You are here

  • Home
  • Archive
  • Volume 11, Issue 10
  • Modification of Lugano criteria by pre-infusion tumor kinetics improves early survival prediction for patients with lymphoma under chimeric antigen receptor T-cell therapy

Article Text

Article menu
Immune cell therapies and immune cell engineering
Original research
Modification of Lugano criteria by pre-infusion tumor kinetics improves early survival prediction for patients with lymphoma under chimeric antigen receptor T-cell therapy
  1. Michael Winkelmann1,
  2. Viktoria Blumenberg2,3,
  3. Kai Rejeski2,3,
  4. Christina Quell1,
  5. Veit Bücklein2,3,
  6. Maria Ingenerf1,
  7. Marcus Unterrainer1,
  8. Christian Schmidt2,3,
  9. Franziska J Dekorsy4,
  10. Peter Bartenstein3,4,
  11. Jens Ricke1,3,
  12. Michael von Bergwelt-Baildon2,3,
  13. Marion Subklewe2,3 and
  14. Wolfgang G Kunz1,3
  1. 1Department of Radiology, University Hospital, LMU Munich, Munich, Germany
  2. 2Department of Hematology and Oncology, University Hospital Munich Campus Grosshadern, Munich, Germany
  3. 3German Cancer Consortium, Heidelberg, Germany
  4. 4Department of Nuclear Medicine, University Hospital, Munich, Germany
  1. Correspondence to Professor Wolfgang G Kunz; wolfgang.kunz{at}med.lmu.de

Abstract

Background Chimeric antigen receptor T-cell therapy (CART) is effective for patients with refractory or relapsed lymphoma with prolongation of survival. We aimed to improve the prediction of Lugano criteria for overall survival (OS) at 30-day follow-up (FU1) by including the pre-infusion tumor growth rate (TGRpre-BL) and its early change to 30-day FU1 imaging (TGRpost-BL).

Methods Consecutive patients with pre-baseline (pre-BL), baseline (BL) and FU1 imaging with CT or positron emission tomography/CT before CART were included. TGR was defined as change of Lugano criteria-based tumor burden between pre-BL, BL and FU1 examinations in relation to days between imaging examinations. Overall response and progression-free survival were determined based on Lugano criteria. Proportional Cox regression analysis studied association of TGR with OS. For survival analysis, OS was analyzed using Kaplan-Meier survival curves.

Results Fifty-nine out of 81 patients met the inclusion criteria. At 30-day FU1 8 patients (13.6%) had a complete response (CR), 25 patients (42.4%) a partial response (PR), 15 patients (25.4%) a stable disease (SD), and 11 patients (18.6%) a progressive disease (PD) according to CT-based Lugano criteria. The median TGRpre-BL was −0.6 mm2/day, 24.4 mm2/day, −5.1 mm2/day, and 18.6 mm2/day and the median TGRpost-BL was −16.7 mm2/day, −102.0 mm2/day, −19.8 mm2/day and 8.5 mm2/day in CR, PR, SD, and PD patients, respectively. PD patients could be subclassified into a cohort with an increase in TGR (7 of 11 patients (64%), PD TGRpre-to-post-BL INCR) and a cohort with a decrease in TGR (4 of 11 patients (36%), PD TGRpre-to-post-BL DECR) from pre-BL to post-BL. PD TGRpre-to-post-BL DECR patients exhibited similar OS to patients classified as SD, while PD TGRpre-to-post-BL INCR patients had significantly shorter OS (65 days vs 471 days, p<0.001).

Conclusion In the context of CART, the additional use of TGRpre-BL and its change to TGRpost-BL determined at 30-day FU1 showed better OS prognostication for patients with overall PD according to Lugano criteria. Therefore, this modification of the Lugano classification should be explored as a potential novel imaging biomarker of early response and should be validated prospectively in future studies.

  • lymphoma
  • immunotherapy
  • receptors, chimeric antigen

Data availability statement

Data are available upon reasonable request.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/jitc-2022-006659

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    WHAT IS ALREADY KNOWN ON THIS TOPIC

    • In contrast to tumor burden and characteristics at baseline (BL), the role of dynamic changes in tumor growth rate (TGR) per day from pre-BL to early post-BL imaging in the context of lymphoma under chimeric antigen receptor T-cell therapy (CART) has not been studied.

    WHAT THIS STUDY ADDS

    • Modifying the Lugano criteria by the change in TGR from pre-BL to post-BL improves early survival prediction at 30-day follow-up for patients classified as progressive disease per conventional Lugano criteria.

    HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

    • In the context of CART in later line lymphoma treatment, TGR is readily available and holds potential as a novel prognostic imaging biomarker for risk assessment and patient management.

