Baseline immunonutritional status drives CAR T-cell efficacy, survival, and safety in R/R lymphoma: validation of a pre-lymphodepletion PNI threshold.

Introduction

Chimeric antigen receptor (CAR) T-cell therapy has emerged as a transformative modality for treating relapsed/refractory (R/R) lymphoma, with unprecedented response rates and curative potential [ 1 , 2 ]. However, the clinical success of CAR T - cell products is often heterogeneous and is dictated by an interplay of tumor-intrinsic factors—such as TP53 mutations, target antigen modulation, and an immunosuppressive microenvironment—and the complex biological behavior of the engineered cells [ 3 , 4 ]. Furthermore, the therapeutic window is frequently narrowed by severe treatment-related toxicities, including cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS), alongside significant variability in long-term durable remission [ 5 , 6 ].

In addition to tumor-centric features, emerging evidence suggests that host-intrinsic factors, particularly baseline systemic inflammation and nutritional status, play pivotal roles in determining the “fitness” of a patient’s immune system to support CAR T-cell expansion, persistence, and homeostasis [ 7 – 9 ]. In the context of cellular immunotherapy, the identification of robust, noninvasive biomarkers to evaluate this immunonutritional axis is essential for refining response prediction and optimizing patient selection.

The prognostic nutritional index (PNI), a composite score derived from serum albumin levels and absolute lymphocyte counts, was initially developed to assess perioperative risk in surgical oncology [ 10 ]. Its biological relevance lies in its dual reflection of the host’s physiological state: the albumin level serves as a surrogate marker for hepatic synthetic function and systemic inflammatory burden [ 11 , 12 ], while the absolute lymphocyte count represents the endogenous T-cell reservoir and cellular immune competence [ 13 ]. While the PNI has been validated as a powerful prognostic indicator in various solid malignancies—including lung and gastrointestinal cancers [ 14 – 17 ]—and in patients with lymphoma treated with conventional chemo-radiotherapy [ 18 – 23 ], these historical data may not directly translate to the CAR T-cell era. Previous studies conducted on patients undergoing chemotherapy typically utilized PNI cutoffs ranging from 40 to 50, consistently indicating that a low PNI is associated with poor survival. However, the unique dynamics of CAR T - cell therapy—where the “living drug” must engraft and expand within the host — necessitate a specific evaluation of the PNI, particularly at the critical prelymphodepletion window.

To date, the predictive value of the PNI in CAR T-cell therapy for R/R lymphoma remains insufficiently explored, especially concerning its correlation with acute toxicities and long-term survival in large-scale cohorts. Therefore, this study aimed to investigate whether the prelymphodepletion PNI can serve as a determinant of therapeutic efficacy, long-term survival outcomes, and the risk of severe toxicities in a large cohort of 449 patients, thereby providing a practical framework for risk stratification in cellular immunotherapy.

Materials and methods

In this retrospective study, data from 449 patients with R/R lymphoma who received CAR T-cell therapy at our center between December 2018 and January 2024 were analyzed. The study was approved by the Institutional Review Committee of Beijing GoBroad Hospital (ChiCTR-1900020980, ChiCTR-2200058972). All procedures adhered to the principles of the Declaration of Helsinki, and written informed consent was obtained from all participants prior to enrollment.

Clinical variables were obtained from two sources. Sex and disease type (including the classification of other rare subtypes) were obtained from medical records at initial diagnosis. All other variables, including age, BMI, ECOG, Ann Arbor stage, IPI, ALB, LYM, LDH, extranodal disease status, prior radiotherapy, prior transplantation, and disease response status after bridging therapy, were assessed before lymphodepleting chemotherapy (within 3 days prior to CAR T-cell infusion).This distinction ensures that time-dependent variables reflect the pre-CAR T disease burden rather than initial diagnostic features.

All patients in this study received second-generation targeted CD19 CAR constructs that integrate the 4-1BB costimulatory domain and the CD3-zeta domain. The antigen-binding fragment of YK-CD19BB-001 is derived from the FMC63 monoclonal antibody. The cytotoxic efficacy of this product has been validated in preliminary experiments (Supplementary Methods 1 ).

