Progression of Disease Within 24 Months (POD24) Based Prognostic Signature: A New Method for Personalized Risk Assessment in Patients with Mantle Cell Lymphoma

doi:10.21203/rs.3.rs-4895296/v1

Mantle cell lymphoma (MCL) exhibits significant biological and clinical heterogeneity, necessitating a refined prognostic model. According to the drawback of the models existed which are not truly define the complexity of the disease, we used the clinical and molecular data from nine medical centers of China to validate the predictive utility in progression of disease within 24 months (POD24), and also establish a novel prognostic risk model to predict the survival outcome of MCL patients. POD24 occurred in 37.7% of evaluable patients, with median over survival was 21 months (vs 122 months for those without POD24, P < 0.0001). The POD24-based risk model had the highest sensitivity to predict to predict survival with the most satisfying AUC value for risk score (AUC = 0.869). In conclusion, we confirm the obviously predictive performance of POD24 and established a novel risk model combined POD24 and clinical factors. Our new prognostic model might be helpful to effectively classify MCL patients with high-risk groups in terms of survival rate, which may help select high-risk MCL patients for more intensive treatment at time of relapse.

mantle cell lymphoma

progression of disease within 24 months

prognostic

clinical factor

Mantle cell lymphoma (MCL) is a unique subcategory of B cell malignancy disease accounting for 6–7% of non-Hodgkin lymphomas (NHL) and is characterized by the overexpression of the cell cycle regulatory protein cyclin D1^1–3. This lymphoma subtype is considered an incurable disease showing a generally aggressive, albeit heterogeneous and clinical course³. The prognosis of MCL after relapse is generally poor, and no standard of treatment guidance exists with a median survival time of approximately 5 years². MCL is a disease of older individuals with the median age at diagnosis was 70 years, and is more common in male than female (male/female ratio, 3:1)⁴. Additionally, MCL has a heterogeneous clinical course, including a subgroup of patients with long survival in different treatment but without a striking unfavorable effect on their outcomes⁵. The majority of patients are diagnosed with widespread disease and is characterized by frequent recurrences⁶. Most of MCL patients respond to the current standard of care but often relapse or refractory due to drug resistance⁷. At time of relapse, no standard of treatment guidance exists and the prognosis after relapse is generally poor despite the increasing novel treatment options, such as BTK inhibitors⁸.

Well-established prognostic factors such as the MCL International Prognostic Index (MIPI) and the proliferative activity (Ki-67) classifies MCL patients into different risk groups, but have not been useful to predict the prognosis of identify indolent disease patients^9,10. Other relevant factors such as karyotypic complexity, genetic aberrations and DNA methylation are well verified as independent prognostic factors for MCL outcome⁸. Moreover, in the era of newer therapies such as proteasome inhibitors, immunomodulatory drugs, tyrosine kinase inhibitors and Chimeric Antigen Receptor T (CAR-T) cells, the relevance of these prognostic factors is largely inefficient to accurate definition of expected response and survival rates for relapsed or refractory (R/R) MCL patients^8,11. Therefore, there is an urgent necessity to develop innovative biomarkers for individual prognosis and risk stratification, enabling a precise estimation of survival outcomes.

POD within 2 years (POD24) of diagnosis has been identified as a robust predictor of poor survival in lymphoma disease, although it is unknown at diagnosis or induction regimen administered^12–14. POD24 can assist in selecting second line treatments in relapsed patients, which defined high-risk group of patients in whom further study is warranted in both directed prospective clinical trials of biology and treatment¹⁵. Likewise, the observation that patients with relapse over two years have prolonged survival expectation in the novel treatment era seems of great importance in the clinical management of MCL patients^13,15.

Due to its rarity and the great diversity in clinical behavior of MCL, it has been difficult to predicting the prognosis and the heterogeneity of clinical of MCL¹⁶. The aim of this study was to describe the clinical features and clinical outcomes of MCL patients in China from nine cancer centers, both confirming the prognostic impact of progression of disease within 24 months (POD24) and highlight the relevance of other clinical biomarkers in predicting the survival of MCL patients. More importantly, a first-ever prognostic model based on POD24 and clinical factors was provided to stratify MCL patients into different survival outcomes, for which might be helpful to allow individualized, risk-adapted treatment decisions in patients with advanced stage of MCL.