    Background

    Chimeric antigen receptor T-cell therapy (CART) targeting the CD19 antigen has shown to be effective in relapsed or refractory (r/r) large B-cell lymphoma (LBCL), follicular lymphoma (FL), and mantle-cell lymphoma (MCL).1–6 Compared with previously established therapies, CART has significantly improved progression-free survival (PFS) and overall survival (OS). The efficacy of CART has been shown to be influenced by several factors. Among them are age, failure to bridging therapy, the International Prognostic Index (IPI), and the tumor burden (TB) of lymphoma as detected by imaging.7–10 Recently, a comparison of different methods for quantifying baseline TB in the context of CART was published.11

    In the first-line treatment setting of lymphoma, the influence of TB on outcome has been studied extensively.12 13 A more recently published study suggests a combination of the traditional IPI and metabolic tumor volume (MTV) to a new so called International Metabolic Prognostic Index (IMPI).14 This new index significantly outperformed IPI in patients with LBCL, indicating the prognostic value of TB. In the setting of CART and later lines of treatment, several trials demonstrated the impact of TB on efficacy, for example, in JULIET, ZUMA-1 and ZUMA-2, and preliminary data indicate that IMPI influences PFS after CART.2 4 5 15

    In contrast with the single-time point assessment of the baseline (ie, pre-infusion) TB, the role of the tumor growth rate (TGR) in the time interval prior to CART is not well understood.16 17 The TGR and its change pretreatment to post-treatment were originally studied in solid tumors treated with chemotherapy.16 18 In these studies, the change of TGR in the interval from pre-baseline to post-baseline was a better predictor of drug activity than conventional response criteria.16 18 In the context of immunotherapy, the pretreatment TGR was a strong prognostic biomarker for checkpoint inhibitor efficacy in metastatic melanoma.19 In the setting of later line lymphoma treatment, the TGR is readily available in patients with prior imaging during relapse and may allow a better understanding of the impact of tumor kinetics on the efficacy of CART.

    TGR and its per cent change post-infusion allow a more dynamic and quantitative assessment of TB kinetics than established response criteria. We therefore aimed to explore the additional prognostic value of the TGRpre-BL and its change over time on the efficacy of CART. We further studied how TGR may enhance the prognostic value of Lugano response criteria with regard to OS stratification.20

    Methods

    Study design and population

    The study population was based on a prospective registry of all consecutive patients who were treated at the Comprehensive Cancer Center Munich-Ludwig-Maximilian University Munich with commercialized CD19-specific CART products in between January 2019 and June 2022. The following inclusion criteria were applied:

    • Patients with r/r lymphoma (LBCL, transformed FL (tFL) and MCL).

    • Available CT or 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/CT imaging studies at baseline (BL; less than 2 weeks before CART), pre-baseline (pre-BL; more than 2 weeks and less than 6 months before BL), and follow-up (FU) 1 (30 days after CART infusion).

    • Any measurable disease on imaging according to Lugano criteria.21

    The following exclusion criteria were applied:

    1. Missing or incomplete imaging at pre-BL, BL or FU1.

    2. Any non-diagnostic imaging studies.

    3. No measurable disease on imaging according to Lugano criteria.21

    Histologic diagnoses were reviewed by expert pathologists. Patients received lymphodepletion with fludarabine and cyclophosphamide according to the manufacturers’ instructions. IPI was calculated using age, performance status, Ann Arbor stage, serum lactate dehydrogenase, and extranodal involvement.22

    Imaging and response assessment

    Imaging analysis was performed by consensus reading of two radiologists with 8 years (WGK) and 5 years (MW) of experience in radiology and nuclear medicine. The sum of the product of diameters (SPD) of up to six target lesions (TL) according to Lugano classification was measured at pre-BL, BL and FU1 imaging to represent tumor burden (TB). In case of 18F-FDG PET/CT imaging, the Deauville score was calculated to evaluate metabolic response. Deauville scores for a complete response (CR) were chosen according to Lugano criteria with a score of 1–3 defining a complete metabolic response.23 Spleen size was measured with splenomegaly being defined by a vertical length >13.0 cm. Response assessment was determined at 30-day FU1 CT based on Lugano criteria with the response categories of CR, partial response (PR), stable disease (SD), and progressive disease (PD). All imaging analyses were performed with dedicated trial reporting software mint Lesion V.3.8 (mint Medical GmbH; Heidelberg, Germany).

    Definition of TGR

    The TGR was defined as in the following formulas16 17:

    Embedded Image

    Embedded Image

    As a BL time point, the most recent imaging examination before CAR T-cell infusion was used. For the pre-BL time point, the last imaging examination before BL was applied, unless the time interval was less than 2 weeks or more than 6 months; this was intended to limit time bias in the calculation of TGR. The absolute and percentage change from TGRpre-BL to TGRpost-BL was calculated. For OS stratification analyses, patients were grouped according to their Lugano response category. For each response category, patients were grouped into a group with positive TGRpre-BL (TGRpre-BL POS) and negative TGRpre-BL (TGRpre-BL NEG). A second subdivision was performed into a patient cohort with an increase (TGRpre-to-post-BL INCR) and a decrease from TGRpre-BL to TGRpost-BL (TGRpre-to-post-BL DECR). A third subdivision was based on the extent of increase in TGR from pre-BL to post-BL, creating a patient group with at least 100% increase (TGRpre-to-post-BL≥100%) and a cohort with a decrease or an increase in TGR less than 100% (TGRpre-to-post-BL<100%).

    Analysis of PFS and OS

    PFS was defined from day one of CART to the day progression of lymphoma was detected on CT as defined by Lugano criteria and above. OS was defined from day one of CART to the day of any death related event (either lymphoma-related, treatment-related or any other cause).

    Statistical analysis

    All statistical analyses were performed using GraphPad Prism V.9. Proportional Cox regression analysis studied association of TGR with OS. Univariable and multivariable analysis was assessed by Cox logistic regression. For survival analysis, OS was visualized using Kaplan-Meier survival curves. Log-rank (Mantel-Cox) test was performed to examine the significance of the results. P values below 0.05 were considered to indicate statistical significance.

