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Introduction In the KarMMa-3 trial (NCT03651128), ide-cel, a BCMA-directed CAR T cell therapy (Tx), significantly improved median progression-free survival (13.3 vs 4.4 mo, HR 0.49, P < 0.001) and overall response rate (71% vs 42%, P < 0.001) vs std regimens in pts with RRMM who were TCE to immunomodulatory (IMiD ®) agents, proteasome inhibitors (PIs), and daratumumab (Rodríguez-Otero NEJM 2023). Early high-grade (gr) cytopenias and severe infections have been reported post-CAR T cell infusion (Logue Blood 2022, Thibaud Blood 2022), and persistent cytopenias and infections can pose clinical challenges in pt care. In this safety analysis of KarMMa-3, we report frequency, kinetics of occurrence, and management of cytopenias and infections post-Tx with ide-cel or std regimens. Methods This international, randomized, multicenter phase 3 trial enrolled pts with RRMM who received 2-4 prior Txs (an IMiD agent, a PI, and daratumumab) and were refractory to the last Tx. Pts were randomized 2:1 to ide-cel or a std regimen (DPd, DVd, IRd, Kd, or EPd) selected by investigator based on prior anti-MM Tx. In the ide-cel arm, pts could have ≤ 1 cycle of optional bridging Tx (BTx) for disease control. Treated population (pop; ide-cel: pts who received leukapheresis [LA], BTx, lymphodepleting chemotherapy, or ide-cel; std regimens: pts who received any dose of study Tx) was used to assess adverse events (AEs), gr 3/4 AEs, and serious AEs. Safety pop (pts who received ide-cel or any dose of std regimens) was used to assess treatment-emergent AEs, AEs of special interest, investigator-identified neurotoxicity/cytokine release syndrome and others. In pts who had documented disease progression on std regimens and then underwent LA to receive ide-cel, only AEs before LA were included. In this analysis, incidence of cytopenias and infections at various time intervals and management, types of infections, and incidence of hypogammaglobulinemia (HGG) were assessed. Results At data cutoff (Oct 3, 2022), median follow-up was 24.1 (range 5.9−41.0) mo in the 386 randomized pts. In the treated pop, 248/249 (99.6%) vs 124/126 (98.4%) pts had any-gr AEs, 234 (94.0%) vs 96 (76.2%) had gr 3/4, and 36 (14.5%) vs 9 (7.1%) had gr 5 in ide-cel vs std regimens arms, respectively. Of the gr 5 AEs, 18/36 and 3/9 had verbatim terms consistent with progressive disease. In the safety pop, incidence of gr 3/4 cytopenias with ide-cel were higher early after infusion but generally resolved over time (196 [87.1%] pts at < 3 mo, 52 [24.1%] pts at 3−< 6 mo, 30 [14.7%] pts at ≥ 6 mo; Table 1) vs std regimens, which showed lower rate of resolution over time. More pts in the ide-cel (16 [7.1%]) vs std regimens arms (3 [2.4%]) had any-gr febrile neutropenia. Cytopenias were mainly managed using colony-stimulating factor in both arms (55.5% and 58.7%, respectively); RBC (48%) and platelet transfusions (32%) were more common in the ide-cel arm. Overall, incidence of gr 3/4 infections was high at < 3 mo and remained consistent over time in both arms (ide-cel: 17 [7.6%] pts at < 3 mo, 18 [8.3%] pts at 3−< 6 mo, 13 [6.4%] pts at ≥ 6 mo; Table 2). Any-gr bacterial infections were more common in the ide-cel vs std regimens arms with higher incidence at < 3 mo (10% vs 6%) but lowered at later timepoints. Incidence of gr ≥ 3 viral infections remained low and consistent with time; fungal infections were uncommon. Deaths due to infections occurred in 3 (1.3%) vs 1 (0.8%) pts at < 3 mo, 0 pts at 3−< 6 mo, and 7 (3.4%) vs 2 (2.6%) pts at ≥ 6 mo in the ide-cel vs std regimens arms, respectively. Indence of any-gr new HGG was higher in the ide-cel vs std regimens arms but decreased over time: 15 (6.7%) vs 2 (1.6%) pts at < 3 mo, 3 (1.4%) vs 1 (0.9%) at 3−< 6 mo, and 5 (2.5%) vs 0 at ≥ 6 mo, respectively; of these, 13 (5.8%) vs 1 (0.8%), 2 (0.9%) vs 1 (0.9%), and 5 (2.5%) vs 0 pts received IV immunoglobulin replacement Tx at the respective time intervals. Incidence rate (% per 100 pt-years, 95% CI) of second primary malignancies remained low and similar between arms (ide-cel, 4.0 [2.4−6.7]; std regimens, 4.4 [1.9−10.7]). Conclusion In KarMMa-3, most severe cytopenias associated with ide-cel occurred early, with a low incidence of prolonged cytopenias; all were largely manageable. Any-gr bacterial infections decreased over time while viral infections remained the same. No new safety concerns were identified for ide-cel; safety profile was consistent with previous reports. Study support 2seventy bio and Celgene, a Bristol-Myers Squibb Company.