The PNI was calculated using the formula: serum albumin level (g/L) + 5 × total lymphocyte count (×10⁹/L) [ 24 ], based on values obtained within three days before administering lymphodepleting chemotherapy. Maximally selected rank statistics were used to calculate the optimal cutoff value of the PNI.

Patients underwent standardized efficacy assessments following CAR T-cell infusion: monthly for the first 3 months and every 3 months thereafter for up to 2 years. Treatment response was evaluated using imaging modalities such as PET/CT, contrast-enhanced CT, or MRI, according to the Lugano classification and 2016 NCCN guidelines. Clinical endpoints included the objective response rate (ORR) and complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD) rates. OS was defined as the time from CAR-T-cell infusion to death from any cause, and progression-free survival (PFS) was defined as the time from infusion to either disease progression or death, whichever occurred first.

Adverse events—including CRS, ICANS, and hematologic toxicities—were graded according to the American Society for Transplantation and Cellular Therapy (ASTCT) consensus criteria, while hematologic toxicities (neutropenia, thrombocytopenia, anemia) were graded per the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0 [ 25 ].

Statistical analysis method

Data analysis was performed using SAS 9.4 and R 4.4.1. Maximally selected rank statistics were used to calculate the optimal cutoff value of the PNI. A bootstrap resampling procedure was then performed to assess its stability: in each resample, the optimal cutoff value was re‑estimated, and the 95% percentile interval of the cutoff values across resamples was calculated. To evaluate the discriminative power of the obtained cutoff value, a time‑dependent C‑index was computed using the optimism correction bootstrap method. Continuous variables were compared using the t test or the Mann-Whitney test, while comparisons of the categorical variables were performed by the chi-square test or Fisher’s exact method. The median follow-up time was estimated by the reverse Kaplan-Meier method. The OS and PFS were summarized using the Kaplan-Meier method, and the KM curves were plotted. The differences in survival curves between the groups were compared using the log-rank test. The ORR and its two-sided 95% Clopper-Pearson confidence interval were calculated, and the P value for the comparison between the two groups was calculated using Fisher’s exact method. Cox regression analyses were performed to identify independent prognostic factors, and the potentially significant variables ( p < 0.10) in univariate analyses were included in multivariate analysis. Multivariable logistic regression with covariate adjustment was performed to evaluate the association between PNI and the occurrence of CRS and ICANS.

Results

Baseline characteristics

A total of 449 patients with relapsed/refractory aggressive B-cell lymphoma were enrolled in this study, including 235 males (52.3%) and 214 females (47.7%), with a median age of 52 years (range: 13–86 years). All patients had received at least two lines of prior therapy. The pathological subtypes included diffuse large B-cell lymphoma not otherwise specified (DLBCL NOS, n = 312, 69.5%), central nervous system lymphoma (CNSL, n = 80, 17.8%), high-grade B-cell lymphoma (HGBL, n = 29, 6.5%), primary mediastinal large B-cell lymphoma (PMBL, n = 19, 4.2%), and transformed follicular lymphoma to diffuse large B-cell lymphoma (tFL, n = 9, 2.0%).

Based on the optimal PNI cutoff of 39.2, patients were stratified into a high-PNI group (> 39.2, n = 363, 80.8%) and a low-PNI group (≤ 39.2, n = 86, 19.2%). Several baseline variables were imbalanced between the two groups. Compared with the high-PNI group, the low-PNI group had a higher proportion of patients with BMI < 18.5 kg/m² (22.1% vs. 8.5%, P < 0.001); ECOG performance status ≥ 2 (60.5% vs. 29.5%, P < 0.001); and IPI score ≥ 3 (66.3% vs. 35.5%, P < 0.001). Regarding laboratory parameters, the low-PNI group had a significantly higher rate of abnormal LDH levels than the high-PNI group (68.6% vs. 36.4%, P < 0.001).