    Results

    Patient characteristics

    Fifty-nine out of 81 patients met the inclusion criteria (median age: 63 years, 39% women). Eighteen patients were excluded due to missing pre-BL imaging, two patients died before FU1 imaging, and two patients had an incomplete FU1 examination. A flow chart is provided in figure 1. Ten patients (16.9%) had stage I disease, 11 patients (18.6%) stage II, 8 patients (13.6%) stage III, and 30 patients (50.8%) stage IV according to the Ann Arbor staging system. IPI was 0 in 1 patient (1.7%), 1 in 13 patients (22.0%), 2 in 15 patients (25.4%), 3 in 16 patients (27.1%), 4 in 11 patients (19%), and 5 in 3 patients (5%). Forty-two out of 59 patients (71.2%) received a bridging therapy between apheresis and CAR T-cell infusion. Detailed patient characteristics are shown in table 1. Twenty-nine patients (49.2%) received no further therapy subsequent to CART, 17 patients (28.8%) had one, 8 patients (13.6%) had two, and 5 patients (8.5%) had three treatment lines post CART. Detailed information about the treatment post CART is provided in online supplemental table 1.

    Supplemental material

    Supplemental material

    View this table:
    Table 1

    Characteristics, patients (n=59)

    Figure 1
    Figure 1

    Flow chart. Illustrated is a flow chart for all 81 patients who were treated with CAR T-cell therapy Comprehensive Cancer Center Munich-Ludwig-Maximilian University Munich with CD19-specific CART products in between January 2019 and June 2022. Eighteen patients had to be excluded due to incomplete or missing pre-baseline imaging. Of the 63 included patients, 2 died before and 2 had incomplete FU1 imaging. CART, chimeric antigen receptor T-cell therapy; FU, follow-up.

    Lugano response at day 30 (FU1) and evolution of TGR pre-BL to post-BL

    At 30-day FU1 8 patients (13.6%) had a CR, 25 patients (42.4%) a PR, 15 patients (25.4%) a SD, and 11 patients (18.6%) a PD according to Lugano criteria. The median TGRpre-BL was −0.6 mm2/day, 24.4 mm2/day, −5.1 mm2/day, and 18.6 mm2/day and the median TGRpost-BL was −16.7 mm2/day, −102.0 mm2/day, −19.8 mm2/day and 8.5 mm2/day in CR, PR, SD, and PD patients, respectively. The evolution from TGRpre-BL to TGRpost-BL for each patient according to their Lugano response category is illustrated in figure 2. There was no significant difference in frequency of bridging therapy between the TGRpre-BL POS and TGRpre-BL NEG groups. Out of the 36 TGRpre-BL POS patients, 26 (72.2%) received bridging therapy. Out of the 23 TGRpre-BL NEG patients, 70.4% underwent bridging therapy.

    Figure 2
    Figure 2

    Change of TGR pre-to-post-baseline. Depicted are the absolute changes in tumor growth rate (TGR) from pre-baseline (pre-BL) to post-BL for the Lugano response categories complete response (CR; green), partial response (PR; orange), stable disease (SD; gray), and progressive disease (PD; red). TGR was defined as change of Lugano criteria-based tumor burden/sum of the product diameters in mm2 between pre-BL and BL (TGRpre-BL) or BL and FU1 (TGRpost-BL) examinations in relation to days (d) between imaging examinations.

    Patient examples

    An example of three evolutions from TGRpre-BL to TGRpost-BL is depicted in figure 3. Patient CR first showed a significant increase in TB from pre-BL to BL with positive TGRpre-BL, yet experienced a −418% decrease in TGR after the initiation of CART. The second patient PD1 initially had a slight decrease in TGR from pre-BL to post-BL, however, after CAR T-cell infusion the TGR increased by +322%. Patient PD2 demonstrated a very similar TGRpre-BL as patient CR but had a comparable per cent increase in TGR of +331% after CART infusion as patient PD1. Patients CR, PD1, and PD2 had an OS of 374 days (ongoing), 121 days, and 25 days, respectively.

    Figure 3
    Figure 3

    Patient examples of changes in TGR. (A) Shows three patient examples of changes in tumor growth rate (TGR). One of the patients had a complete response (CR, green) and two patients had a progressive disease (PD1, red and PD2, black) according to Lugano criteria at 30-day follow-up (FU1). The slope in the graph corresponds to the TGR pre- (TGRpre-BL) and post-baseline (TGRpost-BL) as shown in the legend of the graph. In addition, the percentage change of TGRpost-BL to TGRpre-BL was calculated. (B) Illustrates the main lymphoma manifestations of the same three patients in the course of CAR T-cell therapy from pre-baseline to 30-day FU1.

    Modification of Lugano criteria by TGR

    Out of the eight patients with CR at 30-day FU1, four patients had a positive and four had a negative TGRpre-BL. Of the four CR TGRpre-BL POS patients, TGR decreased (TGRpre-to-post-BL DECR) in all patients and did not increase in any of the patients, whereas it increased in three of the TGRpre-BL-NEG patients (TGRpre-to-post-BL INCR) and decreased in one patient post-BL. Among the 25 Lugano PR patients, 19 patients had positive and six had negative TGRpre-BL. All 19 PR TGRpre-BL POS decreased in TGR after initiation of therapy. One of the TGRpre-BL NEG increased and five decreased in TGR post-BL. None of the CR and PR patients experienced an increase of ≥100%. Among patients with SD, six patients had positive and nine had negative TGR before BL. Out of the 11 PD patients, 7 had a positive and 4 had a negative TGRpre-BL. In all four TGRpre-BL NEG patients with PD, TGR increased. Among the TGRpre-BL POS PD patients, TGR decreased post-BL in four patients and increased in three patients. In total, one patient with SD and seven patients with PD increased by more than 100% from TGRpre-BL to TGRpost-BL. A flow diagram is provided in figure 4.

