RER/TLG2.5 up to 1841, 60% for RER/TLG2.5 greater than 1841, 74% for SER/TLG2.5 up to 1841 and 79% for SER/TLG2.5 greater than 1841. Second EFS for RER/TLG2.5 up to 1841 was 100%. Thus, RERs with a low baseline TLG2.5 experienced excellent EFS with less intensive therapy, whereas RERs with a high baseline TLG2.5 experienced poor EFS. These findings suggest that patients with a high upfront tumour burden may benefit from intensified therapy, even if they achieve a RER.

PURPOSE The International Prognostic Score (IPS) has been used in classic Hodgkin lymphoma (cHL) for 25 years. However, analyses have documented suboptimal performance of the IPS among contemporarily treated patients. Harnessing multisource individual patient data from the Hodgkin Lymphoma International Study for Individual Care consortium, we developed and validated a modern clinical prediction model. METHODS Model development via Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guidelines was performed on 4,022 patients with newly diagnosed advanced-stage adult cHL from eight international phase III clinical trials, conducted from 1996 to 2014. External validation was performed on 1,431 contemporaneously treated patients from four real-world cHL registries. To consider association over a full range of continuous variables, we evaluated piecewise linear splines for potential nonlinear relationships. Five-year progression-free survival (PFS) and overall survival (OS) were estimated using Cox proportional hazard models. RESULTS The median age in the development cohort was 33 (18-65) years; nodular sclerosis was the most common histology. Kaplan-Meier estimators were 0.77 for 5-year PFS and 0.92 for 5-year OS. Significant predictor variables included age, sex, stage, bulk, absolute lymphocyte count, hemoglobin, and albumin, with slight variation for PFS versus OS. Moreover, age and absolute lymphocyte count yielded nonlinear relationships with outcomes. Optimism-corrected c-statistics in the development model for 5-year PFS and OS were 0.590 and 0.720, respectively. There was good discrimination and calibration in external validation and consistent performance in internal-external validation. Compared with the IPS, there was superior discrimination for OS and enhanced calibration for PFS and OS. CONCLUSION We rigorously developed and externally validated a clinical prediction model in > 5,000 patients with advanced-stage cHL. Furthermore, we identified several novel nonlinear relationships and improved the prediction of patient outcomes. An online calculator was created for individualized point-of-care use.