Federico, Massimo

Abstract Background: Despite recent therapeutic advances in AS cHL, PET-adaptive chemotherapy-based regimens remain a first-line treatment option, especially for patients (pts) without access to novel agents. Most PET-adaptive regiments start with either 2 cycles of escBEACOPP (escBEACOPP2) or ABVD (ABVD2) prior to interim PET assessment (iPET), followed by treatment de-escalation or intensification by iPET result. There have been no direct comparisons of escBEACOPP2 vs ABVD2 PET-adaptive regimens. Additionally, the AS-Hodgkin Lymphoma International Prognostication Index (A-HIPI) score (Rodday JCO 2023) has not been fully explored in escBEACOPP2 regimens. Therefore, we utilized multiple analytic approaches to compare outcomes of PET-adaptive escBEACOPP2 and ABVD2 regimens, while adjusting for and assessing the effect of the baseline A-HIPI score. Methods: Through the global HoLISTIC Consortium (www.hodgkinconsortium.com), we obtained individual patient data from 4 AS clinical trials of newly diagnosed cHL pts treated with PET-adaptive escBEACOPP2 (AHL2011) or ABVD2 (SWOG0816, RATHL, HD0607) regimens. Pts were restricted to age 18 to 65 years (y), stage IIB-IV, and PET-adapted treatment arms. Positive iPET was based on Deauville score >3. Treatment effects of escBEACOPP2 vs ABVD2 (reference) on 5y progression-free survival (PFS) and overall survival (OS) were first evaluated using Cox models. Importantly, baseline disease risk was adjusted for using the 5y PFS or OS A-HIPI score (including stage, sex, age, bulk, lymphocyte count, albumin, white blood cell count), where higher scores indicate higher predicted risk (scale 1-100). The A-HIPI was modeled per 1 standard deviation (SD) increase. Our multistate model (MSM) comprised 4 health states: diagnosis, sustained remission at 1yr, treatment failure, and death from any cause. Pts all started in the diagnosis state and transitioned to other states without return to prior states. We assessed the impact of treatment on transitions (except binary sustained remission at 1y) that involved ≥5 pts per treatment group, adjusting for A-HIPI for all transitions. All effects are reported as adjusted hazard ratios (aHRs) with 95% confidence intervals (CI). Results: 2,381 AS cHL pts were included (n=372 for escBEACOPP2, n=2009 for ABVD2). For escBEACOPP2 and ABVD2 groups (median follow-up 60 and 56 months), respectively, the mean baseline A-HIPI scores (i.e., predicted 5y PFS event rate) were 25.1 (SD=6.3) and 22.9 (SD=6.5), the rates of positive iPET were 11.3% and 17.0%, 5y PFS was 87.0% and 79.5%, and 5y OS was 96.3% and 94.6%. In Cox models, escBEACOPP2 was associated with significantly improved PFS compared to ABVD2 (aHR=0.54, 95% CI=0.39-0.73); the aHR for OS was 0.67 (95% CI=0.37-1.19). Notably, higher risk A-HIPI score was associated with significantly worse PFS (aHR=1.42, 95% CI=1.31-1.54) and OS (aHR=1.60, 95% CI=1.47-1.76), independent of treatment. In the MSM, the effect of escBEACOPP2 compared to ABVD2 regimens was modeled in 3 transitions with adjustment for A-HIPI score: those treated with escBEACOPP2 had lower likelihood of treatment failure with (aHR=0.38, 95% CI=0.22-0.66) or without (aHR=0.61, 95% CI=0.40-0.92) sustained remission at 1y; the aHR for treatment failure to death was 0.55 (95% CI=0.25-1.22). Higher risk baseline A-HIPI score was associated with higher likelihood of treatment failure and death without sustained remission at 1y (aHR=1.43, 95% CI=1.28-1.61 and aHR=2.50, 95% CI=1.80-3.49, respectively). Furthermore, higher risk baseline A-HIPI score was associated with higher likelihood of treatment failure after achieving sustained remission at 1y (aHR=1.29, 95% CI=1.12-1.48) as well as death after treatment failure (aHR=1.54, 95% CI=1.27-1.86). Conclusions: Comparing newly diagnosed adult AS cHL PET-adaptive regimens with adjustment for baseline A-HIPI score, we found pts treated with escBEACOPP2 on AHL2011 had improved PFS versus pooled pt data from 3 ABVD2 trials. Using MSM, we further demonstrated that escBEACOPP2 was associated with a lower likelihood of treatment failure with or without achieving sustained remission at 1yr. In addition, baseline A-HIPI score was associated with PFS and OS, which was independent of treatment. Finally, higher baseline A-HIPI scores maintained prognostic impact throughout the disease course, including increased risk of treatment failure for pts in remission at 1yr and death after treatment failure.