    Figure 4
    Figure 4

    Modification of Lugano criteria by TGR. The left panel shows the number and percentage of patients according to their Lugano response category at 30-day follow-up with complete response (CR; green), partial response (PR; orange), stable disease (SD; gray), and progressive disease (PD; red). A first subdivision was performed into groups with positive (TGRpre-BL POS) and negative (TGRpre-BL NEG) pre-baseline (pre-BL) tumor growth rate (TGR) for each response category. A second subdivision was performed into a patient cohort with an increase (TGRpre-to-post-BL INCR) and a decrease from TGRpre-BL to TGRpost-BL (TGRpre-to-post-BL DECR). A third subdivision was based on the extent of increase in TGR from pre-baseline to post-baseline, creating a patient group with at least 100% increase (TGRpre-to-post-BL≥100%) and a cohort with a decrease or an increase in TGR less than 100% (TGRpre-to-post-BL<100%). The number of redistributed patients is indicated on the left within the bars between the boxes.

    Survival analysis

    Both CR TGRpre-to-post-BL INCR and TGRpre-to-post-BL DECR did not reach median PFS and OS. Among PR patients TGRpre-to-post-BL INCR showed a shorter median PFS with 343 days compared with TGRpre-to-post-BL DECR patients with 641 days. Both PR groups did not reach median OS. Interestingly, SD TGRpre-to-post-BL INCR patients had a longer PFS (97 days vs 56 days) and OS (384 days vs 126 days) than TGRpre-to-post-BL DECR patients. Subdividing the group with PD, patients with PD TGRpre-to-post-BL INCR showed a comparable PFS to PD TGRpre-to-post-BL DECR patients (30 days vs 39 days), but a significant shorter median OS (65 days vs 471 days). The data is illustrated in table 2.

    View this table:
    Table 2

    Association of pre-BL to post-BL TGR with PFS and OS

    In general, the Lugano criteria at day 30 FU1 performed well for OS stratification (figure 5A; p<0.001). Interestingly, when PD patients were subdivided according to their TGRpre-BL, those with a positive TGRpre-BL showed longer OS compared with the TGRpre-BL NEG group (figure 5B; p<0.001). The most interesting result was observed when PD patients were divided into a cohort with an increase in TGR (PD TGRpre-to-post-BL INCR) and a second cohort with a decrease in TGR (PD TGRpre-to-post-BL DECR) from pre- to post-BL. PD TGRpre-to-post-BL DECR exhibited similar OS to patients classified as SD, while PD TGRpre-to-post-BL INCR had significantly shorter OS (65 days vs 471 days; figure 5C; p<0.001). Further, increasing the threshold for TGR pre-BL to post-BL change showed no additional benefit in OS stratification, as shown for a cut-off of 150% (figure 5D).

    Figure 5
    Figure 5

    Overall survival stratification with TGR-modified Lugano criteria at 30 days. Analysis of overall survival (OS) by 30-day Lugano overall response in CT imaging modified by tumor growth rate (TGR). (A) Depicts Kaplan-Meier curves for OS (p<0.001) by Lugano response category. (B) Shows OS data (p<0.001) with modification of progressive disease (PD) category by grouping patients into a group with positive pre-baseline (PD TGRpre-BL POS) and negative pre-baseline TGR (PD TGRpre-BL NEG). (C) Demonstrates OS analysis (p<0.001) by separation of PD patients to a cohort with an increase of TGR≥100% (PD TGRpre-to-post-BL INCR) and a second cohort with increase <100% or decrease in TGR (PD TGRpre-to-post-BL DECR) from pre-baseline to post-baseline, while (D) depicts a dichotomization by 150% increase in TGR (p<0.001). Median follow-up was 294 days. HRs are shown in boxes with 95% CI.

    To check if baseline characteristics also had an impact on survival (online supplemental figure 1), we first dichotomized patients by median SPD at baseline. Here, we could not detect any significant difference in PFS (p=0.271) or OS (p=0.638) between the two groups (online supplemental figure 1A,B). In the next step, we determined a cut-off at 1510 mm2 for the baseline tumor burden in our cohort. Patients with a tumor burden below this threshold had a significantly longer median PFS (p=0.008), which was not reached compared with patients with a higher tumor burden that had a median PFS of 94 days (online supplemental figure 1). In addition, there was a difference in OS (online supplemental figure 1D), which was however not significant in the statistical analysis (p=0.109). Grouping patients according to the median lactate dehydrogenase (LDH) at lymphodepletion (online supplemental figure 1E+F) showed a significant difference in terms of PFS (p=0.002) and minor differences in median OS, which was statistically not significant (p=0.090).

    Supplemental material

    Results for PFS are shown in online supplemental figure 2. Patients with CR at 30-day FU showed longest median PFS (not reached) compared with PR (641 days), SD (94 days), and PD patients (32 days). As Lugano criteria were used to assess PFS as described in the methods PD occurred at day 30 FU and further stratification by pre-BL TGR was not possible (online supplemental figure 2).

    Supplemental material

    Additional survival analyses evaluating the impact of bridging on OS (online supplemental figure 3) and the role of lymphoma entity (online supplemental figure 4) were performed. Interestingly, the impact of a change in TGR from pre-BL to post-BL was more pronounced in the bridging group (online supplemental figure 3). As there were no progressive patients in the patients with MCL included in this study the effects of pre-BL TGR, as described above, can be attributed to the patients with LBCL alone (online supplemental figure 4).