Background: For patients with chronic lymphocytic leukemia (CLL)-the most common form of leukemia in older adults-equitable access to care is particularly challenging, as the disease is often diagnosed in community cancer centers encumbered by regional disparities. Therefore, identifying and understanding regional barriers to care is essential. In 2020, the Association of Community Cancer Centers (ACCC) developed a heat map detailing the incidence rate of CLL diagnoses across the country (Fig.1), overlayed with the locations of CLL “expert” providers, revealing the areas in the US most in need of additional CLL clinical expertise (Fig. 2). In 2022, these incidence rates were stratified by race, ethnicity, and metropolitan statistical area to identify disparities Building on these maps, ACCC set out to identify specific regional challenges to appropriate, equitable care for patients with CLL. Methods: As part of an independent education program, ACCC initiated conversations with providers about the regional barriers that patients with CLL face. Through 4 webinars in February 2023, participants from 4 regions-Northeast (New York and Maine), Northwest (Montana and Idaho), Midwest (Wisconsin, Illinois, and Iowa), and Southwest (Texas)-shared insights on challenges and exchanged ideas on strategies to improve care for patients with CLL. In addition, ACCC interviewed 3 regionally diverse cancer programs (from the US Northwest, South, and Southwest regions) that are effectively overcoming regional challenges to reach their patient populations and provide equitable care. These interviews highlighted barriers and challenges, showcased effective outreach campaigns, and explored how the outreach improved the patient journey. Findings: The following themes emerged as key challenges and potential solutions throughout all regions studied: (1) Comprehensive Diagnostic Work-up Before Treatment Initiation: A key component in caring for patients with CLL is identifying certain disease related criteria prior to formulating a treatment plan. This includes determining the disease burden, disease-related symptoms, and if the patient meets certain active disease criteria (eg, Binet Stage C or Rai Stage III or IV). Further, by utilizing genetic testing, such as the TP53 gene mutation and IGHV mutation tests, physicians can estimate time to treatment and choice of specific novel targeted therapies. (2) Understanding Patient Population Needs: Cancer programs must understand the needs of their unique patient populations to effectively address them. For example, in the Northwest, as mountainous passes can make travel dangerous at times, one local program utilizes near-campus housing/hotel space for patients for the duration of their intensive treatment. In the Southwest, addressing health insurance coverage, low health literacy, language barriers, patient-provider mistrust, and culturally competent care for undocumented individuals presents challenges to equitable care delivery. Therefore, cancer programs in this region have implemented protocols to utilize community and social services to help support these patients. (3) Developing Community Partnerships: As the median age of diagnosis with CLL is approximately 70 years old, barriers such as transportation availability, caregiver support and financial constraints could be magnified. Building trust, particularly among vulnerable communities, also presents a significant challenge. By partnering with community outreach organizations, programs can help address these issues through community resources such as ride share programs, transportation vouchers, and financial assistance programs. (4) Provider Education and Learning: Because many patients are diagnosed in community cancer programs and smaller practices where physicians do not often have experience with CLL, nationally available provider education on new developments in testing and treatments is key. Conclusions: ACCC identified regional challenges and solutions to appropriate and equitable care for patients with CLL, including the importance of comprehensive diagnostic work-up before treating, understanding the regional needs of patient populations (particularly underserved populations), building community partnerships, and engaging in targeted provider education.

