New comorbidity index associated with survival after chimeric antigen receptor T-cell therapy for large B-cell lymphoma Open Access

Chimeric antigen receptor T-cell (CAR-T) therapy has emerged as a standard-of-care treatment for patients with relapsed or refractory large B-cell lymphoma (R/R LBCL). CAR-T therapy has shown significant promise in providing deep, durable responses, and potential cures for these patients.^1,2 Outcomes vary depending on disease- and patient-related characteristics,³ yet the impact of comorbidities on CAR-T therapy–related outcomes has not been studied in large data sets.

Comorbidities in cancer patients may contribute to the development or worsening of complications and can help guide the best therapeutic approach to balance the risks and benefits of cancer therapy.⁴ This is especially true in the era of immunotherapy, where comorbidities have been shown to reduce rates of immunotherapy utilization,⁵ as well as the success of disease control in those receiving these therapies.^6,7 Furthermore, as older adults are more likely to have preexisting medical conditions, it is imperative to address comorbidities.⁸ Recent studies have shown that nonrelapse mortality (NRM) may be associated with comorbidities. For example, several studies have demonstrated that a leading cause of NRM in CAR-T patients is infections, followed by vascular events, and it is likely that comorbidities may affect the risk for NRM after CAR-T infusion.^9,10 

Preexisting comorbidities, as scored by the Hematopoietic Cell Transplantation Comorbidity Index (HCT-CI), are associated with morbidities and mortality following HCT.¹¹ The Center for International Blood and Marrow Transplant Research (CIBMTR) has been collecting HCT-CI–defined comorbidity parameters for all registered patients who underwent HCT or cellular therapy. However, the cumulative impact of baseline comorbidities on outcomes of CAR-T therapy is not well established. To this end, we studied the effect of comorbidities on survival among recipients of CAR-T therapy for R/R LBCL and developed and validated a Cellular Therapy Comorbidity Index (CT-CI) that predicts survival after treatment.

Utilizing the CIBMTR registry, we identified adults with R/R LBCL who received commercially available CAR-T therapy since Food and Drug Administration approval (2017 and 2018 for axicabtagene ciloleucel [axi-cel] and tisagenlecleucel [tisa-cel], respectively) through December 2020.

CIBMTR is a research collaboration between the Medical College of Wisconsin and NMDP. CIBMTR’s Research Database includes long-term clinical data from >22 000 patients worldwide who received CAR-Ts and other adoptive cellular therapies. Data are submitted before infusion and then during follow-up visits at 3 months, 6 months, and yearly; CIBMTR has automated and manual checks to ensure data quality. The NMDP Institutional Review Board reviews CIBMTR’s research. Patients and/or guardian(s) give informed consent for research.

The individual components of HCT-CI prior to CAR-T infusion were captured from patients’ medical records.¹² The HCT-CI score was calculated per previously published guidelines.¹² 

Comorbidities present in <30 patients were excluded. Weight as a comorbidity was modified to test different body mass index (BMI) categories (<20, 20-30, 30-35, and >35 kg/m²).

Overall survival (OS) was measured from the day of CAR-T infusion until death from any cause. Treatment-related mortality (TRM) included deaths occurring in patients without disease relapse. Relapse was the cumulative incidence of relapse or progression of disease. Incidence and severity of cytokine release syndrome (CRS) and immune effector cell–associated neurotoxicity syndrome (ICANS) were assessed according to the American Society for Transplantation and Cellular Therapy criteria.¹³ 

The primary objective was to develop a CT-CI and investigate its prognostic impact on survival following CAR-T therapy. Patients were randomly divided into a training cohort (1/2 of patients) and a validation cohort (1/2 of patients) using Bernoulli sampling, where each patient is independently assigned to 1 of the 2 groups, with probability of 0.5. To develop the scoring weights for the CT-CI, 951 patients were assigned to a training cohort. To test the CT-CI’s predictive ability, 965 patients were assigned to the validation cohort. In the training set, the Cox proportional hazard model was used to examine risk factors associated with OS. A stepwise selection method identified the significant covariates affecting OS with a significance level of 0.01. Pairwise interactions between significant factors were tested. Multivariate analysis (MVA), adjusting for the significant covariates, was used to develop a weighted score for each comorbidity based on the magnitude of hazard ratios (HRs) for OS. The adjusted HRs for each comorbidity controlling for Karnofsky Performance Status (KPS) and disease status at infusion were converted to integer weights as follows: (1) adjusted HRs <1.1 were not considered; (2) adjusted HRs between 1.1 and 1.5 were converted to a score weight of 1; (3) adjusted HRs between 1.5 and 2 were converted to a score weight of 2; (4) adjusted HRs >2 were converted to a score weight of 3. The new CT-CI was calculated as the sum of weighted scores of individual comorbidities. The CT-CI scores were divided into 5 risk groups: 0, 1, 2, ≥3, and not reported.