    Supplemental material

    Supplemental material

    Multivariable analysis

    Cox logistic regression was performed to test the significance of BL parameters and the effect of pre-BL TGR on OS. Univariable and bivariable analyses showed significant association of change of TGR from pre-BL to post-BL and Eastern Cooperative Oncology Group (ECOG). Most BL parameters like BL SPD, age, Ann Arbor stage, LDH, and IPI had no significant association with OS both in univariate and multivariate Cox regression. The results for multivariable analysis are shown in online supplemental table 2. The only two parameters that showed a significant association with OS in multivariable analysis were absolute change of TGR from pre-BL to post-BL (p=0.001) and ECOG performance status (p=0.007).

    Supplemental material

    Discussion

    In our study conducted in the context of CAR T-cell therapy for patients with r/r non-Hodgkin’s lymphoma (NHL), early change in TGR from pre-BL to post-BL imaging showed a significant association with survival. Modification of the Lugano criteria by the change in TGR from pre-BL to post-BL improves early survival prediction at 30-day FU for patients classified as PD per conventional Lugano criteria. Future studies on patients with lymphoma undergoing CART should prospectively assess the value of these early changes in TGR as a potential new imaging biomarker for early response and survival prediction.

    In first-line treatment of lymphoma, an association has been established between imaging endpoint surrogates for survival such as PFS and OS.24 25 In addition, the Lugano criteria are the most widely used criteria for response classification.21 In the setting of CAR-T cell therapy, there are very few studies examining the association of imaging-based response on clinical outcome and survival. A study of a small prospective cohort of seven patients with LBCL and FL treated with CD19 CART assessed early response according to the Lugano criteria. Here, all patients with less than a CR on 30-day PET/CT imaging subsequently relapsed.26

    In our study, some patients with less than a CR did not relapse, but patients with a PR at day 30 had a significantly shorter PFS and OS than those with a CR. Another multicenter study with 171 patients with NHL under CART analyzed response prediction by Deauville score at day 30.27 In this study patients with Deauville score 1+2 had an improved long-term outcome compared with patients with score 3–5, who were at risk for an early relapse.27 Similar results were shown in other studies.28 29 In a further report, the predictive value of established and various exploratory response criteria such as Response Evaluation Criteria In Lymphoma (RECIL)30 and Lymphoma Response to Immunomodulatory Therapy Criteria (LYRIC)31 was compared in patients with NHL in the context of CART at 90-day FU, and they were all shown to have prognostic value.11

    In our study, a 30-day response according to the Lugano criteria was already a good prognostic factor for OS. An interesting study examined the prognostic information of absolute difference in metabolic tumor volume (MTV) from time of decision to time of treatment.32 This study showed that ΔMTVpre-CAR+300% and ΔTLGpre-CAR+420% were found to be predictive for PFS, while there was no association with OS. However, in contrast to our study, the effect of dynamic TGR before infusion as tumor growth per time interval was not examined.

    In contrast, several studies have been conducted in solid tumors treated with ICI to evaluate the impact of pre-BL TGR on outcome and response prediction according to Response Evaluation Criteria in Solid Tumours (RECIST) version 1.1.16 18 19 33 34 One of these studies revealed that 82% of patients with SD and 65% of patients with PD had a decrease in TGR from pretreatment to post-treatment, which was also associated with longer PFS. Interestingly, there patients would still be considered non-responders according to RECIST criteria.18

    Another study observed an acceleration of TGR during treatment in 38% of patients classified as non-progressive and, conversely, a decline in TGR in 53% of patients classified as PD.16 Both studies demonstrated a discrepancy between established response criteria and pre-BL TGR.16 18 In our study, we could show that none of the CR patients and only one PR and one SD patient had an increase in TGR from pre-BL to post-BL. In contrast, 4 of 11 patients with PD had an increase in TGR at 30-day FU (36%), which was also associated with significantly shorter OS compared with progressive patients who had a decrease in TGR.

    Champiat and colleagues studied TGR in a cohort of 131 patients with various solid tumors undergoing ICI therapy. Within this cohort, they were able to identify a group of 9% of all patients with increase in TGR greater than or equal to twofold on therapy, which they termed hyperprogressive disease (HPD). HPD was associated with worse OS.34 Kas et al also analyzed HPD in a subset of 406 patients with non-small cell lung cancer treated with ICI. In this study, different definitions were used and HPD ranged from 5.4% (progression pace greater than twofold and a time to treatment failure of <2 months) and 18.5% (definition based on TGR ratio).35 Another study distinguished a fast progression group as a special subgroup in addition to HPD, both of which are associated with shortened survival.36 In our study, 14% of patients had an increase in TGR ≥100% on treatment, corresponding to the proposed twofold change in HPD definition. All these patients had an LBCL. Interestingly, 88% of these patients had a PD, 12% had a SD, and none had a CR or PR according to the Lugano criteria.

    Interestingly, patients with SD TGRpre-to-post-BL DECR patients had a shorter PFS and OS than the SD TGRpre-to-post-BL INCR group as shown in table 2.This might reflect no response to bridging or uncontrolled disease at the time of CAR T-cell therapy. On the other hand, the PD TGRpre-to-post-BL DECR group could also reflect no or an inadequate response to bridging therapy and this group still shows a longer median PFS and OS compared with the PD TGRpre-to-post-BL INCR patients. Another point is that many SD patients had smaller changes in absolute TGR, hence changes in tumor burden for calculating the pre-to-post BL TGR might not be as reliable as in other groups.