Introduction: Sickle cell disease (SCD) is the most common inherited hemoglobinopathy affecting populations across the United States. Several well-established patient-centered comprehensive care models have been in practice for preventive care and pain management. However, access to hematopoietic stem cell transplant (HSCT) and clinical trials for sickle cell gene therapies are limited, especially in pediatric and adult centers with limited HSCT-related resources. We describe a patient-centered collaborative model in the Upper Midwest United States to improve patient education and engagement as well as access to HSCT and gene therapy trials for patients with SCD. Collaborative Care Model: Since August 2021, centers across the Midwest meet monthly to improve access for HSCT and gene therapy as well as education about these therapies for individuals with SCD. The teams include: hematology and transplant teams from University of Minnesota, Comprehensive Sickle Cell Care team from Children's Hospital of Minneapolis, hematology and transplant team from Mayo Children's Hospital, hematology team from Essential Health, Duluth, Minnesota, hematology team from Sanford Children's Hospital Fargo and Sioux Falls and hematology team from Blank Children's Hospital, Des Moines, Iowa. This collaborative approach involves several stakeholders including an SCD community advocacy organization, hematology care providers across several rural as well as community/university hospitals, social workers, nurse coordinators, HSCT/gene therapy providers, as well as representatives from National Marrow Donor Program. Individual patient needs and available community support as well as institutional resources are discussed at length during monthly virtual meetings and a pre-intervention plan is established to ensure smooth transition of care to and from primary centers (Figure 1). Ongoing communication with the primary team helps with efficient transition of care after transplant as patients continue ongoing monitoring and care closer to home. Community partners help with early education of the families and provide necessary resources and support. Institutional partners like NMDP provide support with immune typing and discuss ongoing support and other resources available for providers and families. Results/Impact: Since the implementation of this collaborative model, there has been a steady increase in early referrals for education regarding HSCT and potential gene therapies trials for patients with SCD. There has been about 100% increase in new patient referrals since the implementation of this collaborative model compared to the average new referrals in the previous years (2021-22 vs 2018-19; Figure 2). Enrollment in HSCT as well as gene therapy clinical trials also has increased 2-3 folds since the implementation of the collaborative care model. It has helped established early discussion and longitudinal care and has also led to ongoing evaluation and discussions for availability of optimal donors, determining eligibility for HSCT and gene therapy trials as well as providing ongoing support and education to the sickle cell community and providers regarding the available therapies for SCD. This has also facilitated the transplant team establish earlier and better rapport with patients and their families and provide necessary education with an in-depth discussion regarding the potential benefits and risks of therapies such as HSCT and gene therapy. These discussions often occur over multiple visits and helps address psychological as well social barriers with a prospective evaluation. Additionally, the ongoing discussion among the providers helps with longitudinal monitoring of the patients and provide an assessment of the risks and benefits of therapies as the patient's disease course continues to evolve. This multi-stake holder collaborative approach continues to provide a well-established platform for discussion of newer therapies and access to open clinical trials, available resources for patients and families, and improve patient care and experience. The impact of this collaboration is noted in significant increase in early referrals and educational consults for HSCT and gene therapies, improved patient engagement as well as the acceptability and participation in clinical trials.