To evaluate the impact of CT-CI on TRM and relapse, the cause-specific hazard models were used with all patients. Logistic regression was used to evaluate the impact of the CT-CI on CRS and ICANS. To evaluate the significant covariates affecting CRS and ICANS, the stepwise selection method was used with a significance level of 0.01. The pairwise interactions between the CT-CI and significant variables were tested.

The new CT-CI scores were tested and compared to the HCT-CI in the entire cohort. The C statistic (C index) was used to evaluate the predictive availability of the CT-CI and compare it to the HCT-CI. Because the primary outcome, OS, is time-dependent, the time-dependent C index was calculated at 12 months. In addition, the new CT-CI scores were compared to the HCT-CI using negative log-likelihood value, 1 measure of goodness of fit for the model. Lower negative log-likelihood value represents a better model in terms of goodness of fit. To examine the correlation between pairs of comorbidities, Phi coefficients, which is the statistical measure to quantify the association between binary variables, were reported.

A total of 1916 patients from 97 medical centers were included, with a median age of 63.6 years (range, 18.5-91). Of these, 473 patients (25%) were 70 or older, and 1225 (64%) were male (Table 1). Median follow-up was 14.2 months (range, 0.7-39.1). Prior autologous HCTs were reported in 496 (26%) and 1466 (77%) had 3 or more prior lines of therapy. KPS of <80 was recorded in 373 (20%) patients, whereas 836 (44%) patients had an elevated lactate dehydrogenase before CAR-T therapy. At least 1 comorbidity was recorded in 1321 (69%) patients, whereas 558 (29%) patients had none, and 37 (2%) patients did not have reported data (Table 1). Common comorbidities were cardiac disease (12%); diabetes (14%); hepatic dysfunction (mild, 8%; moderate to severe, 2%); psychiatric disturbance (18%); and pulmonary dysfunction (moderate, 15%; severe, 12%). Prevalence of comorbidities are shown in supplemental Figure 1A. Phi coefficients for pairs of comorbidities showed correlations between comorbidities were generally positive in supplemental Figure 1B.

The incidence of all grades of CRS was 75%, and 9% had grade ≥3 events. The incidence of all grades of ICANS was 43%, with 21% of patients with grade ≥3 events (supplemental Table 1). Patients who received axi-cel were more likely than those who received tisa-cel to have CRS (HR, 4.74; 95% confidence interval [CI], 3.76-5.98; P = .004) and ICANS (HR, 5.55; 95% CI, 4.24-7.27; P ≤ .001). The 12-month progression-free survival was 42% (95% CI, 39.9-44.5), and 12-month OS was 62% (95% CI, 59.4-63.9), with an overall TRM of 4%. The most common primary causes of death were LBCL in 618 patients (73% of the patients who died); and infections, in 81 (10%) (supplemental Table 1).

To develop the CT-CI, using the training cohort, a MVA of the impact of each comorbidity on OS was used to develop a weighted score for each comorbidity. The comorbidities associated with mortality and included in the CT-CI were diabetes, cerebrovascular disease, BMI of <20, mild-to-severe hepatic insufficiency, severe pulmonary disease, moderate to severe renal failure (defined as serum creatinine >2, dialysis or prior renal transplant), and infection still requiring continuation of antimicrobial treatment at CAR-T infusion (Table 2; supplemental Table 2). The 2 comorbidities that were not tested due to small numbers were inflammatory bowel disease and peptic ulcer disease. Cardiac disease, both as a composite variable and its subtypes, were not associated with adverse OS.