    To our knowledge, there is no literature analyzing the dynamic changes of TGR from pre-BL to early post-BL imaging in the context of lymphoma under CART. Our study has limitations which need to be considered. First, this is a single-center study with a limited number of subjects. This may limit the interpretation of the association of TGR with OS. Second, some patients had to be excluded as there was no measurable disease on CT, which also represents a limitation of imaging-based prognostic indices in clinical routine. Third, resulting from the operational and logistical nature of CART, the clinical use of bridging therapy affects the TGR. Fourth, some patients only had a CT without PET at 30-day FU, hence there may be a possible redistribution of some patients among the Lugano response categories, especially patients with a PD which might have a PR or CR according to PET-based Lugano criteria.

    To conclude, the early dynamic change in TGR from pre-BL to 30-day FU imaging showed prognostic value for OS stratification by modifying Lugano criteria, especially regarding patients with a PD. In the context of CART in later line lymphoma treatment, TGR is readily available and holds potential as a novel prognostic imaging biomarker. Future research should prospectively assess the value of pre-BL TGR in larger patients samples, its early change to 30-day FU, and possible benefits from such early response biomarkers in terms of outcome and survival prediction in patients under CART.

    Data availability statement

    Data are available upon reasonable request.

    Ethics statements

    Patient consent for publication

    Ethics approval

    All medical records and imaging studies were reviewed with the approval of the LMU Munich Institutional Review Board (LMU Ethics Committee, project number 19-817). Participants gave informed consent to participate in the study before taking part.