Introduction There is no prospective data to guide the utilization of peri-transplant radiation (PTRT) in patients with relapsed or refractory (R/R) classical Hodgkin lymphoma (cHL). In retrospective analyses, PTRT has been associated with local disease control, however evidence supporting improved progression free survival (PFS) is less well defined (Milgrom SA et al Cancer. 2017; 123(8):1363-1371). Furthermore, the added benefit of PTRT in the era of recent advancement in salvage strategies, including brentuximab vedotin (BV) and checkpoint inhibitors (CPI), is unclear. In a multicenter, retrospective analysis, we evaluated the impact of PTRT on PFS for patients with R/R cHL. Methods We identified consecutive pts ≥7 years old at time of diagnosis with R/R cHL treated with autologous stem cell transplant (ASCT) at 5 US academic institutions between 2010-2021. PTRT was defined as radiation administered within 3 months of ASCT. The primary objective was to compare the post-ASCT PFS, defined as the time from ASCT to subsequent relapse or death from any cause, between the two groups. Univariable and Multivariable Cox regression was performed to evaluate the impact of various relevant clinical factors on post-ASCT PFS. Results A total of 958 eligible pts were identified ( Table 1). The median age at cHL diagnosis was 31 (range, 7-72). At the time of first relapse/primary refractory disease, 406 (54%) pts had early-stage disease, 167 (24%) pts had bulky disease (≥5.0 cm), 308 (36%) pts had extra-nodal disease, 182 (22%) pts had B-symptoms (B-sx), and 338 (45%) had primary refractory disease. A total of 302 (32%) pts received >1 line of salvage before ASCT. Salvage treatment at any time point before ASCT included chemotherapy only for 457 (48%) patients, BV(+/- chemotherapy) for 308 (32%) patients, and CPI (+/- BV or +/- chemotherapy) for 193 (20%) patients. In total, 239 (24.9%) pts received PTRT, 139 (58.1%) before ASCT and 100 (41.8%) after ASCT. Among pts with available dosing information (23.3%), the median radiation dose administered was 28 Gy. The most common doses administered were 30 Gy in 77 (31.7%) pts, 36 Gy in 62 (27.8%) pts, and 18 Gy in 43 (19.2%) pts. Radiated sites included mediastinum in 172 (80.7%) pts, neck/cervical lymph nodes in 68 (31.9%) pts, and supra/infra clavicular region and axilla, each in 31 (14.6%) pts. Compared to the 719 pts that did not receive PTRT, patients treated with PTRT were more likely to have early stage disease (73% vs 47%, p<0.001), bulky disease (30% vs 22%, p = 0.048), primary refractory disease (56% vs 40%, p <0.001), >1 line of salvage treatment prior to ASCT (40% vs 29%, p<0.001), and <CR prior to ASCT (36% vs 27%, p = 0.007) (Table 1). Patients treated with PTRT were less likely to have RT at initial diagnosis (5.9% vs 22%, p<0.001), extra-nodal disease (19% vs 42%, p<0.001), B-sx (17% vs 25%, p=0.035), salvage treatment that included CPI (11% vs 23%, p<0.001), and BV maintenance (20 vs 31%, p<0.001). After a median follow up from ASCT of 4.5 years (range, 2.4-7.2), 2-year PFS was 76% (73%, 79%) and 5-year PFS was 70% (67%, 73%). In univariable analysis, receipt of PTRT showed non-significant reduced hazard for PFS (HR 0.79, 95% CI 0.59-1.05, p = 0.093) (Figure 1). In a multivariable model, PTRT showed significant association with PFS (HR 0.56, 95% CI 0.36-0.88, p=0.009) when adjusting for other clinically relevant factors at relapse such as stage, B-sx, bulk, primary refractory disease, BV(+/- chemotherapy) or CPI (+/- BV or +/- chemotherapy) in salvage, PET response prior to ASCT, and BV maintenance after ASCT. To determine if specific subgroups benefited from PTRT, we tested for interaction effects between PTRT and clinically relevant factors. Only bulk (HR 0.37, 95% CI 0.16-0.85, p=0.013) and B-sx (HR 0.45, 95% CI 0.20-1.02, p=0.041) showed significant interaction with PTRT.Among pts with bulky disease, 5-year PFS was significantly improved with receipt of PTRT (81% vs 62%, p=0.013). Conclusion In this large multi-center analysis of pts with R/R cHL undergoing ASCT, there was no significant difference in PFS regardless of receipt of PTRT. However, after adjusting for known clinically relevant factors, receipt of PTRT was significantly associated with PFS and was particularly beneficial for patients with bulky disease or B-sx at relapse. We found decreased utilization of PTRT in pts receiving CPI-based salvage; therefore the role for PTRT for pts receiving CPI-based salvage is unclear.

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.