The CT-CI was calculated for the training cohort and incorporated into a multivariate model assessing the impact of CT-CI on OS (Figure 1A-B). Higher CT-CI scores were associated with increased mortality in the training cohort (CT-CI 1: HR, 1.44 [P = .003]; CT-CI 2: HR, 1.47 [P = .033]; CT-CI ≥ 3: HR, 3.21 [P< .0001]; Table 3) and this association was confirmed in the validation cohort (CT-CI 1: HR, 1.28 [P = .038]; CT-CI 2: HR, 1.52 [P = .013]; CT-CI ≥ 3: HR, 2.09 [P = .0004]; Table 3). Similar findings were seen within the entire cohort (Table 3). There were no significant differences between the CT-CI 1 and 2 risk groups (Table 3: CT-CI 1 vs 2 contrasts) within any of the cohorts, so patients were stratified into the CT-CI risk groups: low-risk group with a score of 0, intermediate-risk group with a score of 1 or 2, and high-risk group with a score of ≥3, for the adjusted OS analysis shown in Figure 1A-B.

A MVA (training cohort) assessing clinical factors other than comorbidities affecting OS identified KPS of 80 (HR, 1.36; 95% CI, 1.14-1.62; P = .001) and <80 (HR, 2.06; 95% CI, 1.72-2.47; P< .001), as well as partial response to previous therapy (HR, 2.21; 95% CI, 1.30-3.75; P = .003) and resistant disease at time of infusion (HR, 2.95; 95% CI, 1.77-4.93; P< .0001) to be associated with worse OS (supplemental Table 3). Age, line of treatment, and product used (axi-cel vs tisa-cel) were included in the model and were not predictive of OS. Notably, CT-CI did not predict CAR-T toxicities, including incidence and severity of CRS and ICANS (supplemental Table 4).

To discriminate disease progression from other causes of mortality, we analyzed the impact of the CT-CI on TRM and relapse. The high-risk CT-CI group was associated with higher TRM (HR, 4.67; 95% CI, 2.52-8.65; P< .0001; Table 4; supplemental Figure 1A). Other significant covariates impacting TRM were age ≥65 (HR, 1.68; 95% CI, 1.16-2.42; P = .006), and KPS <80 (HR, 2.59; 95% CI, 1.62-4.15; P< .0001) (supplemental Table 3). The probability of relapse was higher for intermediate- and high-risk CT-CI groups, respectively (Table 4; supplemental Figure 1B). Other factors associated with relapse were male sex (HR, 1.31; 95% CI, 1.15-1.49; P< .0001), product (axi-cel HR, 0.67; 95% CI, 0.58-0.76; P< .001), disease status of partial response or resistant disease at infusion (HR, 1.96; 95% CI, 1.28-2.99; P = .002; and HR, 2.56; 95% CI, 1.70-3.86; P< .0001, respectively). Patients at age ≥65 had a lower likelihood of relapse (HR, 0.83; 95% CI, 0.73-0.94; P = .003) and KPS of ≤90 had an adverse effect on relapse (HR, 1.21; 95% CI, 1.04-1.41; P = .013; and HR, 1.41; 95% CI, 1.19-1.68; P< .0001 for KPS 80-90 and <80, respectively) (supplemental Table 3).

Although a higher HCT-CI of ≥3 also predicted worse OS in CAR-T patients (HR, 1.19; 95% CI, 1.01-1.41; P = .040), the CT-CI showed increasing risk of mortality with increasing scores, suggesting a more distinct distribution with this updated score (supplemental Table 5). Statistical comparisons of the HCT-CI and CT-CI included C index and fit statistics. The CT-CI C index was 56.7 at 12 months, which was modestly better than the HCT-CI (supplemental Table 6). Similarly, the CT-CI was the better fit compared to HCT-CI with a lower −2 log-likelihood (supplemental Table 6).

The HCT-CI reliably predicts the impact of pretransplant comorbidities on survival after allogeneic HCT.¹¹ Although biomarkers of inflammatory status and disease burden may affect toxicities and outcomes after cellular therapy and HCT,^14,15 the impact of comorbidities after CAR-T therapy remains unclear. Preexisting comorbidities present a multidimensional variable because chronic diseases may influence mortality regardless of treatment for LBCL. Thus, by translating comorbidities into weighted scores, we determined the impact of multiple comorbidities on survival after CAR-T therapy.