Clinical features and characteristics

Of 979 total patients with MCL, 369 patients were in POD ≤ 24 months group and 610 patients in POD>24 months (Non-POD24) group. Clinical features and characteristics for different POD groups are shown in Table 1.

Table 1

**Clinical Features of 979 Patients with MCL according to POD24 status**
Features	POD24 (n = 369)	Non-POD24(n = 610)	P- value
Age(y)			0.066
≥ 60	202	297
< 60	167	313
Sex			0.823
male	283	464
female	86	146
B symptom			0.003
Yes	132	167
No	225	433
LDH			0.000
Elevated	164	166
normal	188	428
ECOG			0.005
>=2	276	73
< 2	66	511
Extranodal sites			0.171
≥ 2	66	89
<2	303	521
HBsAg			0.96
Positive	38	62
Negative	311	513
Ann Abor Stage			0.076
I or II	32	76
III or IV	329	528
MIPI scores			0.000
≥ 6	110	103
<6	218	482
Splenomegaly			0.000
Yes	193	249
No	163	349
Ki67			0.000
<=30%	291	112
> 30%	212	162
na	107	95
ASCT			0.037
Yes	68	26
No	540	340

MCL: Mantle cell lymphoma; POD24: Progression of disease within 24 months; LDH: Lactate dehydrogenase; ECOG: Eastern Cooperative Oncology Group;MIPI: Mantle cell lymphoma international prognostic index; ASCT: autologous stem cell transplantation

In the POD24 cohort, 283 (77%) were male. In POD 24 patients with quantifiable MIPI, 218 (66%) and 110 (34%) had low/intermediate and high-risk MIPI respectively. In the Non-POD24 cohort, 464 (76%) were male. In Non-POD24 patients with quantifiable MIPI, 482 (82%) and 103 (18%) had low/intermediate and high-risk MIPI respectively. POD24 patients were more likely to have B symptoms (p = 0.003), elevated LDH (p<0.0001), splenomegaly (p<0.0001), Ki67 expression (p<0.0001)), autologous transplant (p = 0.037) and as expected, more likely to have a worse performance status (p = 0.005).

Univariate analysis and multivariate analysis

In a univariate analysis of all patients, sex, disease stage, extranodal sites and HBV infection were not associated with OS. The identified prognostic factors including age, ECOG performance status, LDH level, MIPI score, B symptoms, splenomegaly, Ki67 expression, ASCT and POD24 (Table 2).

Table 2

Factors associated with improved overall survival in MCL patients on univariate and multivariate analysis
Variable	Univariate analysis			Multivariate analysis
Variable	HR	95% CI	P value	HR	95% CI	P value
Sex	1.131	0.885–1.445	0.326
Age (> 60 vs. <=60 years)	1.638	1.333–2.012	0.000	1.375	1.047–1.805	0.022
ECOG ( > = 2 vs. <2)	1.854	1.424–2.415	0.000
Stage (III/IV vs. I/II)	1.311	0.936–1.835	0.115
LDH (Elevated vs. Normal)	2.132	1.733–2.624	0.000	1.385	1.037–1.849	0.027
Extranodal sites ( > = 2 vs. <2)	0.995	0.75–1.32	0.973
MIPI( > = 3 vs. <3)	2.412	1.919–3.031	0.000	1.540	1.075–2.205	0.019
B symptom (Yes vs. No)	1.639	1.325–2.028	0.000
HBsAg (Positive vs. Negative)	1.17	0.855–1.601	0.326
Splenomegaly (Yes vs. No)	1.802	1.463–2.221	0.000	1.4	1.061–1.849	0.017
Ki67 (> 30% vs. <=3%)	1.537	1.208–1.954	0.000	1.374	1.056–1.789	0.018
ASCT (Yes vs. No)	0.324	0.199–0.527	0.000	0.363	0.182–0.725	0.004
POD24 (Yes vs. No)	5.966	4.785–7.439	0.000	5.321	4.048–6.995	0.000
MCL: Mantle cell lymphoma; POD24: Progression of disease within 24 months; LDH: Lactate dehydrogenase; ECOG: Eastern Cooperative Oncology Group;MIPI: Mantle cell lymphoma international prognostic index; ASCT: autologous stem cell transplantation

Variables factors included in the multivariate analysis were those clinically relevant or statistically significant in univariate analysis. Among these factors, age, LDH level, MIPI score, splenomegaly, Ki67 expression, ASCT and POD24 remained significant for OS in multivariate analysis (Table 2). These results indicating that those clinical factors and POD24 were significantly related with the prognosis of MCL patients.