Scoring systems designed to improve the accuracy of prognostication in chronic lymphocytic leukemia (CLL) usually rely on discretized/dichotomic values of the clinical and biological variables. Here we analyzed the immunophenotypic and (immuno)genetic profiles of 2,243 CLL patients with Rai stage 0-I-II, by applying unsupervised machine learning methods, elaborating prognostic factors as continuous variables, to identify novel relationships and interactions likely missed in conventional hierarchical models. The study included an internal training cohort (n=863) and two external validation cohorts provided from Cardiff University/Brighton Medical School (n=455) and the Mayo Clinic (Rochester, MN; n=925). Primary endpoint was Time to First Treatment (TTFT). Laboratory-based markers evaluated in the training cohort were: surface antigen expression by flow cytometry (as % of positive cells) of CD20, FMC7, CD49d, CD49c, CD38, CD23, CD43, CD22, ZAP-70; chromosomal aberrations (% of nuclei with abnormal signal) del13q, tris12, del11q and del17p,; mutational status of TP53 by NGS as % VAF; IGHV mutational status as % mutation. 420/863 cases received treatment (median 46 months). Univariate Cox regression identified FISH del11q, del17p and tris12, TP53 and IGHV mutations, CD38 and CD49d expression, as features associated with TTFT (p-value <0.001). An unsupervised k-means algorithm partitioned cases into 6 clusters (C1-C6), as selected by the elbow method, and centroid analysis evaluated which feature most contributed to each cluster (Panel A): C1 (n=275) was heavily IGHV-mutated (mutation range 4.5-22.0%) with low representation of all other features; C2 (n=208) was mostly IGHV-unmutated/intermediate (up to 4.5%) in the absence of other features; C3 (n=169) were CD49d-expressing cases (>50% expression), with low representation of other features, except CD38; C4 (n=127) were cases with trisomy 12 (>28%), also expressing CD49d and CD38; C5 (n=48) were cases with highly clonal del11q (44-98%) and IGHV-unmutated, mutually exclusive with TP53 disruption; C6 (n=34) contained TP53-disrupted cases with high mutation burden (VAF 36-97%) and/or del17p (range 40-96%), with low importance of all the other features. Of note, alternative use of either TP53 mutation or del17p as defining features of cluster 6 did not result in changes of the classification accuracy. Clinically, clusters 3-4-5-6 showed median TTFT of 26, 22, 9, 5 months respectively whereas median TTFT was not reached for clusters 1-2. Hierarchical agglomerative clustering aggregated the 6 clusters in 3 major risk tiers: high (C5-6), intermediate (C2-3-4) and low (C1), with median TTFT of 7, 45 and not reached, respectively (Panel B). Multivariate Cox analysis (MVA) with Rai staging demonstrated that the 3-tier risk score contributed significantly and independently to TTFT estimate (p<0.0001; Harrel's c-index=0.71). To classify novel patients, we designed a hierarchical algorithm, based on each clusters' estimated risk and according to calculated cut-off, i.e. C6, del17p/ TP53>36%; C5, del11q>43%; C4, tris12>28%; C3, CD49d>50%; C2, IGHV≤4.5%; C1, the remaining patients with IGHV>4.5%. This approach was highly robust by an internal training-validation bootstrap (cluster classification accuracy = 0.96±0.02). External samples from two independent cohorts were classified into the six clusters using the cut-offs defined by the hierarchical classifier, and then aggregated into the 3-risk tiers. The classifier performed robustly in both cohorts, again adding independent prognostic information to Rai stage in MVA, with p<0.0001 and c-index of 0.76 for Cardiff/Brighton cohort and p<0.0001 and c-index of 0.74 for Mayo cohort. In conclusion, our machine-learning-driven, laboratory-based classification identifies clusters of patients who at higher risk of requiring early treatment, independent of clinical staging, and may help to better identify patients who may benefit from early treatment or more frequent disease surveillance. For some features, TP53 or IGHV mutational status, our unsupervised approach selected cut-offs not recapitulating the canonical ones, envisioning different biological activities in the TTFT setting. For example, TP53 mutations were clinically relevant only if present in the vast majority of the clone , suggesting the involvement of specific activities of the TP53 mutant in this context.