    References

    1. ↵
      1. June CH,
      2. Sadelain M
      . Chimeric antigen receptor therapy. N Engl J Med 2018;379:64–73. doi:10.1056/NEJMra1706169
      OpenUrlCrossRefPubMed
    2. ↵
      1. Schuster SJ,
      2. Bishop MR,
      3. Tam CS, et al
      . Tisagenlecleucel in adult Relapsed or refractory diffuse large B-cell lymphoma. N Engl J Med 2019;380:45–56. doi:10.1056/NEJMoa1804980
      OpenUrlCrossRefPubMed
    3. ↵
      1. Schuster SJ,
      2. Svoboda J,
      3. Chong EA, et al
      . Chimeric antigen receptor T cells in refractory B-cell Lymphomas. N Engl J Med 2017;377:2545–54. doi:10.1056/NEJMoa1708566
      OpenUrlCrossRefPubMed
    4. ↵
      1. Neelapu SS,
      2. Locke FL,
      3. Bartlett NL, et al
      . Axicabtagene Ciloleucel CAR T-cell therapy in refractory large B-cell lymphoma. N Engl J Med 2017;377:2531–44. doi:10.1056/NEJMoa1707447
      OpenUrlCrossRefPubMed
    5. ↵
      1. Wang M,
      2. Munoz J,
      3. Goy A, et al
      . KTE-X19 CAR T-cell therapy in Relapsed or refractory Mantle-cell lymphoma. N Engl J Med 2020;382:1331–42. doi:10.1056/NEJMoa1914347
      OpenUrlCrossRefPubMed
    6. ↵
      1. Abramson JS,
      2. Palomba ML,
      3. Gordon LI, et al
      . Lisocabtagene Maraleucel for patients with Relapsed or refractory large B-cell Lymphomas (TRANSCEND NHL 001): a Multicentre seamless design study. Lancet 2020;396:839–52. doi:10.1016/S0140-6736(20)31366-0
      OpenUrlCrossRefPubMed
    7. ↵
      1. Bethge WA,
      2. Martus P,
      3. Schmitt M, et al
      . GLA/DRST real-world outcome analysis of CAR-T cell therapies for large B-cell lymphoma in Germany. Blood 2022;140:349–58. doi:10.1182/blood.2021015209
      OpenUrlCrossRef
    8. ↵
      1. Garcia-Recio M,
      2. Wudhikarn K,
      3. Pennisi M, et al
      . The International Prognostic index is associated with outcomes in diffuse large B cell lymphoma after Chimeric antigen receptor T cell therapy. Transplant Cell Ther 2021;27:233–40. doi:10.1016/j.jtct.2020.10.022
      OpenUrl
    9. ↵
      1. Dean EA,
      2. Mhaskar RS,
      3. Lu H, et al
      . High metabolic tumor volume is associated with decreased efficacy of Axicabtagene Ciloleucel in large B-cell lymphoma. Blood Adv 2020;4:3268–76. doi:10.1182/bloodadvances.2020001900
      OpenUrlCrossRef
    10. ↵
      1. Iacoboni G,
      2. Simó M,
      3. Villacampa G, et al
      . Prognostic impact of total metabolic tumor volume in large B-cell lymphoma patients receiving CAR T-cell therapy. Ann Hematol 2021;100:2303–10. doi:10.1007/s00277-021-04560-6
      OpenUrl
    11. ↵
      1. Winkelmann M,
      2. Bücklein VL,
      3. Blumenberg V, et al
      . Lymphoma tumor burden before Chimeric antigen receptor T-cell treatment: RECIL vs. Front Oncol 2022;12:974029. doi:10.3389/fonc.2022.974029
    12. ↵
      1. Meignan M,
      2. Cottereau AS,
      3. Specht L, et al
      . Total tumor burden in lymphoma - an evolving strong Prognostic parameter. Br J Radiol 2021;94:20210448. doi:10.1259/bjr.20210448
    13. ↵
      1. Gobbi PG
      . Tumor burden in Hodgkin's lymphoma: much more than the best Prognostic factor. Crit Rev Oncol Hematol 2014;90:17–23. doi:10.1016/j.critrevonc.2013.11.002
      OpenUrlCrossRefPubMed
    14. ↵
      1. Mikhaeel NG,
      2. Heymans MW,
      3. Eertink JJ, et al
      . Proposed new dynamic Prognostic index for diffuse large B-cell lymphoma: International metabolic Prognostic index. J Clin Oncol 2022;40:2352–60. doi:10.1200/JCO.21.02063
      OpenUrl
    15. ↵
      1. Winkelmann M,
      2. Blumenberg V,
      3. Rejeski K, et al
      . Prognostic value of the International metabolic Prognostic index for lymphoma patients receiving Chimeric antigen receptor T-cell therapy. Eur J Nucl Med Mol Imaging 2023;50:1406–13. doi:10.1007/s00259-022-06075-2
      OpenUrl
    16. ↵
      1. Gomez-Roca C,
      2. Koscielny S,
      3. Ribrag V, et al
      . Tumour growth rates and RECIST criteria in early drug development. Eur J Cancer 2011;47:2512–6. doi:10.1016/j.ejca.2011.06.012
      OpenUrlCrossRefPubMed
    17. ↵
      1. Winkelmann M,
      2. Blumenberg V,
      3. Rejeski K, et al
      . Prognostic value of pre-infusion tumor growth rate for patients with lymphoma receiving Chimeric antigen receptor T-cell therapy. Cytotherapy 2023;25:986–92. doi:10.1016/j.jcyt.2023.03.007
      OpenUrl
    18. ↵
      1. Ferté C,
      2. Fernandez M,
      3. Hollebecque A, et al
      . Tumor growth rate is an early indicator of antitumor drug activity in phase I clinical trials. Clin Cancer Res 2014;20:246–52. doi:10.1158/1078-0432.CCR-13-2098
      OpenUrlAbstract/FREE Full Text
    19. ↵
      1. Wagner NB,
      2. Lenders MM,
      3. Kühl K, et al
      . Pretreatment metastatic growth rate determines clinical outcome of advanced Melanoma patients treated with anti-PD-1 antibodies: a multicenter cohort study. J Immunother Cancer 2021;9:e002350. doi:10.1136/jitc-2021-002350
    20. ↵
      1. Winkelmann M,
      2. Rejeski K,
      3. Blumenberg V, et al
      . Chimeric antigen receptor T-cell therapy: imaging response criteria and relation to progression-free and overall survival. Hemasphere 2022;6:e781. doi:10.1097/HS9.0000000000000781
    21. ↵
      1. Cheson BD,
      2. Fisher RI,
      3. Barrington SF, et al
      . Recommendations for initial evaluation, staging, and response assessment of Hodgkin and non-Hodgkin lymphoma: the Lugano classification. J Clin Oncol 2014;32:3059–68. doi:10.1200/JCO.2013.54.8800
      OpenUrlAbstract/FREE Full Text
    22. ↵
      1. International Non-Hodgkin’s Lymphoma Prognostic Factors Project
      A predictive model for aggressive non-Hodgkin’s lymphoma. N Engl J Med 1993;329:987–94. doi:10.1056/NEJM199309303291402
      OpenUrlCrossRefPubMedWeb of Science
    23. ↵
      1. Zhang XY,
      2. Song L,
      3. Wang PJ, et al
      . Prognostic value of pre-Autologous stem cell transplantation PET/CT in diffuse large B-cell lymphoma: the Deauville score is Prognostically superior to Deltasuvmax. Acta Haematol 2020;143:124–30. doi:10.1159/000500512
      OpenUrl
    24. ↵
      1. Batlevi CL,
      2. Younes A
      . Surrogate end points in lymphoma. Ann Oncol 2018;29:1622–3. doi:10.1093/annonc/mdy219
      OpenUrl
    25. ↵
      1. Zhu J,
      2. Yang Y,
      3. Tao J, et al
      . Association of progression-free or event-free survival with overall survival in diffuse large B-cell lymphoma after Immunochemotherapy: a systematic review. Leukemia 2020;34:2576–91. doi:10.1038/s41375-020-0963-1
      OpenUrlCrossRef
    26. ↵
      1. Shah NN,
      2. Nagle SJ,
      3. Torigian DA, et al
      . Early positron emission tomography/computed tomography as a Predictor of response after Ctl019 Chimeric antigen receptor -T-cell therapy in B-cell non-Hodgkin Lymphomas. Cytotherapy 2018;20:1415–8. doi:10.1016/j.jcyt.2018.10.003
      OpenUrl
    27. ↵
      1. Kuhnl A,
      2. Roddie C,
      3. Kirkwood AA, et al
      . Early FDG-PET response predicts CAR-T failure in large B-cell lymphoma. Blood Adv 2022;6:321–6. doi:10.1182/bloodadvances.2021005807
      OpenUrl
    28. ↵
      1. Derlin T,
      2. Schultze-Florey C,
      3. Werner RA, et al
      . 18)F-FDG PET/CT of off-target Lymphoid organs in Cd19-targeting Chimeric antigen receptor T-cell therapy for Relapsed or refractory diffuse large B-cell lymphoma. Ann Nucl Med 2021;35:132–8. doi:10.1007/s12149-020-01544-w
      OpenUrl
    29. ↵
      1. Cohen D,
      2. Luttwak E,
      3. Beyar-Katz O, et al
      . 18)F]FDG PET-CT in patients with DLBCL treated with CAR-T cell therapy: a practical approach of reporting Pre- and post-treatment studies. Eur J Nucl Med Mol Imaging 2022;49:953–62. doi:10.1007/s00259-021-05551-5
      OpenUrl
    30. ↵
      1. Younes A,
      2. Hilden P,
      3. Coiffier B, et al
      . International working group consensus response evaluation criteria in lymphoma (RECIL 2017). Ann Oncol 2017;28:1436–47. doi:10.1093/annonc/mdx097
      OpenUrlCrossRefPubMed
    31. ↵
      1. Cheson BD,
      2. Ansell S,
      3. Schwartz L, et al
      . Refinement of the Lugano classification lymphoma response criteria in the era of immunomodulatory therapy. Blood 2016;128:2489–96. doi:10.1182/blood-2016-05-718528
      OpenUrlAbstract/FREE Full Text
    32. ↵
      1. Sesques P,
      2. Tordo J,
      3. Ferrant E, et al
      . Prognostic impact of 18F-FDG PET/CT in patients with aggressive B-cell lymphoma treated with anti-Cd19 Chimeric antigen receptor T cells. Clin Nucl Med 2021;46:627–34. doi:10.1097/RLU.0000000000003756
      OpenUrl
    33. ↵
      1. Champiat S,
      2. Ferrara R,
      3. Massard C, et al
      . Hyperprogressive disease: recognizing a novel pattern to improve patient management. Nat Rev Clin Oncol 2018;15:748–62. doi:10.1038/s41571-018-0111-2
      OpenUrlPubMed
    34. ↵
      1. Champiat S,
      2. Dercle L,
      3. Ammari S, et al
      . Hyperprogressive disease is a new pattern of progression in cancer patients treated by anti-PD-1/PD-L1. Clin Cancer Res 2017;23:1920–8. doi:10.1158/1078-0432.CCR-16-1741
      OpenUrlAbstract/FREE Full Text
    35. ↵
      1. Kas B,
      2. Talbot H,
      3. Ferrara R, et al
      . Clarification of definitions of Hyperprogressive disease during Immunotherapy for non-small cell lung cancer. JAMA Oncol 2020;6:1039–46. doi:10.1001/jamaoncol.2020.1634
      OpenUrl
    36. ↵
      1. Ferrara R,
      2. Mezquita L,
      3. Texier M, et al
      . Comparison of fast-progression, Hyperprogressive disease, and early deaths in advanced non-small-cell lung cancer treated with PD-1/PD-L1 inhibitors or chemotherapy. JCO Precis Oncol 2020;4:829–40. doi:10.1200/PO.20.00021
      OpenUrl