Patient outcomes

Follow-up time was available for all patients with a median follow-up of 31 months, with ranging from 1 to 210 months. The median PFS was 30 months (95% CI: 26.73–33.28, range: 1–194 months), and the median OS was 75 months (95%CI: 66.17–85.83, range: 1–210 months). The 5-year PFS and OS estimates for all patients were 30.5% and 56.1%, respectively (Fig. 1A, 1B).

RCS models showed an approximately linear association between month to disease progression and risk of death, while it became flat thereafter, with a point of inflection located around 24 months. The hazard ratio for death decreased rapidly with POD during the 24–36 months (Fig. 1C). Subsequently, the ROC curve analysis also confirmed POD24 presented a good performance in survival prediction with the highly reliable AUC was 0.797, and the AUC for POD12 was 0.754 (Fig. 1D). Thus, 24 months seemed a reasonable cut-off which showed the most significant prognostic effects in predicting OS.

Risk prognostic factors associated with overall survival in patients with MCL wers presented in Fig. 2. There were significant differences in median OS between POD24 groups and Non-POD24 groups. The median OS for Non-POD24 patients (mOS: 122 months) was significantly longer compared with those of POD24 patients (mOS: 24 months) (P<0.0001, Fig. 2G). These data demonstrate that POD24 was significantly associated with inferior survival of MCL patients.

Construction of the prognostic nomogram

In an exploratory analysis, we developed a nomogram to quantitatively evaluate the impact of progression of disease within 24 months (POD24) alongside other overall survival (OS)-related clinical factors within a predictive model for Mantle Cell Lymphoma (MCL) patient survival. Utilizing multivariate Cox regression analysis, we allocated scores to each factor on the nomogram scale, ascertained the individual contribution of each variable, and computed the aggregate risk score for each patient, thereby enhancing the precision of survival prediction. The total score was normalized to a distribution ranging from 0 to 100 and used to calculate the 3- and 5-year estimated OS rates of MCL patients (Fig. 3A). Calibration curves of observed versus predicted probabilities of 3-and 5-year OS demonstrated excellent concordance between the predicted survival rate and the actual survival rate (Fig. 3B). Patients with higher score indicated a poor outcome and POD24 combined with clinical factors contributed most to the survival. These results suggested that the nomogram may reliably predict MCL patient prognoses in clinical practice.

Validation of the POD24 combined with clinical prognostic factors signature

To compare the predictive power of compound nomogram and POD24 or clinical risk factors separately, we then used multivariable ROC to compare the predictive accuracy between nomogram model and individual predictors. The results suggested that the nomogram model has a higher prognostic accuracy for 3- year OS, with the most satisfying AUC value for risk score (AUC = 0.869), followed by POD24 (AUC = 0.797), LDH (AUC = 0.636), MIPI (AUC = 0.626), splenomegaly (AUC = 0.594), age (AUC = 0.584), Ki67 (AUC = 0.572) and ASCT (AUC = 0.450) (Fig. 4A). Similar results were also observed in predicting 5-year survival (Fig. 4B). To sum up, although POD24 showed an obviously predictive performance, our POD24-clinical nomogram combining the POD24 and clinical risk factors demonstrated high prognostic predictive efficiency than their separation.

MCL is a rare subtype of hematological malignant disease that comprise 2.5–6% non-Hodgkin’s lymphomas. The incidence of MCL has been increasing over these years, especially among elderly patients¹⁷. Moreover, the complexity of its clinical presentations, variety in disease pathophysiological and high incidence of disease progression/recurrence pose a major challenge to developing personalized precise therapeutic strategy^8,17. Although kinds of chemotherapeutic regimens show significant activity in MCL patients, no regimen has been found to be superior, and no standard treatment has been identified for ^4,18. Over the years, the therapeutic options has been gradually evolving from combination chemotherapy to combination novel treatment options⁴. A number of targeted therapies are approved for R/R MCL patients in a surprisingly short time, including immunomodulatory agent, proteasome inhibitor and Bruton kinase inhibitors^11,19,20. Despite currently available therapies show significant activity in MCL patients, prognosis remains poor after progression on these novel agents²¹. Treating aggressive variants of this disease is remain a serious challenge for clinicians and researchers¹⁸. Therefore, there is an urgent need to explore new individual prognostic and risk-stratified biomarkers to better distinguish the prognostic subsets of MCL with the potential to guide therapeutic decisions.