Introduction: DH/THL also classified as high-grade B cell lymphomas (HGBCL) with MYC and BCL2 and/or BCL6 rearrangements have higher risk of progression after initial treatment and poor response to salvage therapies compared to diffuse large B cell lymphoma (DLBCL), NOS. Recently, CART cell therapy has shown encouraging responses in treatment of relapsed refractory (R/R) DLBCL. In this retrospective study, we evaluated whether survival of (R/R) DH/THL is improved in the CART era. Methods: In this multicenter retrospective study, adult pts (pts) with R/R DH/THL after anthracycline-based therapy were included. Demographic and clinical variables were obtained from electronic health records. Study objectives was overall survival (OS) at 2 year landmark. We utilized full propensity score matching to compare progression free survival (PFS) and OS of CART with 2 nd line chemoimmunotherapy. Results: 278 DH/THL pts treated between 2010-2020 were included. Median age was 63 years (range 52-74), 163 (58%) were male, 119 (43%) were classified as DLBCL and 116 (42%) as HGBCL; 40 (14%) were transformed from follicular lymphoma. 225 (82%) had advanced stage, 161 (67%) had elevated LDH, 85 (33%) had bulky disease, 74 (59%) had B symptoms. 105 (38%) pts treated for relapsed disease before 2018 were included in the pre-CART era cohort. 168 (60%) pts treated from 2018 onwards were included in the CART era cohort. 102 (60%) pts in CART cell era received CART cells, 40 (14%) received polatuzumab vedotin (pola), 10 (3.5%) received loncastuximab tesirine, tafasitamab and bispecific antibody each. Characteristics at relapse were similar in both groups. Median follow up was 8.9 (0.2-146) months. 2-year OS estimate was 36% (CI 95: 30-42%) in entire population. Patients treated in CART cell era had significantly higher OS (2 yr OS: 43% [CI 95: 35-53]) compared to pts treated in pre-CART cells era (2-year OS: 24% [CI 95: 16-34], p=0.0006) (Figure 1). In multivariable analyses, increasing age (HR = 1.02 (CI 95: 1.01, 1.04), p=0.003), extranodal disease (HR = 1.41 (CI 95: 1.00, 1.99), p<0.001), elevated LDH (HR = 1.57 (CI 95: 1.11, 2.24), p=0.01) were associated with inferior OS. Increasing time to relapse (HR = 0.98 (CI 95: 0.97, 0.99), p<0.001) was associated with superior OS. BCL6 rearrangement did not affect OS. When adjusted for these factors, treatment with CART cells was associated with higher 2 yr OS (HR = 0.33 (CI 95: 0.22, 0.49), p<0.001). Treatment with polatuzumab (HR = 1.07 (CI 95: 0.71, 1.6), p=0.07) was not significantly associated with OS in univariable analysis. 102 pts underwent CART cell therapy at any point for relapse; 94 (93%) were refractory to prior chemotherapy. 62 (60%) responded and 52 (50%) had CR after CART cells. 2-year PFS and OS after CART cell therapy were 28% (CI 95: 19-40) and 40% (CI 95: 30-53), respectively. Increasing time to CART was associated with inferior OS (HR: 1.02 (CI 95: 1.0-1.03, p=0.03) but not PFS (HR: 1.02 (CI 95: 0.99-1.04), p=0.2). 219 (79%) pts received curative intent salvage chemotherapy for 2 nd line. 2-year PFS and OS were 15% (CI 95: 11-20) and 38% (CI 95: 31-45), respectively. 21 pts received CART cell therapy for 2 nd line. 2 year PFS and OS in this small number of pts were 52% (CI 95: 35-79) and 70% (CI 95: 52-94), respectively. Due in part to the small number of pts who received CART cells in 2 nd line, we utilized full propensity score matched analysis to compare each patient in CART cell group to all possible matched controls in salvage chemotherapy group. Variables matched were age, LDH, extra nodal disease and time to relapse. In propensity score matched analysis, pts who received CART cells had significantly higher 2 year PFS (HR: 10.5 (CI 95: 3.4-17.5), p=002) and 2 year OS (HR: 7.5 (CI 95: 1.4-13), p=0.01). Conclusions: Survival of r/r DH/THL appears to have improved in the era of CART cell therapy. About 2/3 of pts refractory to chemotherapy respond to CART cells, and in multivariable analysis, receipt of CART cells was associated with improved OS. Increasing time to CART cells was associated with inferior survival. In a propensity score matched analysis utilizing full matching, CART cell therapy in 2 nd line was associated with significantly higher 2 year PFS and OS compared to curative intent salvage therapy. Other CD 19 and CD 20 targeting agents approved after CART cell therapy might contribute to favorable survival, effect of these agents could not be assessed because of small numbers.

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.