SummaryProgression‐free survival (PFS) has been the regulatory primary end‐point for recent phase III trials in first‐line follicular lymphoma (FL), but requires prolonged follow‐up. Complete response (CR) at 30 months after initiation of induction treatment was validated as surrogate end‐point for PFS. Our objective was to further evaluate surrogacy of CR measured by [18F] fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging at the end of induction (EoI). Individual patient data were analysed from 1505 patients from five randomized trials. Trial‐level surrogacy examining the association between treatment effects on EoI‐PET‐CR and PFS was evaluated using linear regression () and bivariate Copula () models. Although EoI‐PET‐CR strongly predicted PFS at a prognostic level, the trial‐level assessment did not show strong correlation (, confidence interval [CI]: 0.20–0.88; , CI: 0.0–0.82). The high uncertainty in estimation was possibly due to the small number of trials and the population of patients with available PET data. Maintenance therapy affecting PFS beyond induction treatment, but not EoI‐PET‐CR end‐point, may have distorted the association between treatment effects. However, there will probably be a number of additional trials approaching completion with available PET response data. Refined evaluation of PET‐CR based surrogate end‐points is still warranted.

Background: While HL is a highly curable cancer, patients (pts) with advanced stage disease experience increased risk of relapse. Delineation of prognosis is desired to compare cohorts and outcomes between trials, and to define groups of pts for whom reduction in treatment may be appropriate or where novel therapeutic approaches are needed. The International Prognostic Score (IPS), which was derived from a discovery set of 1,618 HL pts with complete data, was a seminal publication in the field (Hasenclever and Diehl NEJM 1998). However, these data were published >20 years ago with a significant minority of pts having received chemotherapy regimens no longer in clinical use. More contemporary analyses have shown altered utility of the IPS (e.g., Moccia JCO 2012; Diefenbach BJH 2015). In addition, prior studies identified bulk disease as an adverse prognostic factor in advanced stage HL (Laskar JCO 2004; Johnson JCO 2010). Our objective was to leverage individual pt data (IPD) from HoLISTIC (www.hodgkinconsortium.com) to discover a new, robust, and modern prognostication index for advanced-stage HL pts applicable to diverse settings across the world. Methods: We created a data repository of IPD from clinical trials for newly diagnosed HL pts, which includes 4,085 advanced-stage (III or IV) pts treated in 8 large, prospective studies completed in the modern era (ie, IIL HD9601: Gobbi JCO 2005; Italian HD2000: Federico JCO 2009; ECOG 2496: Gordon JCO 2013; SWOG 0816: Press JCO 2016; IIL HD0801: Zinzani JCO 2016; RATHL: Johnson NEJM 2016; GITIL HD0607: Gallamini JCO 2017; and COG AHOD 0831: Kelly BJH 2019) as well as prominent cancer registries (eg, the Mayo/Iowa Molecular Epidemiologic Resource (MER)). The discovery analysis herein included pts from the ECOG 2496, HD0801 IIL, GITIL HD0607, and SWOG 0816 studies. Furthermore, it was restricted to pts (n=1,279) on these trials with complete data for all 9 covariates of interest: age; sex; advanced stage (III vs IV); B symptoms; any bulk; and values of hemoglobin, white blood count (WBC), lymphocyte count, and albumin. Using Cox proportional hazard (PH) models, we evaluated univariate associations between 5-year progression-free survival (PFS) and overall survival (OS) with the aforementioned prognostic variables. Age was categorized based on plots and optimum model fit (c statistic). Lab values were dichotomized using cut-points from the 1998 IPS. Per convention, treatment factors were not included in the model. To identify independent prognostic factors of PFS and OS, a parsimonious Cox PH model was fit using backward selection of all potential risk factors (P<0.05). Hazard ratios (HR) with 95% confidence interval (CI) were reported. Kaplan Meier (KM) plots were also reported for risk factors in the multivariable (MVA) models to visualize differences. Results: Among all pts, characteristics included: median age of 32.9 years (IQR 25.4-44, range 15-83); 55% male; 49% stage IV; 63% B symptoms; 26% bulk >10 cm; 20% hemoglobin <10.5 g/dL; 15% WBC count ≥15,000/mm3; 9% lymphocyte count <600/mm3; and 63% with albumin <4g/dL. For analysis of age, we observed a U-shaped relationship with PFS (Fig A), which helped delineate optimal cut points of 15-24 years (23.1%), 25-49 years (61.4%), and ≥50 years (15.6%). In univariate analysis: age, stage, B symptoms, bulk, anemia, low lymphocyte count, and low albumin were associated with worse survival. In the MVA model, age >50 years, stage IV disease, B symptoms, and