No significant differences were observed between the two groups regarding age ( P = 1.000), sex ( P = 0.233), Ann Arbor stage ( P = 0.536), prior radiotherapy ( P = 0.350), prior transplantation ( P = 0.240), Disease response status ( P = 0.053), or extranodal disease status ( P = 0.600) (Table 1 ).

PNI cutoff value

Maximally selected rank statistics were used to calculate the optimal cutoff value of the PNI. Based on the optimal PNI cutoff of 39.2 derived from the log-rank test for overall survival, the patients were stratified into a low-PNI group (≤ 39.2, n = 86, 19.2%) and a high-PNI group (> 39.2, n = 363, 80.8%) (Fig. 1 ).

We performed bootstrap internal validation with 1,000 resamples. In each bootstrap sample, maximally selected rank statistics were applied to identify the optimal cutoff for overall survival (OS). The 95% percentile interval of the cutoffs across bootstrap samples was 38.17 to 42.60, supporting the robustness of the original threshold (Supplementary Fig. 1 ).

Time-dependent receiver operating characteristic (ROC) analysis was performed to evaluate the discriminatory ability of the pre-lymphodepletion PNI for overall survival at various time points. The time-dependent AUC values for OS ranged from 0.601 to 0.634 across 12 to 60 months, indicating modest prognostic discrimination that remained stable over time. We performed bootstrap validation of the time-dependent C-index, which confirmed model stability without significant overfitting (Supplementary Fig. 2 – 3 ).

Therapeutic response to CAR T-cell therapy

In the cohort ( n = 449), the ORR was 61.4%, with a CR rate of 47.5%. Stratification by the PNI revealed that the ORR was significantly higher in the high-PNI group than in the low-PNI group (65.5% vs. 44.2%, P < 0.001). Similarly, the CR rate was significantly higher in the high-PNI group (52.2% vs. 27.9%, P < 0.001).One patient in the high-PNI group had an unevaluable response and was excluded from the response analysis (Fig. 2 ).

Survival analysis

The median follow-up duration was 33.12 months (range: 0.13–74.71 months). For all patients, the median OS was not reached (95% CI: 27.04%-not reached), and the median PFS was 8.05 months (95% CI: 6.28–13.31). The estimated 5-year OS and PFS rates were 51.85% (95% CI: 46.76%–56.69%) and 37.04% (95% CI: 32.21%–41.87%), respectively.and the Kaplan–Meier analysis revealed that the OS and PFS of patients in the high-PNI group were higher than those of patients in the low-PNI group (mOS: NR vs. 6.49 months [95% CI: 4.53–8.28 months], P < 0.001; mPFS: 18.43 months [95% CI: 9.03–31.01 months] vs. 2.96 months [95% CI: 1.94–4.40], P < 0.001).

Patients in the high-PNI group had significantly better long-term survival outcomes than those in the low-PNI group, with 5-year OS rates of 59.02% (95% CI: 53.29–64.29%) vs. 21.88% (95% CI: 13.52–31.52%) and 5-year PFS rates of 42.69% (95% CI: 37.07–48.20%) vs. 13.29% (95% CI: 6.99–21.63%), respectively (Fig. 3 ).

CAR T-cell therapy-related toxicities

Within 30 days post-CAR T-cell infusion, cytokine release syndrome (CRS) occurred in 77.3% of patients (347/449), and the incidence rate of the events graded ≥ 3 was 8.2% (37/449). Immune effector cell-associated neurotoxicity syndrome (ICANS) was observed in 4.2% (19/449), of the patients, and the incidence rate of the events graded ≥ 3 was 2.2% (10/449). Regarding hematologic toxicities, the incidence of grade ≥ 3 neutropenia was 95.3% (428/449), followed by thrombocytopenia (64.6%, 290/449) and anemia (58.1%, 261/449) (Table 2 ).