In this study, we summarized 979 cases of MCL from nine medical centers of China, which is a largest retrospective study on MCL in China. The present study presented similar clinical outcome as in previous reports^13,15. The proportion POD-24 patients with Low/intermediate MIPI was significantly lower (66% vs. 82%), and the POD-24 patients more likely to have a worse performance status. Also, POD24 could be used as a prognostic predictor of MCL patients has been proposed by previous works^13,15. Consistent with these results, our study demonstrated that the median OS for Non-POD24 patients was significantly longer than those of POD24 patients ((122 months vs 24 months), which displayed that POD24 was significantly associated with inferior survival of MCL patients. Besides, there was nonlinear dose-response relationship between POD and survival outcome of MCL, with POD was highly prognostic with a cut-off around 24 months. POD24 with markedly reduced OS with a hazard ratio (HR) of 5.32 (95% CI, 4.05–6.70) in multivariable cox regression analysis and POD24 was most powerful factor associated with an elevated risk of death than age, LDH level, MIPI score, splenomegaly, Ki67 expression and ASCT. These results indicated that POD24 was related with worse prognosis outcome for MCL patients.

MCL patient risk stratification and prognosis remain a challenge for clinicians. Mantle cell lymphoma international prognostic index (MIPI) score was established as the first prognostic stratification tool to identify clinical prognostic factors and patient with different risk groups²². The MIPI discriminated three groups of low, intermediate and high risk groups and has been validated by numerous studies^10,22,23. However, the MIPI was derived from patients with advanced stage disease primarily in the pre-rituximab era and therefore presumably do not represent the general patient population of a new era¹⁰. Several studies have demonstrated that Ki-67 proliferation index is a strong biologic prognostic factor independent of MIPI score^24,25. Thus, a combined biologic index (MIPI-b) that integrated MIPI and the Ki-67 proliferation index was developed and confirmed the prognostic value for patients with MCL¹⁰. MIPI-b model discriminates the high-risk group well, but a small low-risk group that overlapping with the intermediate-risk group in overall survival curves¹⁰. Therefore, MIPI-b does not adequately reflect the heterogeneity clinical outcome of MCL¹³. Similarly, our results also confirmed that MIPI and Ki67 do not show a promising performance in 3-years survival prediction with the AUC was 0.626 and 0.572, respectively. Thus, it is essential to establish novel strategies for risk assessment that include clinically relevant biomarkers to develop precision medicine treatment strategies.

Kinds of prognostic factors are markedly associated with survival outcome in MCL patients, including TP53 mutation, epigenetic profile variance, SOX11 gene expression profile and POD24^4,11,26. Several prognostic tools currently allow for risk stratification at diagnosis, but do not truly define the complexity of the heterogeneity of MCL disease^12,13. Our findings are consistent with some results of the previously studies from western countries, which have confirmed the impact of POD24 as a prognostic and predictive marker both at diagnosis and at time of relapse in the 2 prospective Nordic MCL trials¹³. Furthermore, we constructed an encompassed prognostic nomogram combining POD24 with OS-related clinical factors. This nomogram (AUC = 0.869) was superior than the POD24 single factor and other clinical factors. This novel developed model greatly improved the prognostic predictive accuracy, which could be used to individualize the survival outcomes of patients and has the potential translation into clinical practice in the future.

Additionally, the level of healthcare resources and available treatments in China vary from one country/region to another^27–29. While some areas may have only basic access to oncology care, some highly urbanized areas may have world-class expertise and newer therapies, some areas may have only basic treatments after being diagnosed with MCL²⁷. Additionally, many Chinese patients common have different comorbidities, for example hepatitis B virus or tuberculosis infection. Although, our results not found the clinically relevant or statistically significant of active hepatitis B virus infection in univariate analysis, chemotherapy may increase threat with hepatitis B virus reactivation and the spread of tuberculosis. So, these should be considered as a serious consideration for the treatment of MCL patients in China²⁷.

While our study has several limitations including its retrospective design, limited quality of the captured data and a heterogeneous patient population. On the other hand, it truly represents a real-world cohort experience, which is considered extremely important for understanding trends in day to day practice.