bulky disease were associated with worse PFS; and age >50 years, stage IV disease, bulky disease, anemia, and low albumin were associated with worse OS (Fig B). KM plots for age, stage, and bulk are presented in Fig C. Conclusions. In this international, multi-study analysis of advanced stage HL in the modern era, we identified several factors that were associated with both worse PFS and OS on MVA (ie, age, stage IV disease, and bulky disease). The finding of bulky disease as a significant prognostic factor warrants further investigation. In addition, we detected an age-related U-shaped impact on PFS with inferior outcomes for pts ages 15-25 years and ≥50 years, the latter in an increasing linear fashion. Altogether, these data will serve as a training cohort for a modern HL prognostication index that will be augmented and analyzed with a large independent validation cohort (vis-à-vis the remaining HL data in the HoLISTIC consortium), which will be presented at the ASH meeting. Disclosures Parsons: Seattle Genetics: Consultancy. Advani:Astra Zeneca, Bayer Healthcare Pharmaceuticals, Cell Medica, Celgene, Genentech/Roche, Gilead, KitePharma, Kyowa, Portola Pharmaceuticals, Sanofi, Seattle Genetics, Takeda: Consultancy; Celgene, Forty Seven, Inc., Genentech/Roche, Janssen Pharmaceutical, Kura, Merck, Millenium, Pharmacyclics, Regeneron, Seattle Genetics: Research Funding. Federico:Spectrum: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sandoz: Consultancy, Membership on an entity's Board of Directors or advisory committees; Mundipharma s.r.l.: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Millennium/Takeda: Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Cephalon/Teva: Research Funding. Friedberg:Acerta Pharma - A member of the AstraZeneca Group, Bayer HealthCare Pharmaceuticals.: Other; Bayer: Consultancy; Kite Pharmaceuticals: Research Funding; Portola Pharmaceuticals: Consultancy; Roche: Other: Travel expenses; Seattle Genetics: Research Funding; Astellas: Consultancy. Hutchings:Genmab: Research Funding; Janssen: Research Funding; Roche: Consultancy; Genmab: Consultancy; Takeda: Consultancy; Roche: Research Funding; Celgene: Research Funding; Daiichi: Research Funding; Sankyo: Research Funding; Novartis: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; Roche: Honoraria; Genmab: Honoraria; Takeda: Honoraria. Johnson:MorphoSys: Honoraria; Kymera: Honoraria; Kite Pharma: Honoraria; Incyte: Honoraria; Celgene: Honoraria; Epizyme: Consultancy, Research Funding; Novartis: Honoraria; Takeda: Honoraria; Oncimmune: Consultancy; Boehringer Ingelheim: Consultancy; Janssen: Consultancy; Oncimmune: Consultancy; Janssen: Consultancy; Genmab: Honoraria; Bristol-Myers: Honoraria; Epizyme: Consultancy, Research Funding. Radford:Novartis: Consultancy, Honoraria; BMS: Consultancy, Honoraria, Speakers Bureau; ADCT: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Research Funding; Seattle Genetics: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; GlaxoSmithKline: Current equity holder in publicly-traded company, Other: Spouse; AstraZeneca: Current equity holder in publicly-traded company, Other: Spouse; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Zinzani:MSD: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Incyte: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Sanofi: Consultancy, Membership on an entity's Board of Directors or advisory committees; EUSA Pharma: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AbbVie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Gilead: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sandoz: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Immune Design: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Eusapharma: Consultancy, Speakers Bureau; Verastem: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Kyowa Kirin: Consultancy, Speakers Bureau; TG Therapeutics, Inc.: Honoraria, Speakers Bureau; Kirin Kyowa: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Servier: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen-Cilag: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celltrion: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; ADC Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Portola: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Immune Design: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Merck: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Cerhan:NanoString: Research Funding; BMS/Celgene: Research Funding. Evens:Abbvie: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria, Research Funding; MorphoSys: Consultancy, Honoraria; Research To Practice: Honoraria, Speakers Bureau; Novartis: Consultancy, Honoraria; Epizyme: Consultancy, Honoraria, Research Funding; Merck: Consultancy, Honoraria, Research Funding; Pharmacyclics: Consultancy, Honoraria; Mylteni: Consultancy, Honoraria.

