TNF‐α increases the risk of bleeding in patients after CAR T‐cell therapy: A bleeding model based on a real‐world study of Chinese CAR T Working Party

Chimeric antigen receptor (CAR) T‐cell therapy has shown excellent clinical efficacy in patients with hematologic malignancies. However, severe bleeding after this treatment is a life‐threatening complication for most patients. This study evaluated the risk factors associated with bleeding in CAR T treatment and developed a predictive model for this complication. Analysis performed in the First Affiliated Hospital of Suzhou University and external validation launched in Suzhou Hongci Hematology Hospital (Jiangsu, China). We conducted a real‐world study incorporating data from 400 patients with hematologic malignancies treated with CAR T between 1 November 2015 and 1 September 2019. Also, 39 patients from another hospital were selected for external validation. Patients with severe bleeding (hazard ratio [HR] 13.04, 95% confidence interval 5.82–29.18; p < 0.001) had a higher risk of death after CAR T. Stage III and IV cytokine release syndrome (CRS) (odds ratio [OR] 6.07, 95% CI 2.35–16.76; p < 0.001) and higher tumor necrosis factor‐α (TNF‐α) levels (OR 4.00, 95% CI 1.53–11.35; p < 0.001) were independent factors of bleeding in patients after CAR‐T treatment. The predictive model developed by Lasso regression, which selected factors such as CRS period, transfusion volume, platelet percentage, platelet count, thrombinogen time, interleukin 6, and TNF‐α levels, and showed Nomogram, yielded excellent agreement (C‐statistics = 0.905) with the calibration curve, which improved clinical benefit with respect to established bleeding scores such as outpatient bleeding risk index (MOBRI). External validation was performed using 39 patients from another hospital with an AUC of 0.700. Patients with severe bleeding after Car‐T therapy had increased the risk of death. A cross‐validated bleeding risk score based on CRS stages and TNF‐α level show significant prognostic value in patients undergoing CAR‐T treatment.

However, the association between CRS, TNF-α and bleeding during CAR T-cell therapy is unclear.
To determine the role of bleeding in patients receiving CAR Tcell therapy and to elucidate risk factors for this complication, we conducted a single-center real-world study in a Chinese population.
The prognostic value of bleeding was determined using multivariate adjusted survival analysis. Subsequently, we obtained a multivariate scoring model to predict the prognostic value of CAR T-cell therapyrelated bleeding, which was validated using a logistic regression model. In addition, the prognostic value of the bleeding risk score was also confirmed. The validation cohort included a separate series of 39 patients from another hospital.

| Study design and participants
We conducted a real-world study incorporating data from 400 pa-

| CRS grading system
The CRS stage was evaluated by the CAR T-cell therapy-associated Toxicity Working Group. 17

| Definition of bleeding class
Bleeding levels are determined according to World Health Organization scales. 18 QI ET AL. -65

| Assays for plasma cytokines
Peripheral blood specimens from patients treated with CAR T cells were collected and all specimens were treated with EDTA as an anticoagulant. The peripheral blood specimens were then centrifuged at 800�g for 10 min at room temperature to collect plasma. Plasma levels of IL-2, IL-4, IL-6, IL-10, IL-17A, TNF-α and IFN-γ were determined by flow cytometry using the corresponding antibodies (BD Biosciences) according to manufacturer's instructions. Details of this antibody are given in the supplementary material. Flow cytometry was performed using the BD FAC SCalibur system (BD Biosciences).