The CT-CI was developed using the weighted score for each comorbidity based on the magnitude of the HR for OS. This new CT-CI score (the sum of weighted scores due to comorbidities) was then applied to training and validation cohorts. The CT-CI predicts OS, as higher scores are associated with increased mortality, yet CT-CI does not correlate with the incidence or severity of toxicities, including CRS and ICANS. This score can be used in clinical practice with an online scoring tool, which is publicly accessible and hosted by CIBMTR (https://cibmtr.org/CIBMTR/Resources/Research-Tools-Calculators/CT-CI-Calculator).

The CT-CI utilizes less variables than the HCT-CI and was developed and validated specifically for CAR-T patients. This score may help clinicians stratify risk, select appropriate patients for CAR-T therapy, and guide the workup of major comorbidities before and during CAR-T therapy. The CT-CI can help clinicians better predict outcomes with CAR-T therapy and potentially use the score to counsel patients before undergoing this treatment. Although even in the high-risk CT-CI score groups, some patients had long-term survival after treatment, when taken together with KPS, disease burden, and other risk factors, CT-CI may be useful for assessing risks before therapy. Nevertheless, the C index was not highly predictive of survival, probably due to disease-related factors being strongly correlated with CAR-T outcomes. Thus, the score should not preclude patients from receiving CAR-T therapy, but rather guide patients and treating physicians as to the risks and possible treatment options. This is especially true as more CAR-T products and other modalities such as bispecific T-cell engagers and antibody-drug conjugates become available. Furthermore, strategies to better prepare patients, such as “prehabilitation” programs being explored in this setting, could be employed for high-risk patients.¹⁶ 

Few studies have investigated the impact of comorbidities on the outcomes of CAR-T therapy, with varying results. A retrospective analysis of 130 patients with r/r LBCL who received CAR-T therapy showed that patients with more severe comorbidities developed severe CRS and neurotoxicity and had inferior survival compared with those with lower comorbidity burden.¹⁷ Moreover, several studies have shown a strong correlation between pretreatment levels of serum biomarkers like ferritin, C-reactive protein, and lactate dehydrogenase, and CAR-T toxicity, however, these models did not account for preexisting comorbidities.^18,19 A recent study looked at the prognostic power of Severe4, a modified Cumulative Illness Rating Scale, a comorbidity score intended to predict mortality in older, hospitalized patients.²⁰ The study found that high (>2) Cumulative Illness Rating Scale scores, in either the respiratory, upper gastrointestinal, renal, or hepatic systems, had the strongest impact on progression-free survival and OS. These findings are similar to the current study, yet our study adds important information regarding weighted scores used in the novel CT-CI, and its dose-response risk stratification (Table 2; Figure 1) may provide more in-depth information regarding patients’ risk. For example, moderate or severe hepatic insufficiency had a weighted score of 3, with a high HR of 3.84 for mortality, yet mild hepatic disease only has an HR of 1.48 Furthermore, because our study was performed on more patients, there are other comorbidities which were found to be significant prognostic factors, such as infections with ongoing treatment, low BMI, diabetes, and cerebrovascular disease.

The prognostic effect of preexisting comorbidities on cancer patients is also likely explained by the physiological burden of chronic diseases and their interaction with cancer and cancer-related treatment and manifested as treatment-related toxicities.²¹ It has been well established that CAR-T therapy toxicities, including CRS and ICANS, are primarily related to disease biology, disease burden, type of product, cell dose, and CAR-T construct.²² However, little is known about the impact of comorbidities on CAR-T–related toxicities. This study shows that comorbidities do not impact the incidence of toxicities like CRS and ICANS after CAR-T therapy for R/R LBCL and should not preclude patients from this effective therapy.

Since most patients whose disease does not respond to CAR-T therapy die of relapse, we analyzed whether the CT-CI predicts for relapse or TRM as well. Higher CT-CI scores were associated with higher incidence of both TRM and relapse. This higher incidence of relapse could be related to more refractory disease status or aggressive disease biology leading to end-organ dysfunction as captured by CT-CI at the time of CAR-T infusion. Infections, a common cause of death in our cohort, are likely to be more severe and lead to serious complications/consequences in patients with poor organ reserve, as determined by CT-CI. Active infection prior to CAR-T therapy is a modifiable risk factor that worsened survival in our model; hence attempts should be made to resolve clinically significant infections before CAR-T therapy, especially for patients with higher CT-CI.