Multivariate analysis further confirmed that a high PNI was independently associated with a significantly lower risk of any-grade CRS (OR = 0.446, 95% CI: 0.201–0.989, P = 0.047). However, a high PNI only showed a trend toward a lower risk of grade ≥ 3 CRS that did not reach statistical significance (OR = 0.533, 95% CI: 0.240–1.180, P = 0.121); nor was it significantly associated with a reduced risk of any-grade ICANS (OR = 0.645, 95% CI: 0.217–1.923, P = 0.432). Additionally, an ECOG score ≥ 2 emerged as an independent risk factor for grade ≥ 3 CRS (OR = 3.109, 95% CI: 1.235–7.825, P = 0.016); and for any-grade ICANS (OR = 5.437, 95% CI: 1.476–20.027, P = 0.011). However, given the low incidence of ICANS (4.2% for any-grade, 2.2% for grade ≥ 3) and the small number of grade ≥ 3 events ( n = 10), these results should be interpreted with caution due to potential bias from sparse events (Table 3 ).

Univariate and multivariate analysis

Overall survival (OS)

In univariate Cox regression analysis, high PNI group, BMI ≥ 18.5 kg/m², and absence of extranodal disease were also associated with significantly superior overall survival (OS), with corresponding HR = 0.298 (95% CI: 0.222–0.401, P < 0.001); HR = 0.545 (95% CI: 0.370–0.803, P = 0.002); and HR = 0.639 (95% CI: 0.460–0.888, P = 0.008); respectively. Additional univariate predictors of worse OS included ECOG performance status ≥ 2, IPI score ≥ 3, elevated LDH, and non-responder status, with corresponding HR = 2.300 (95% CI: 1.745–3.031, P < 0.001); HR = 2.585 (95% CI: 1.958–3.413, P < 0.001); HR = 2.383 (95% CI: 1.805–3.144, P < 0.001); and HR = 1.824 (95% CI: 1.362–2.444, P < 0.001); respectively. Age, sex, and disease stage were not significant in univariate analysis (all P > 0.05).

In the multivariable Cox regression model, high PNI group remained an independent predictor of superior OS (HR = 0.425, 95% CI: 0.310–0.582, P < 0.001). Independent negative prognostic factors included elevated LDH, and non-responder status (HR = 1.671, 95% CI: 1.224–2.281, P = 0.001; and HR = 1.529, 95% CI: 1.133–2.062, P = 0.005), respectively. After multivariable adjustment, BMI, ECOG performance status, IPI, and extranodal disease status were no longer significant (all P > 0.05) (Table 4 ).

Progression-free survival (PFS)

In univariate Cox regression analysis, high PNI group, BMI ≥ 18.5 kg/m², and absence of extranodal disease were identified as protective factors for progression-free survival (PFS), with corresponding HR = 0.394 (95% CI: 0.301–0.516, P < 0.001); HR = 0.601 (95% CI: 0.425–0.851, P = 0.004 ); and HR = 0.538 (95% CI: 0.402–0.720, P < 0.001 ); respectively. Conversely, factors associated with worse PFS included ECOG performance status ≥ 2, IPI score ≥ 3, elevated LDH, and non-responder status, with corresponding HR = 1.990 (95% CI: 1.563–2.534, P < 0.001 ); HR = 2.336 (95% CI: 1.838–2.970, P < 0.001 ); HR = 2.098 (95% CI: 1.651–2.664, P < 0.001 ); and HR = 1.889 (95% CI: 1.470–2.427, P < 0.001 ); respectively. Age, sex, and disease stage were not significantly associated with PFS in univariate analysis (all P > 0.05).

In the multivariable Cox regression model, high PNI group and absence of extranodal disease remained independent protective factors for PFS (HR = 0.542, 95% CI: 0.408–0.720, P < 0.001 ; and HR = 0.726, 95% CI: 0.537–0.983, P = 0.038 ; respectively). Other independent predictors of worse PFS included IPI score ≥ 3, elevated LDH, and non-responder status (HR = 1.409, 95% CI: 1.023–1.942, P = 0.036; HR = 1.465, 95% CI: 1.117–1.922, P = 0.006; and HR = 1.628, 95% CI: 1.261–2.103, P < 0.001 ; respectively). After multivariable adjustment, BMI and ECOG performance status were no longer significant (both P > 0.05) (Table 5 ).