| Statistical analysis
Sample size assessment was performed using NCSS-PASS software (https://www.ncss.com/software/pass/) version 11.0. Power was set as 0.90 and alpha was 0.5. Mortality rates (0.40 and 0.029) for the severe and non-severe bleeding groups from previous data were entered into PASS. The actual hazard ratio was set to 10. Sample size was then calculated using PASS and the minimum sample size was found to be 233 (control group = 203 and experimental group = 30).
Our sample size of 400 (363 in the control group and 37 in the experimental group) is more appropriate. The sample size assessment report (Part II of supporting information S1) was also presented.
Missing data were estimated and a random forest algorithm using the mouse package in RStudio (R version 3.6.1). Continuous variables with skewed and normal distributions are expressed as median to interval mean and mean � standard deviation. The Mann-Whitney U test and unpaired t test were used for intergroup comparisons.
Categorical variables were expressed as percentages and compared using the κ 2 test. Cumulative mortality was shown using Kaplan-Meier curves and analyzed using the log-rank test. Univariate and multivariate survival analyses for total survival (OS) were assessed using Cox regression models. The significance of covariates on prognosis was visually analyzed using forest plots. Restricted stereo spline analysis was performed using Harrell's Regression Modelling Strategies (rms) package.
To build a bleeding risk model, Lasso regression was used to identify factors associated with bleeding. The contribution of each covariate was quantified and presented in the form of a Nomogram  (Table S2). When adjusted for age and sex, patients in the severe bleeding group also had a higher cumulative incidence of death compared with patients in the non-severe bleeding group (Table S2). After multivariate adjustment for all variants, it was statistically significant (P < 0.05) and the severe bleeding group was associated with a more than fourfold increased risk of death (HR 4.73, 95% CI 1.45-15.44; P < 0.01) (Table S3).
Severe bleeding as a fatal event after CAR T-cell therapy has shown significant prognostic value for multiple outcomes. Successful prediction may help prevent bleeding and suggests an improved prognosis after CAR T-cell therapy. Therefore, we modeled the as-  (Table S4). When adjusting for model-1 (age and sex) and model-2 (age, sex, HSCT, IL-6 knockout, BLAST percentage, CAR T type, diagnosis, CAR T-cell count and complete remission), TNF-α and CRS stages were also risk factors for CAR T treatment-related bleeding (Table S4). Multivariate regression was statistically significant (P < 0.05) for all variants, showing that stage III and IV CRS (OR 6.07, 95% CI 2.35-16.76; P < 0.001) and high TNF-α levels (OR 4.00, 95% CI 1.53-11.35; P < 0.001) were independent factors of bleeding in patients after CAR T treatment (Table 1). Without assuming a linear relationship, a logistic regression model was constructed using restrictive cubic splines to determine whether TNF-α levels affect the risk of severe bleeding and to show age-and sex-adjusted OR for severe bleeding, and TNF-α levels were found to be linearly corre-

| External validation of nomogram for CAR T associated bleeding
Nomogram was externally validated by the calibration plots in Figure S4 and by calculating bootstrap C statistics in an independent validation cohort of 39 patients. In the external validation step, Nomogram predicted a C index of 0.700 for CAR-T-related bleeding ( Figure S4A), indicating that the model is well discriminated. The calibration curve shows that the nomogram was well calibrated; the 5-year OS shows optimal agreement between the actual observations and the nomogram predicted values ( Figure S4B).  We used the machine learning method Lasso regression to screen for factors associated with severe bleeding, using a machine learning approach to control for collisionality, 23  almost always result in hematologic abnormalities, ranging from inconspicuous laboratory changes to severe DIC, 7 and it has long been known that inflammation can lead to activation of the coagulation system. Acute inflammation as a response to a serious infection or trauma can lead to systemic activation of the coagulation system and bleeding. 24 Endotoxins are lipopolysaccharide compounds of Gram-negative bacteria that induce sepsis syndrome and DIC. 25 In addition to the direct positive effect of endotoxins on tissue factor (TF) synthesis, synthesis of cytokines IL-1, TNF-α, and IL-6 all stimulate TF formation. 26 An in vitro study showed that TF mRNA production was rapidly induced in blood cells after injection of endotoxin into human volunteers 27 and in vivo, whole blood cells produced TF after incubation with endotoxin or IL-1, TNF-α and IL-6.

| DISCUSSION
In vitro, cultured human endothelial cells also induced TF after incubation with TNF-α and IL-1, but their role in DIC remains un- Bleeding as a fatal event after CAR T-cell therapy requires early prediction and intervention to reduce subsequent adverse events. To the best of our knowledge, no bleeding scores have been proposed for patients receiving CAR T-cell therapy. In contrast, bleeding scores, including MOBRI and HAS-BLED, have been established as a low-cost, convenient tool for cardiovascular care. The predictive role of these scores in CAR T-cell therapy-related bleeding remains unknown, considering the differences between patients receiving CAR T-cell therapy and those with cardiovascular disease. Through internal guidance, we developed a new bleeding score consisting of seven factors associated with this therapy, which in our study has a superior estimated clinical benefit in predicting bleeding risk compared to other bleeding scores. In our clinical work, we will predict the risk of bleeding by our constructed Nomogram for patients treated with CAR T. For patients with high risk of bleeding, we can increase the support of plasma and platelet transfusion and also reduce the patient's activity, thus preventing the occurrence of bleeding. The new score not only predicts bleeding, but also has a prognostic effect on OS in these patients. Our results highlight the need for a prognostic model for bleeding in patients receiving CAR Tcell therapy.

| CONCLUSIONS
In conclusion, our results show that CAR T-cell therapy-related bleeding predicts worse OS in patients. Bleeding grade provides these patients with additional predictive power beyond other factors.
A cross-validated multivariate score showed good agreement between bleeding grade and efficacy in predicting long-term prognosis in patients receiving CAR T-cell therapy.

CONFLICT OF INTEREST
The authors declare no competing financial interests.

DATA AVAILABILITY STATEMENT
All the data and materials are available if necessary. QI ET AL. -71

TRANSPARENT PEER REVIEW
The peer review history for this article is available at https://publons. com/publon/10.1002/hon.2931.