Other factors associated with worse mortality after CAR-T therapy included disease status before CAR-T infusion, especially resistant disease or partial response, and poor KPS. However, both disease and KPS were excluded from the CT-CI, as we focused on determining comorbidities that impact survival. Worse KPS prior to CAR-T therapy was associated with a higher incidence of overall mortality, but not TRM or toxicities, including CRS or ICANS. This may be due to R/R disease, causing weakness and reduced physical fitness, eventually leading to disease-related mortality.

There are several limitations to our analysis. Because this was a retrospective analysis, some data may not have been recorded and hence not included in the analysis. However, the introduction of laboratory and functional data, most of which were reliably captured and reported to the CIBMTR registry, has reduced the possibility of missing significant comorbidities.

Another limitation of the analysis was a lack of interrater and test-retest reliabilities in the study, as well as the use of the definitions of the original HCT-CI score,¹¹ which was created for a different clinical setting, with additional relevant variables based on laboratory and functional testing. However, the CT-CI likely has the same interrater reliability as the original HCT-CI, which can be addressed by confirming the findings of our analysis with future studies with CIBMTR and multicenter consortium efforts utilizing real-world data.

Unlike HCT-CI for allogeneic HCT patients, comorbidity data for CAR-T recipients are not always available, as baseline tests like pulmonary function tests and echocardiograms are not yet standard for all patients. This may affect the performance of the CT-CI; for example, pulmonary function testing may have been performed only in patients with risk factors or pulmonary symptoms. Thus, the fact that only severe pulmonary dysfunction was associated with worse OS may be potentially biased, especially as a previous study showed limited correlation between general pretreatment pulmonary function tests (PFTs) and short- or long-term survival.^23,24 However, our results stress the importance of PFT testing before CAR-T infusion, especially in patients with high risk for pulmonary disease.

The analysis included only patients with R/R LBCL and thus should not be directly applied in patients with leukemia or multiple myeloma, or to patients receiving CAR-T treatment in earlier lines of therapy.

Because most patients in the analysis received axi-cel (CD28 costimulatory domain), the interpretation of these results may vary for patients receiving other CAR-T designs, such as ones with 41BB costimulatory domain, as their toxicity and efficacy profiles vary greatly.

In summary, we have developed the CT-CI, a new tool for assessing pre–CAR-T comorbidities, which could be used to risk-stratify patients receiving CAR-T therapy. Our study not only reported the incidence of comorbidities in a large sample size but also identified prognostically important coexisting illnesses that should be measured in clinical trials and CAR-T registries. With the increasing use of CAR-T therapy, as well as other advanced treatment modalities used after or instead of CAR-T, it will be important to validate CT-CI for other disease indications and products, over longer periods of follow-up.

The authors thank Jennifer Motl of the Medical College of Wisconsin (MCW), for editorial support, which was funded by MCW in accordance with Good Publication Practice guidelines (http://www.ismpp.org/gpp3).