Abstract Background: Over the last two decades several phenotypic, molecular, and chromosomal markers have been identified that are significantly associated with the prognosis of CLL patients. Therefore, clinicians managing CLL patients would benefit from a simplified prognostic index. Methods: We analyzed prospectively collected data from 337 Binet A CLL patients enrolled in Italian the O-CLL1-GISL protocol with the aim of developing scores capable of predicting treatment free survival (TFS). Factors independently associated with TFS were included in the prognostic indexes. To account for differences in the magnitude of the association between the individual independent factors and TFS, we assigned a weighted risk score to each factor based on ranges of their corresponding hazard ratios (HRs) (i.e., 1 point for HR 1.1-1.9; 2 points for HR 2.0-2.9, etc.). The total risk score was then calculated by the sum of the ratings of each factor on individual basis. Risk groups were identified combining risk categories with a non-statistically different TFS. Results: We developed two scores based on weighted multivariable models: the first included clinical and laboratory parameters [clinical score (c-score)], while the second was based on biological markers [biological score (b-score)] (Table 1). The c-score allowed to predict the TFS of patients through the combination of Rai stage, b2-microglobulin and absolute lymphocyte count (ALC), while the b-score predicted TFS by IGHV mutational status and CD38 expression. The c-score showed a C-statistic of 0.72, while the b-score was 0.67, although cases stratified according to the b-score showed a more specific mRNA/microRNA profile. When the two scores were forced in a multivariate analysis, both showed an independent predictive value on TFS with a similar HR, demonstrating their complementarity. Thus, we attempted to integrate the two scores performing a further multivariate analysis in which all parameters, significantly associated with TFS at univariate analysis, were tested (Table 1). ALC, Rai stage, b2-microglobulin together with IGHV mutational status, resulted independently associated with TFS. We constructed a weighted score [comprehensive score (co-score)], including all the above 4 variables, which allowed the identification of 3 different risk groups with significantly different TFS (Figure 1). The C-statistic of the g-score was 0.75, showing a better concordance than the other two scores. Moreover, its validity was externally validated in a series of 297 newly diagnosed Binet A CLL patients from the Mayo Clinic, USA. Conclusions: Using this multistep process and external validation, we developed a score with high discriminatory power and predictive significance on the individual patient level. Table 1. Univariate and multivariate Cox proportional Hazards Models Variable Univariate analysis Multivariate analysis Clinical model Biological model Comprehensive model HR (95% CI) P HR (95% CI) P score HR (95% CI) P score HR (95% CI) P score Age (years) <60/>60 1.12 (0.73-1.74) 0.59 - - - - - - - - - Sex Male/Female 0.93 (0.6-1.44) 0.93 - - - - - - - - - Rai stage 0/I-II 2.30 (1.47-3.50) <0.0001 2.13 (1.24-3.03) 0.004 0/2 - - - 1.76 (1.11-2.78) 0.015 0/1 ALC (109/L) <10/>10 3.43 (1.99-5.92) <0.0001 2.91 (1.85-5.20) <0.0001 0/2 - - - 2.70 (1.54-4.72) 0.001 0/2 b-2 microglobulin normal/elevated 3.04 (1.96-4.70) <0.0001 2.78 (1.79-4.30) <0.0001 0/2 - - - 2.65 (1.66-4.21) <0.0001 0/2 LDH normal/elevated 1.25 (0.57-2.71) 0.57 - - - - - - - - - CD38 negative/positive 3.22 (2.06-5.02) <0.0001 - - - 2.11 (1.15-3.16) 0.02 0/2 1.40 (0.80-2.42) 0.24 - ZAP-70 negative/positive 2.34 (1.51-3.61) <0.0001 - - - 1.21 (0.70-2.16) 0.485 - 1.0 (0.98-1.01) 0.72 - IGHV mutated/unmutated 3.57 (2.32-5.50) <0.0001 - - - 2.10 (1.12-3.90) 0.019 0/2 2.39 (1.27-4.50) 0.007 0/2 FISH risk low+int/high 2.93 (1.46-5.90) 0.002 - - - 1.65 (0.76-3.34) 0.216 - 1.80 (0.84-3.88) 0.13 - Abbreviations: ALC: absolute lymphocyte count; CI: confidential interval; HR: hazard ratio. Figure 1. TFS according to comprehensive progression risk score. Figure 1. TFS according to comprehensive progression risk score. Disclosures Shanafelt: Cephalon: Research Funding; Glaxo-Smith-Kline: Research Funding; Genentech: Research Funding; Hospira: Research Funding; Celgene: Research Funding; Jannsen: Research Funding; Polyphenon E International: Research Funding; Pharmacyclics: Research Funding. Kay:Genentech: Research Funding; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; Hospira: Research Funding; Tolero Pharma: Research Funding; Pharmacyclics: Research Funding.