Discussion

The therapeutic landscape for R/R lymphoma has been fundamentally reshaped by the advent of CAR T-cell therapy. However, conventional prognostic tools such as the IPI and the NCCN-IPI, which were developed primarily in the chemoimmunotherapy era, often fail to capture the complex interplay between the host immune system and the engineered T cells [ 26 ]. While emerging molecular and genetic biomarkers offer precise insights into tumor biology, their clinical integration is hindered by high costs, technical complexity, and a lack of standardization [ 27 ]. In this context, our study—the largest to date ( N = 449) evaluating the prognostic nutritional index (PNI) in the CAR T-cell therapy setting—provides robust evidence that baseline immunonutritional fitness is a primary driver of therapeutic efficacy, long-term survival, and treatment-related toxicities.

The mechanistic synergy of the PNI, which combines the absolute lymphocyte count (ALC) and the serum albumin level, reflects the “host soil” quality required for successful CAR T-cell engraftment and persistence. Our findings demonstrate that a high prelymphodepletion PNI is an independent predictor of superior outcomes, extending the observations previously reported for multiple myeloma [ 28 ]. The ALC serves as a surrogate marker for the endogenous T-cell pool; a depleted baseline often signifies immune senescence or an exhausted T-cell compartment, which limits the fitness of the starting material for CAR T-cell manufacturing and its subsequent in vivo expansion [ 29 , 30 ]. Moreover, serum albumin is more than a nutritional marker; it is a negative acute-phase reactant that inversely correlates with systemic inflammatory cytokines such as IL-6 and TNF-alpha [ 31 – 33 ].

Multivariate analysis further revealed the outcome-specific predictive value of PNI. A high PNI was identified as an independent predictor of a reduced risk of CRS occurrence ( P = 0.047), indicating that better nutritional status lowers the risk of developing CRS after adjustment for confounders. This pattern suggests that better nutritional status and preserved immune reserve, as reflected by high PNI, may be associated with reduced susceptibility to any-grade CRS following CAR-T therapy [ 34 ]. However, this association did not extend to severe CRS or ICANS.High PNI did not significantly predict a reduced risk of CRS severity (grade ≥ 3: P = 0.121), suggesting that while high PNI protects against CRS occurrence, it does not reliably identify which patients will progress to severe CRS. Importantly, patients with a low PNI had significantly worse disease control prior to lymphodepletion (higher rates of SD/PD, elevated LDH, and more frequent extranodal disease). Given the strong collinearity between PNI and these indicators of disease burden, as well as the low event counts for severe CRS and ICANS, it remains unclear whether the observed differences in toxicity are independently attributable to PNI or largely driven by baseline disease burden. Therefore, these findings should be considered hypothesis-generating rather than conclusive.

The determination of an optimal PNI threshold is pivotal for clinical risk stratification. Using maximally selected rank statistics, we identified 39.2 as the critical cutoff value for OS prediction. Interestingly, this threshold is slightly lower than that reported for chemotherapy-naïve populations (typically 40–50) [ 24 , 35 , 36 ]. This discrepancy likely reflects the “systemic exhaustion” and heavy pretreatment characteristics of patients with R/R lymphoma referred for cellular therapy. Despite this baseline fragility, patients in our high-PNI group achieved a remarkable 5-year OS rate of 59.02%, nearly triple that of patients in the low-PNI group (21.88%). This profound difference reinforces the notion that optimizing the host milieu prior to lymphodepletion may be as crucial as optimizing the CAR T-cell product itself.