CIBMTR is supported primarily by the Public Health Service U24CA076518 from the National Cancer Institute (NCI), the National Heart, Lung and Blood Institute (NHLBI), and the National Institute of Allergy and Infectious Diseases (NIAID); Cellular Immunotherapy Data Resource (CIDR-NCI, U24CA233032); 75R60222C00011 from the Health Resources and Services Administration (HRSA); and N00014-24-1-2057 and N00014-25-1-2146 from the Office of Naval Research. Additional federal support is provided by U01AI184132 from the National Institute of Allergy and Infectious Diseases (NIAID); and UG1HL174426 from the National Heart, Lung and Blood Institute (NHLBI). Support is also provided by the Medical College of Wisconsin, NMDP, Gateway for Cancer Research, Pediatric Transplantation and Cellular Therapy Consortium and from the following commercial entities: AbbVie; Actinium Pharmaceuticals, Inc.; Adaptimmune LLC; Adaptive Biotechnologies Corporation; ADC Therapeutics; Adienne SA; Alexion; AlloVir, Inc.; Amgen, Inc.; Astellas Pharma US; AstraZeneca; Atara Biotherapeutics; Autolus Limited; Beam; BeiGene; BioLineRX; Blue Spark Technologies; bluebird bio, inc.; Blueprint Medicines; Bristol Myers Squibb Co.; CareDx Inc.; Caribou Biosciences, Inc.; CSL Behring; CytoSen Therapeutics, Inc.; DKMS; Elevance Health; Eurofins Viracor, DBA Eurofins Transplant Diagnostics; Gamida-Cell, Ltd.; Gift of Life Biologics; Gift of Life Marrow Registry; HistoGenetics; ImmunoFree; In8bio, Inc.; Incyte Corporation; Iovance; Janssen Research & Development, LLC; Janssen/Johnson & Johnson; Japan Hematopoietic Cell Transplantation Data Center; Jasper Therapeutics; Jazz Pharmaceuticals, Inc.; Karius; Kashi Clinical Laboratories; Kiadis Pharma; Kite, a Gilead Company; Kyowa Kirin International plc; Labcorp; Legend Biotech; Mallinckrodt Pharmaceuticals; Med Learning Group; Medac GmbH; Medexus; Merck & Co.; Mesoblast, Inc.; Millennium, the Takeda Oncology Co.; Miller Pharmacal Group, Inc.; Miltenyi Biomedicine; Miltenyi Biotec, Inc.; MorphoSys; MSA-EDITLife; Neovii Pharmaceuticals AG; Novartis Pharmaceuticals Corporation; Omeros Corporation; Orca Biosystems, Inc.; OriGen BioMedical; Ossium Health, Inc.; Pfizer, Inc.; Pharmacyclics, LLC, An AbbVie Company; Pierre Fabre Pharmaceuticals; PPD Development, LP; Registry Partners; Rigel Pharmaceuticals; Sanofi; Sarah Cannon; Seagen Inc.; Sobi, Inc.; Sociedade Brasileira de Terapia Celular e Transplante de Medula Óssea (SBTMO); Stemcell Technologies; Stemline Technologies; STEMSOFT; Takeda Pharmaceuticals; Talaris Therapeutics; Tscan Therapeutics; Vertex Pharmaceuticals; Vor Biopharma Inc.; Xenikos BV.

Contribution: U.G., H.H., M.E., S.K., A.M., F.T.A., M.B., U.F., S.G., M.D.J., P.K., F.L.L., E.M., T.N., A.L.O., M.P., M.-A.P., P.R.G., R.S., E.J.S., M.M.-S., C.S., A.V., K.W., M.C.P., S.A., and M.S. conceptualized and designed the study; U.G., H.H., M.E., S.K., A.M., T.O., F.T.A., M.B., U.F., S.G., M.D.J., P.K., F.L.L., E.M., T.N., A.L.O., M.P., M.-A.P., P.R.G., R.S., E.J.S., M.M.-S., C.S., A.V., K.W., M.C.P., S.A., and M.S. were responsible for collection and assembly of data; and all authors contributed to data analysis and interpretation, and final approval of the manuscript, and are accountable for all aspects of work.