In summary, we confirm the prognostic impact of the POD24 and highlight the relevance of other known prognostic biomarkers in a retrospective analysis of a real-world cohort in Chinese patients, including a nomogram model of POD24-Clinical was established. To the best of our knowledge, our study is the first to identify and validate POD24-based prognostic model comprising with clinical prognostic factors in patients with MCL, which might serve as a prognosis stratification tool for facilitating patient counseling, decision- making regarding individualized adjuvant treatment, and follow-up scheduling.

Patients

We conducted a retrospective cohort study of 979 patients diagnosed with MCL from nine medical centers of China between January 17, 2020 and February 1, 2020. All patients obtained at least one chemotherapeutic regimen of MCL. All patients had biopsy-proven MCL disease and were age 18 or older at time of evaluation at each center. The following data were collected: age, gender, ECOG performance status, stage of disease, serum LDH, extranodal disease, MIPI score, presence of B symptoms, splenomegaly, hepatitis B virus (HBV) infection, initial date of diagnosis, Ki67 expression and use of ASCT. Response to induction treatment was assessed by each participating center through clinical examination, standard biological parameters, computed tomography scan and, when available, bone marrow aspiration/biopsy and positron emission tomography scan. The outcomes were updated in August 2023. The study was approved by the institutional review board of The Affiliated Tumor Hospital of Xiangya Medical School. All procedures were carried out in accordance with the principles of the Helsinki Declaration. The requirement for written informed consent was waived for the retrospective data by institutional review board of The Affiliated Tumor Hospital of Xiangya Medical School.

Statistics

The primary objective of this work was to describe clinical characteristics and survival outcomes of disease progression in MCL patients with POD24 and 24 months after detection, and secondarily to assess prognostic and predictive biomarkers of survival across different groups. We reported differences in baseline characteristics, MIPI score, and prognostic markers between POD groups (≤ 24 and ≥ 24 months ) using Fisher Exact and Chi-Square tests as appropriate. Individual risk factors were compared using the χ2 test. Cox proportional hazards regression was used to estimate each hazard ratio (HR) and built the multivariate model.

Overall survival (OS) was calculated from the time of diagnosis to the date of last follow-up or date of death. Progression-free survival (PFS) was defined as the time from the date of diagnosis to either the date of first relapse or first sign of progression after initial treatment. The Kaplan-Meier test was used to analyze the OS and PFS of MCL patients and each subgroup was compared by using a log-rank test.

To evaluate the prognostic impact of POD24, restricted cubic spline (RCS) were used to detect the possible nonlinear dependency of the relationship between time to POD and risk of death, using 4 knots at prespecified locations according to the percentiles of the distribution of POD.

The descriptive statistics are represented using the proportion and median. P < 0.05 was considered statistically significant. All Statistical analysis was calculated using Statistical Package for the Social Sciences (SPSS) 17.0 software (Chicago, IL, USA)

Construction and validation of the prognostic nomogram

Univariate and multivariate analysis of conventional clinical risk factors and POD24 was perfomed to identify independent prognostic factor of OS in MCL patients. A prognostic nomogram encompassing POD24 and OS-related clinical factors was assessed by “rms” R package. Calibration curves and concordance index (C-index) were used to calculate the accuracy abilities of nomogram between predicted survival and actual survival. Time-dependent receptor operating characteristic (ROC) analysis was tested by “survival ROC” R package to evaluate the predictive accuracy of the POD24-clinical prognostic model.

Ethical statements

According to ethical policies of the Affiliated Tumor Hospital of Xiangya Medical School Ethics Committee, clinical data can be analyzed and used under the precondition of without revealing the identity of patients.

Acknowledgments

We thank the patients and doctors at the participating institutes for their invaluable contributions to this cooperative study.

Author contributions

Conceptualization: Hui Zhou and Ling Xiao; Formal analysis: Qingqing Cai, Dehui Zhou, Huilai Zhang, Rong Liang, Dongfeng Zeng, Haige Ye, Yun Liang and Xiuhua Sun. Writing - Original draft: Yizi He Writing - review & editing: Caiqin Wang, Tao Pan, The work reported in the article has been performed by the authors, unless clearly specified in the text.

Fundings

This study was supported by grants from the National Natural Science Foundation of China [no. 82000200], grants from clinical trial agreement which is Signed by Xi 'an Janssen Pharmaceutical Co., Ltd. and Hunan Cancer Hospital[no:54179060MCL4007]

Hunan Provincial Natural Science Foundation of China (grant numbers 2022JJ30026)

Clinical Research Center For Lymphoma In Hunan Province, Hunan Provincial Clinical Medical Research Center for Lymphatic tumor (grant number 2021SK4015),Project of Health Commission of Hunan Province (grant numbers 202203045455), Xisike-Xinda Clinical Oncology Research Foundation(grant number Y-XD202002-0104).