First, baseline characteristics were imbalanced between the low- and high-PNI groups, with the low-PNI group exhibiting more unfavorable clinical features, including poorer ECOG performance status, higher IPI scores, elevated LDH, and worse response to bridging therapy. Although we adjusted for these factors in multivariable models, residual confounding cannot be entirely excluded. Despite the strengths of our study, such as a large cohort and long-term follow-up, this study has limitations. Its retrospective nature introduces potential selection bias. Furthermore, the PNI was assessed as a static baseline. In addition, metabolic tumor volume (MTV), a quantitative imaging biomarker of baseline disease burden, was not available in this retrospective cohort. Moreover, although internal bootstrap validation supported the robustness of the PNI cutoff of 39.2, external validation in independent prospective cohorts is warranted to confirm its generalizability. Future prospective research should explore “dynamic PNI” trajectories during the manufacturing and bridging intervals. Investigating whether aggressive nutritional support or preemptive anti-inflammatory strategies in low-PNI patients can “rescue” CAR T-cell outcomes remains essential for precision immunotherapy.

Conclusions

In conclusion, the prelymphodepletion PNI is a simple, cost-effective, and powerful biomarker for predicting clinical response, long-term survival, and severe toxicities in patients with R/R lymphoma receiving CAR T-cell therapy. A PNI threshold of 39.2 provides a valuable tool for identifying patients who may require intensive supportive care or optimized pretreatment strategies. Our data underscore that host immunonutritional fitness is a key determinant of the success of synthetic immunity.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1

Acknowledgements

The authors thank their respective institutions for support in the development of this manuscript. All authors read and approved the final manuscript.

Author contributions

**NZ** : Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing original draft, Writing–review & editing. **FY** : Conceptualization, Writing–original draft, Writing–review & editing. **RL** : Conceptualization, Writing–original draft, Writing–review & editing. **ZF** :Investigation, Writing–original draft, Writing–review & editing. **LD** : Visualization, Writing–review & editing. **YG** :Writing-original draft, Writing–review&editing. **LM** :Writing-original draft, Writing–review&editing. **MC** : Methodology, Writing–original draft, Writing–review & editing. **BD** : Supervision, Writing–original draft, Writing–review&editing. **SG** : Software, Writing–review & editing. **CC** : Software, Writing–review & editing. **XK** : Project administration, Writing–original draft, Writing–review & editing. **KH** : Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing original draft, Writing–review & editing. **All authors have read and agreed to the published version of the manuscript.”**.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author upon reasonable request.

Declarations

Institutional review board statement

The study was approved by the Institutional Review Committee of Beijing GoBroad HospitalApproval No.RP2025-033.approval date:June 25, 2025.and conducted according to the Declaration of Helsinki.

Informed consent

The participants provided their written informed consent to participate in this study.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

1. Shune L, Frigault MJ, Riedell PA (2025) CAR-T cell therapy in older adults with relapsed/refractory LBCL: benefits and challenges. J Immunother Cancer 13(6). 10.1136/jitc-2024-009793

2. Jacobson CA, Munoz J, Sun F, Kanters S, Limbrick-Oldfield EH, Spooner C et al (2024) Real-World Outcomes with Chimeric Antigen Receptor T Cell Therapies in Large B Cell Lymphoma: A Systematic Review and Meta-Analysis. Transpl Cell Ther 30(1):77 e1- e15

3. Locke FL, Rossi JM, Neelapu SS, Jacobson CA, Miklos DB, Ghobadi A et al (2020) Tumor burden, inflammation, and product attributes determine outcomes of axicabtagene ciloleucel in large B-cell lymphoma. Blood Adv 4(19):4898–4911

4. Jain MD, Zhao H, Wang X, Atkins R, Menges M, Reid K et al (2021) Tumor interferon signaling and suppressive myeloid cells are associated with CAR T-cell failure in large B-cell lymphoma. Blood 137(19):2621–2633

5. Liu D, Zhao J (2018) Cytokine release syndrome: grading, modeling, and new therapy. J Hematol Oncol 11(1):121

6. Sterner RC, Sterner RM (2021) CAR-T cell therapy: current limitations and potential strategies. Blood Cancer J 11(4):69

7. Rejeski K, Cordas Dos Santos DM, Parker NH, Bucklein VL, Winkelmann M, Jhaveri KS et al (2023) Influence of Adipose Tissue Distribution, Sarcopenia, and Nutritional Status on Clinical Outcomes After CD19 CAR T-cell Therapy. Cancer Immunol Res 11(6):707–719