    Supplementary materials

    Footnotes

    • Twitter @KRejeski, @WolfgangGKunzMD

    • Contributors MW and WGK conceived and designed the study. VBl, KR, CQ, VBü, MI, MU, and CS collected the data. MW, VBl, KR, CQ, VBü, and WGK analyzed and interpreted the data. MW and WGK drafted the manuscript. WGK is the guarantor of the study. VBl, KR, MI, FJD, PB, JR, MvB-B, and MS revised the manuscript.

    • Competing interests VBl: BMS/Celgene: Research Funding; Kite/Gilead: Consultancy, Honoraria, Research Funding; Janssen: Research Funding, Honoraria; Novartis: Research Funding, Honoraria,; Roche: Research Funding; Takeda: Research Funding. KR: Kite/Gilead: Research Funding; Kite/Gilead: Travel Support; Novartis: Honoraria. VBü: Amgen: Honoraria; Celgene/BMS: Research Funding; Kite/Gilead: Research Funding, Honoraria; Novartis: Honoraria; Pfizer: Honoraria. CS: Kite/Gilead: Travel Support. MvB-B: Astellas: Consultancy, Research Funding and Honoraria; BMS: Consultancy, Research Funding and Honoraria; Kite/Gilead: Consultancy, Research Funding and Honoraria; Miltenyi: Consultancy, Research Funding and Honoraria; Mologen: Consultancy, Research Funding and Honoraria; MSD Sharp & Dohme: Consultancy, Research Funding and Honoraria; Novartis: Consultancy, Research Funding and Honoraria; Roche: Consultancy, Research Funding and Honoraria. MS: Amgen: Research Funding, Speakers Bureau; AstraZeneca: Speakers Bureau; Aven Cell: Consultancy, BMS/Celgene: Research Funding, Speakers Bureau; CDR-Life: Consultancy, Gilead: Research Funding, Speakers Bureau; GSK: Speakers Bureau; Ichnos Sciences: Consultancy; Incyte Biosciences: Consultancy; Janssen: Research Funding, Consultancy, Speakers Bureau; Miltenyi Biotec: Research Funding, Consultancy; Morphosys: Research Funding; Molecular Partners: Consultancy; Novartis: Research Funding, Consultancy, Speakers Bureau; Pfizer: Consultancy, Speakers Bureau; Roche: Research Funding, Speakers Bureau; Seattle Genetics: Research Funding; Takeda: Research Funding, Consultancy, Speakers Bureau. WGK: Bristol Myers Squibb: Advisor. The remaining authors declare no competing financial interests. None of the mentioned conflicts of interest were related to financing of the content of this manuscript.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.