Introduction A single ide-cel infusion showed significantly longer median progression-free survival (PFS) vs std regimens (13.3 vs 4.4 months [mo], HR 0.49, 95% CI 0.38-0.65, P < 0.001) with deep, durable responses in heavily pretreated TCE RRMM at an interim analysis (IA) of KarMMa-3 (NCT03651128); safety data were consistent with prior studies (Rodríguez-Otero et al. NEJM 2023). Ide-cel benefits were consistent across pts in high-risk subgroups and across 2-4 prior lines of therapy (Tx). Results of the preplanned final PFS analysis of KarMMa-3 with 12.3 mo additional follow-up are reported. Methods In the phase 3 KarMMa-3 trial, pts with RRMM who received 2-4 prior regimens, including an immunomodulatory agent, proteasome inhibitor, and daratumumab, and were refractory to last regimen were randomized 2:1 to ide-cel or a std regimen (DPd, DVd, IRd, Kd, or EPd). In the ide-cel arm, pts could receive ≤ 1 cycle of optional bridging Tx for disease control. Pts in the std regimens arm could receive ide-cel after confirmed disease progression (PD). The primary endpoint was IRC-assessed PFS in the ITT population; final PFS was planned to be analyzed with ~289 events. Key secondary endpoints were IRC-assessed overall response rate (ORR) and overall survival (OS); other secondary endpoints included complete response rate (CRR), duration of response (DOR), minimal residual disease (MRD) status, time to next anti-myeloma Tx (TTNT; time from randomization to next anti-myeloma Tx [MTx]), event-free survival (EFS; time from randomization to first PD, next MTx or any-cause death, whichever is first), PFS2 (time from randomization to second objective PD or any-cause death, whichever is first), safety, and health-related quality of life (QOL). Results Of 386 randomized pts (ide-cel, n = 254; std regimens, n = 132), 225 received ide-cel and 126 received a std regimen. Baseline characteristics were generally balanced. Median follow-up from randomization to data cutoff (April 28, 2023) was 30.9 mo (range 12.7-47.8). Ide-cel significantly improved median PFS (95% CI) vs std regimens (13.8 [11.8-16.1] vs 4.4 [3.4-5.8] mo), representing a 51% reduced risk of PD or death (HR 0.49, 95% CI 0.38-0.63; Figure); 18 mo PFS rates were 41% vs 19%, respectively. Ide-cel significantly improved ORR vs std regimens (71% vs 42%) with deeper (CRR 44% vs 5%; ≥CR and MRD negative status [sensitivity level 10 -5], 22% vs 1%), more durable responses (median DOR 16.6 vs 9.7 mo; Table). PFS and ORR benefits of ide-cel vs std regimens were consistent with the IA. Interim OS will be included in the presentation. In pts who received ide-cel (n = 225) or a std regimen (n = 126), median PFS (95% CI) was 15.7 (12.5-18.9) vs 4.4 (3.4-5.8) mo, respectively. In the ITT population, median TTNT, EFS, and PFS2 were numerically longer with ide-cel vs std regimens. Median (range) TTNT was 20.9 (16.6-24.2) vs 7.0 (5.3-8.5) mo. Median (95% CI) EFS was 13.3 (11.3-15.7) vs 3.9 (3.0-5.3) mo. Median PFS2 (95% CI) was 23.5 (18.4-27.9) vs 16.7 (12.2-20.3) mo; ide-cel was next MTx in 70 (53%) pts in the std regimens arm. In the treated population, grade (gr) 3/4 infections occurred in 66/249 (27%) pts in the ide-cel arm vs 25/126 (20%) in the std regimens arm. In the ide-cel safety population, any gr cytokine release syndrome occurred in 197/225 (88%) pts, gr ≥ 3 in 11 (5%); median time to first onset was 1 d (1-14), median duration was 4 d (1-51). Any gr investigator-identified neurotoxicity occurred in 34/225 (15%) pts, gr ≥ 3 in 7 (3%); median time to onset was 3 d (range 1-317); median duration was 2.5 d (range 1-252). Ide-cel continued to demonstrate durable, clinically meaningful improvements in pt-reported outcomes, including symptoms, functioning, and QOL vs std regimens. Conclusions In this final PFS analysis of KarMMa-3, significantly longer PFS was maintained with ide-cel; PD or death risk reduced by 51%; responses were deeper and more durable vs std regimens. CRR with ide-cel increased since the IA, indicating a deepening response, but were unchanged with std regimens. A single ide-cel infusion vs continuous treatment with std regimens resulted in longer median TTNT and PFS2, indicating improved long-term disease control. The ide-cel safety profile was consistent with previous reports, with no parkinsonism or Guillain-Barré syndrome reported. These data continue to support use of ide-cel in pts with TCE RRMM. Study support 2seventy bio and Celgene, a Bristol-Myers Squibb Company.