Conflict-of-interest disclosure: S.A. reports consultancy for ADC Therapeutics, Kite/Gilead, Servier, Chimagen; and research funding from Chimagen, Genmab, Merck, and Janssen. F.T.A. reports contracted research for Pharmacyclics LLC, an AbbVie Company; consulting agreements for other contracted research with Janssen, Gilead, Kite Pharmaceuticals, Karyopharm, MEI Pharma, Verastem, Incyte, Johnson and Johnson, Merck, Epizyme, Loxo Oncology, Adaptive Biotechnologies, Genmab, and Actinium Pharmaceuticals; other consulting agreements with AstraZeneca Pharmaceuticals LP; other advisory committee roles with AbbVie Inc, ADC Therapeutics, AstraZeneca Pharmaceuticals LP, BeiGene Ltd, Bristol Myers Squibb (BMS) Company, Cardinal Health, Caribou Biosciences Inc, Celgene Corporation, Cellectar Biosciences Inc, Dava Oncology, Epizyme Inc, and Genentech, a member of the Roche; and advisory committee participation for AstraZeneca Pharmaceuticals LP; they also report serving on the DSMC for Ascentage, AstraZeneca, and Caribou Biosciences. V.B. reports research funding from Citius, Incyte, and Gamida Cell; advisory board roles for ADC Therapeutics, AstraZeneca, and CRIPSR; and data safety and monitoring board membership for Miltenyi Biotec. T.B. reports advisory board roles for MorphoSys, Takeda, Amgen, Syndax, and Pfizer; and research funding from Takeda and Amgen. M.B. reports employment with BMS. P.B. reports consultancy for Miltenyi Biotec, Kite/Gilead, Amgen, AstraZeneca, Novartis, and Pfizer. A.M.B. reports attending the following advisory board consultations: Kite Pharma (2022), Autolus (2023), and Legend Biotech. A.C. reports consultancy for Kite Pharma and BMS. B.D. reports consultation or advisor board for Global Health Research, GSK, F. Hoffmann-La Roche AG, Caribou, Poseida, MOSAIC Research, Sanofi, Pierre Fabre, Poseida, AstraZeneca, Arima Genomics, Dava Oncology, and Sai Medpartners; institutional research funding from Janssen, Angiocrine, Pfizer, Poseida, MEI, Orcabio, Wugen, Allovir, NCI, Atara, Gilead, Molecular templates, BMS, F. Hoffmann-La Roche AG, Lyell, Ono, and Merck. M.E. reports membership with BMS/Celgene, Kite/Gilead, Pfizer, AbbVie, Janssen, Jazz Pharmaceuticals, and Novartis; honoraria and membership on an entity’s board of directors or advisory committees for BMS/Celgene, Kite/Gilead, Pfizer, AbbVie, Janssen, Jazz Pharmaceuticals, and Novartis; honoraria from BMS/Celgene, Kite/Gilead, Pfizer, AbbVie, Janssen, Jazz Pharmaceuticals, and Novartis; and research funding from BMS/Celgene, Kite/Gilead, Pfizer, AbbVie, Janssen, Jazz Pharmaceuticals, and Novartis. S.G. reports speaker’s role for Pfizer and Kite Pharma; and advisory board role for BMS and Sanofi. U.G. reports honoraria from Gilead, BMS, and Novartis. H.H. reports honoraria from Janssen, BMS, Karyopharm, and GSK; speaker’s role for Janssen; honoraria from and speakers bureau participation for Janssen and BMS; and speakers bureau participation for Sanofi and GSK. L.C.H. reports consultancy/scientific advisory board member for March Biosciences; speakers bureau participation and consultancy for Kite/Gilead; and advisory board participation with Autolus Ltd. M.D.J. reports research funding (to institution) from Kite/Gilead, Incyte, and Lilly; consultancy/advisory/honoraria from J&J, Kite/Gilead, and Prime Education. T.J. reports institutional research support from CTI Biopharma, Kartos Therapeutics, Incyte, and Tscan; and advisory board participation with BMS, Incyte, AbbVie, CTI, Kite, Cogent Biosciences, Blueprint Medicine, Telios Pharma, Protagonist Therapeutics, Galapagos, Tscan Therapeutics, Karyopharm, MorphoSys, and In8Bio. P.K. reports consultancy for Pfizer, Kite, and Jazz Pharmaceuticals. A.S.K. reports consultancy for Abbvie, AstraZeneca, Eli Lilly, and Galapagos; and speaking honoraria from AstraZeneca, Eli Lilly, and Janssen. F.L.L. reports scientific advisory role/consulting fees for A2, Adaptive Biotechnologies, Adaptiummune, Allogene, Amgen, Astra-Zeneca, Bluebird Bio, BMS, Calibr, Caribou, EcoR1, Gerson Lehrman Group (GLG), Iovance, Kite Pharma, Janssen, Legend Biotech, Miltenyi, Novartis, Sana, Pfizer, Poseida; Data Safety Monitoring Board for Data and Safety Monitoring Board for the NCI Safety Oversight CAR T-cell Therapies Committee; research contracts or grants to my institution for service for 2SeventyBio (institutional), Allogene (institutional), BMS (institutional), Incyte (institutional), Kite