8. Nawas MT, Scordo M (2023) Linking Body Composition and Nutritional Status with CAR T-cell Efficacy. Cancer Immunol Res 11(6):704

9. Xu X, Gnanaprakasam JNR, Sherman J, Wang R (2019) A Metabolism Toolbox for CAR T Therapy. Front Oncol 9:322

10. Onodera T, Goseki N, Kosaki G (1984) [Prognostic nutritional index in gastrointestinal surgery of malnourished cancer patients]. Nihon Geka Gakkai Zasshi 85(9):1001–1005

11. Roxburgh CS, McMillan DC (2010) Role of systemic inflammatory response in predicting survival in patients with primary operable cancer. Future Oncol 6(1):149–163

12. Almasaudi AS, Dolan RD, Edwards CA, McMillan DC (2020) Hypoalbuminemia reflects nutritional risk, body composition and systemic inflammation and is independently associated with survival in patients with colorectal cancer. Cancers (Basel) 12(7). 10.3390/cancers12071986

13. Oki Y, Yamamoto K, Kato H, Kuwatsuka Y, Taji H, Kagami Y et al (2008) Low absolute lymphocyte count is a poor prognostic marker in patients with diffuse large B-cell lymphoma and suggests patients’ survival benefit from rituximab. Eur J Haematol 81(6):448–453

14. Nakatani M, Migita K, Matsumoto S, Wakatsuki K, Ito M, Nakade H et al (2017) Prognostic significance of the prognostic nutritional index in esophageal cancer patients undergoing neoadjuvant chemotherapy. Dis Esophagus 30(8):1–7

15. Hirahara N, Tajima Y, Fujii Y, Kaji S, Yamamoto T, Hyakudomi R et al (2018) Preoperative Prognostic Nutritional Index Predicts Long-Term Surgical Outcomes in Patients with Esophageal Squamous Cell Carcinoma. World J Surg 42(7):2199–2208

16. Yang Y, Gao P, Song Y, Sun J, Chen X, Zhao J et al (2016) The prognostic nutritional index is a predictive indicator of prognosis and postoperative complications in gastric cancer: A meta-analysis. Eur J Surg Oncol 42(8):1176–1182

17. Hong S, Zhou T, Fang W, Xue C, Hu Z, Qin T et al (2015) The prognostic nutritional index (PNI) predicts overall survival of small-cell lung cancer patients. Tumour Biol 36(5):3389–3397

18. Shen Z, Wang F, He C, Li D, Nie S, Bian Z et al (2021) The Value of Prognostic Nutritional Index (PNI) on Newly Diagnosed Diffuse Large B-Cell Lymphoma Patients: A Multicenter Retrospective Study of HHLWG Based on Propensity Score Matched Analysis. J Inflamm Res 14:5513–5522

19. Mirili C, Paydas S, Kapukaya TK, Yilmaz A (2019) Systemic immune-inflammation index predicting survival outcome in patients with classical Hodgkin lymphoma. Biomark Med 13(18):1565–1575

20. Perisa V, Zibar L, Knezovic A, Perisa I, Sincic-Petricevic J, Aurer I (2017) Prognostic nutritional index as a predictor of prognosis in patients with diffuse large B cell lymphoma. Wien Klin Wochenschr 129(11–12):411–419

21. He J, Yin H, Xia Y, Wu JZ, Liang JH, Zhu HY et al (2021) Prognostic nutritional index, a novel biomarker which predicts worse prognosis in diffuse large B cell lymphoma. Leuk Res 110:106664

22. Yao N, Hou Q, Zhang S, Xiao H, Liang Y, Xu X et al (2020) Prognostic Nutritional Index, Another Prognostic Factor for Extranodal Natural Killer/T Cell Lymphoma, Nasal Type. Front Oncol 10:877