Abstract Background: DS-8201a is a HER2-targeting antibody drug conjugate with a topoisomerase 1 (top1) inhibitor payload. Preclinical evidence suggests that inhibiting PARP can potentiate top1 inhibitor-induced DNA damage and cytotoxicity. However, the clinical activity of combined top1-inhibiting chemotherapy and PARP inhibition has been limited by toxicity. We hypothesized that targeted delivery of a top1 inhibitor via DS-8201a may reduce systemic toxicity while maintaining synergistic cytotoxic activity in combination with olaparib. This combination is being investigated in the ongoing phase 1 trial CTEP #10355 (NCT04585958) in patients (pts) with advanced solid tumors expressing HER2. We present the safety analysis of pts enrolled to Module 1 (Mod 1) of dose escalation (continuous olaparib). Preliminary efficacy results are explored. Methods: Accrual was limited to pts ≥18 years old with advanced or recurrent solid tumors expressing HER2, defined as HER2 IHC ≥1+ (local testing allowed) or amplification of ERBB2 by NGS or ISH. In Mod 1, pts received DS-8201a IV once every 3 weeks and olaparib twice daily (BID) continuously in a 3+3 dose escalation design. Dose Level 1 (DL1) of Mod 1 evaluated DS-8201a dosed at 4.4mg/kg with olaparib 100mg BID Days 1-21. Dose Level 2 (DL2) of Mod 1 evaluated DS-8201a dosed at 5.4mg/kg with olaparib 100mg BID Days 1-21. The primary objective was to evaluate the safety and tolerability of combined DS-8201a + olaparib as graded by CTCAE v5. Secondary objectives include assessment of clinical activity as measured by objective response rate (ORR), clinical benefit rate (CBR; partial response [PR] + complete response [CR] + stable disease [SD] ≥6 months [mo]), median progression-free survival (PFS), and duration of response (DOR). Results: As of 30-Nov-2022, 10 pts were treated in Mod 1, with median age of 62.5 years. 10% were Asian, 10% were African American, and 80% were white. 40% had endometrial cancer and 60% had ovarian cancer. 6 of 7 pts treated on DL1 were DLT-evaluable, of which 1 patient experienced a DLT of Grade (G) 4 neutropenia. All 3 pts in DL2 were DLT-evaluable, and each experienced a DLT (2 pts with G4 febrile neutropenia, 1 pt with G4 neutropenia). Most common TRAEs in Mod 1 included: ANC decrease (60% G4, 20% G3, 20% G1/2), platelet count decrease (20% G4, 20% G3, 40% G1/2), and anemia (70% G3, 20% G1/2). All Mod 1 pts required dose holds, dose reductions, and/or discontinuation of olaparib. Of the 10 pts in Mod 1, 40% achieved a RECIST response (1 CR, 3 PR). 40% had SD lasting ≥6 mo, for a CBR of 80%. Median PFS is estimated at 7.4 mo (95% CI 4.7mo-not reached), with 7 patients alive and progression-free at 6 mo. Conclusion: Continuous dosing of olaparib in combination with DS-8201a yielded an unacceptable safety profile and dose escalation in Mod 1 was not continued. An alternative dosing schedule was initiated in Module 2 (Mod 2) evaluating DS-8201a IV every 3 weeks and olaparib BID Days 8-14; accrual to Mod 2 is ongoing. There is preliminary evidence of clinical activity of this combination. Citation Format: Elizabeth K. Lee, Andrea E. Wahner Hendrickson, Gerardo Colon-Otero, Cheryl A. Pickett, Niya Xiong, Su-Chun Cheng, Madeline Polak, Hannah Sawyer, Martin Hayes, Ursula A. Matulonis, Geoffrey I. Shapiro, Panagiotis A. Konstantinopoulos. A phase 1 study of DS-8201a in combination with olaparib in HER2-expressing malignancies (CTEP #10355): Results of module 1 dose escalation [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr B041.