Pharma (institutional), Leukemia and Lymphoma Society Scholar in Clinical Research (PI: Locke), Mark Foundation, National Cancer Institute; patents, royalties, other intellectual property: several patents held by the institution in my name (unlicensed) in the field of cellular immunotherapy; and education or editorial activity for Aptitude Health, ASH, ASTCT, Clinical Care Options Oncology, and Society for Immunotherapy of Cancer. P.D.L. reports research funding from BMS, Marker Therapeutics; consultancy for Novartis, Foundation for the Accreditation of Cellular Therapies; and data review board participation for Fate Therapeutics. J.P.M. reports consultancy with and honoraria from Novartis, Nektar, and Incyte; consultancy, honoraria, research funding, and speakers bureau with AlloVir and Kite, a Gilead Company; consultancy, honoraria, and speakers bureau with BMS and Sanofi; research funding from Orca Bio; honoraria from Sana; consultancy and research funding with CRISPR Therapeutics and Astellas Pharma; participation in IIT Clinical Trial with In8bio, Inc.; and consultancy, honoraria, research funding with Juno Therapeutics, Magenta Therapeutics, Autolus, Gamida Cell, and Caribou. A.M. reports research funding from Gilead; honoraria from Takeda; and consultancy for BMS and Jazz Pharmaceuticals. T.N. reports participation in the Speakers Bureau for Medexus; personal fees from Karyopharm and Novartis (outside the submitted work); and other support for Moffitt Cancer Center. M.P. reports speakers bureau participation for Kite/Gilead; and advisory board participation for BMS. M.-A.P. reports honoraria from Allogene, Celgene, Bristol Myers Squibb, Exevir, ImmPACT Bio, Incyte, Kite/Gilead, Merck, Miltenyi Biotec, Nektar Therapeutics, Novartis, Omeros, OrcaBio, Pierre Fabre, Sanofi, Syncopation, Takeda, VectivBio AG, and Vor Biopharma; serves on DSMBs for Cidara Therapeutics and Sellas Life Sciences; has ownership interests in Omeros and OrcaBio; has received institutional research support for clinical trials from Allogene, Genmab, Incyte, Kite/Gilead, Miltenyi Biotec, Novartis, and Tr1x. R.S. reports grant support from the National Institutes of Health/National Cancer Institute (NIH/NCI) Memorial Sloan Kettering Cancer Center Support Grant (P30 CA008748), and an NIH-NCI K08CA282987 award; and speaker honoraria from Incyte, Sanofi, and MSD. E.J.S. reports consultancy for NY Blood Center and Celaid Therapeutics; scientific advisory board for Adaptimmune, Navan, Zelluna Immunotherapy, FibroBiologics, Axio, and Orca Biosystems; and a license agreement for Takeda, Affimed, and Prana X. “M.S. reports consultancy and honorarium from Jazz Pharmaceuticals and research funding from BlueNote and Massachusetts General Hospital”; and research funding from Jazz Pharmaceuticals, BlueNote, and Massachusetts General Hospital. C.J.T. reports: C.J.T. has the right to receive payment from Fred Hutch as an inventor on patents related to CAR-T cell therapy; stock options for Eureka Therapeutics, Caribou Biosciences, Myeloid Therapeutics, ArsenalBio; membership on an entity’s advisory committees for Caribou Biosciences, T-CURX, Myeloid Therapeutics, ArsenalBio, IQVIA, Differentia Bio, eGlint; research funding from Juno Therapeutics/BMS, Nektar Therapeutics, 10x Genomics, Genscript, Kite/Gilead, Umoja Biopharma; ad hoc advisory boards/consulting (last 12 months) for Nektar Therapeutics, Century Therapeutics, Prescient Therapeutics, Merck, Sharp and Dohme, Abbvie; and other: DSMB member for Kyverna. M.C.P. reports research funding from BMS, Novartis, Kite, and Janssen; consultancy for Astra Zeneca; and honoraria for Kite and Gilead. P.A.R has served as a consultant and/or advisory board member for AbbVie, Novartis, BMS, ADC Therapeutics, Kite/Gilead, Genentech/Roche, Pfizer, Miltenyi, CVS Caremark, Genmab, BeiGene, and Janssen/Pharmacyclics; received travel support from Adaptive Biotechnologies; and institutional research support from AbbVie, BMS, Kite Pharma, Novartis, Xencor, Fate Therapeutics, Genentech, and Cellectis. C.S. reports research support from Pfizer, Bristol Myers Squibb, Roche, and Janssen; and honoraria from Pfizer. U.F. reports honoraria from Kite Pharma. The remaining authors declare no competing financial interests.

Correspondence: Amy Moskop, Center for International Blood and Marrow Transplant Research, 9200 W Wisconsin Ave, CC5500, Milwaukee, WI 53226; email: amoskop@mcw.edu.