23. Shen Z, Zhang S, Chen X, Zhang Q, Jiao Y, Shi Y et al (2023) Prognostic value of prognostic nutritional index on extranodal natural killer/T-cell lymphoma patients: A multicenter propensity score matched analysis of 1022 cases in Huaihai Lymphoma Working Group. Hematol Oncol 41(3):380–388

24. Liu X, Duan Y, Wang Y, Zhang X, Lv H, Li Q et al (2025) Predictive value of prognostic nutritional index as prognostic biomarkers in patients with lymphoma: a systematic review and meta-analysis. Clin Transl Oncol 27(3):1274–1287

25. Lee DW, Santomasso BD, Locke FL, Ghobadi A, Turtle CJ, Brudno JN et al (2019) ASTCT Consensus Grading for Cytokine Release Syndrome and Neurologic Toxicity Associated with Immune Effector Cells. Biol Blood Marrow Transpl 25(4):625–638

26. Sehn LH, Gascoyne RD (2015) Diffuse large B-cell lymphoma: optimizing outcome in the context of clinical and biologic heterogeneity. Blood 125(1):22–32

27. Carbone A, Gloghini A, Kwong YL, Younes A (2014) Diffuse large B cell lymphoma: using pathologic and molecular biomarkers to define subgroups for novel therapy. Ann Hematol 93(8):1263–1277

28. Xu P, Liu Y, Wang Y, Feng Q, Zhang H, Cheng H et al (2025) Nutritional status indices on the prognosis of patients with relapsed and refractory multiple myeloma treated with CAR-T cell immunotherapy. Front Nutr 12:1654407

29. Mejia Saldarriaga M, Pan D, Unkenholz C, Mouhieddine TH, Velez-Hernandez JE, Engles K et al (2024) Absolute lymphocyte count after BCMA CAR-T therapy is a predictor of response and outcomes in relapsed multiple myeloma. Blood Adv 8(15):3859–3869

30. Lu Y, Zhu H, Liu Y, Wang Y, Sun Y, Cheng H et al (2023) Prognostic value of prelymphodepletion absolute lymphocyte counts in relapsed/refractory diffuse large B-cell lymphoma patients treated with chimeric antigen receptor T cells. Front Immunol 14:1155216

31. Ding S, Chen R, Wang L, Zu C, Zhou X, Zhang J et al (2023) Cytokine release syndrome was an independent risk factor associated with hypoalbuminemia for patients with relapsed/refractory hematological malignancies after CAR-T cell therapy. BMC Cancer 23(1):1055

32. Xu XZ, Liu R, Zhao WH, Yang Y, Liu J, Zhang WG et al (2023) [Alteration and significance of serum lipid levels and nutritional status during BCMA-CAR-T-cell therapy in patients with refractory or relapsed multiple myeloma: a retrospective study based on LEGEND-2]. Zhonghua Xue Ye Xue Za Zhi 44(10):838–844

33. Valtis YK, Devlin S, Shouval R, Rejeski K, Corona M, De Luna A et al (2025) Cancer cachexia and weight loss before CAR T-cell therapy for lymphoma are independently associated with poor outcomes. Blood Adv 9(1):151–161

34. Ding S, Cai L, Jin A, Zhou X, Yan J, Wang L et al (2022) Nutritional status alterations after chimeric antigen receptor T cell therapy in patients with hematological malignancies: a retrospective study. Support Care Cancer 30(4):3321–3327

35. Ma S, Zhang B, Lu T, Li D, Li T, Shen Z et al (2022) Value of the prognostic nutritional index (PNI) in patients with newly diagnosed, CD5-positive diffuse large B-cell lymphoma: A multicenter retrospective study of the Huaihai Lymphoma Working Group. Cancer 128(19):3487–3494

36. Luan C, Wang F, Wei N, Chen B (2020) Prognostic nutritional index and the prognosis of diffuse large b-cell lymphoma: a meta-analysis. Cancer Cell Int 20:455

Associated Data

Supplementary Materials

Supplementary Material 1

Data Availability Statement

The datasets generated during and/or analysed during the current study are available from the corresponding author upon reasonable request.