Data availability statement

The datasets generated during the current study are available from the corresponding

author on reasonable request.

Conflict of interest

The other authors declare no conflict of interest.

Navarro, A., Beà, S., Jares, P. & Campo, E. Molecular Pathogenesis of Mantle Cell Lymphoma. Hematol Oncol Clin North Am 34, 795-807, doi:10.1016/j.hoc.2020.05.002 (2020).
Jain, P. & Wang, M. Mantle cell lymphoma: 2019 update on the diagnosis, pathogenesis, prognostication, and management. Am J Hematol 94, 710-725, doi:10.1002/ajh.25487 (2019).
Flinn, I. W. et al. First-Line Treatment of Patients With Indolent Non-Hodgkin Lymphoma or Mantle-Cell Lymphoma With Bendamustine Plus Rituximab Versus R-CHOP or R-CVP: Results of the BRIGHT 5-Year Follow-Up Study. J Clin Oncol 37, 984-991, doi:10.1200/jco.18.00605 (2019).
Jain, P., Dreyling, M., Seymour, J. F. & Wang, M. High-Risk Mantle Cell Lymphoma: Definition, Current Challenges, and Management. J Clin Oncol 38, 4302-4316, doi:10.1200/jco.20.02287 (2020).
Jain, A. G., Chang, C. C., Ahmad, S. & Mori, S. Leukemic Non-nodal Mantle Cell Lymphoma: Diagnosis and Treatment. Current treatment options in oncology 20, 85, doi:10.1007/s11864-019-0684-8 (2019).
Nadeu, F. et al. Genomic and epigenomic insights into the origin, pathogenesis, and clinical behavior of mantle cell lymphoma subtypes. Blood 136, 1419-1432, doi:10.1182/blood.2020005289 (2020).
Jung, D., Jain, P., Yao, Y. & Wang, M. Advances in the assessment of minimal residual disease in mantle cell lymphoma. Journal of hematology & oncology 13, 127, doi:10.1186/s13045-020-00961-8 (2020).
Roue, G. & Sola, B. Management of Drug Resistance in Mantle Cell Lymphoma. Cancers (Basel) 12, doi:10.3390/cancers12061565 (2020).
Hoster, E. et al. A new prognostic index (MIPI) for patients with advanced-stage mantle cell lymphoma. Blood 111, 558-565 (2008).
Hoster, E. et al. Prognostic Value of Ki-67 Index, Cytology, and Growth Pattern in Mantle-Cell Lymphoma: Results From Randomized Trials of the European Mantle Cell Lymphoma Network. J Clin Oncol 34, 1386-1394, doi:10.1200/JCO.2015.63.8387 (2016).
Zhang, L. et al. Metabolic reprogramming toward oxidative phosphorylation identifies a therapeutic target for mantle cell lymphoma. Science translational medicine 11, doi:10.1126/scitranslmed.aau1167 (2019).
Moccia, A. A. et al. Prognostic value of POD24 validation in follicular lymphoma patients initially treated with chemotherapy-free regimens in a pooled analysis of three randomized trials of the Swiss Group for Clinical Cancer Research (SAKK). British journal of haematology 192, 1031-1034, doi:10.1111/bjh.17045 (2021).
Eskelund, C. W. et al. Detailed Long-Term Follow-Up of Patients Who Relapsed After the Nordic Mantle Cell Lymphoma Trials: MCL2 and MCL3. Hemasphere 5, e510, doi:10.1097/HS9.0000000000000510 (2021).
Yamaguchi, M. et al. Early disease progression in patients with localized natural killer/T-cell lymphoma treated with concurrent chemoradiotherapy. Cancer Sci 109, 2056-2062, doi:10.1111/cas.13597 (2018).
Visco, C. et al. Time to progression of mantle cell lymphoma after high-dose cytarabine-based regimens defines patients risk for death. British journal of haematology 185, 940-944, doi:10.1111/bjh.15643 (2019).
Visco, C. et al. Outcomes in first relapsed-refractory younger patients with mantle cell lymphoma: results from the MANTLE-FIRST study. Leukemia 35, 787-795, doi:10.1038/s41375-020-01013-3 (2021).
Maddocks, K. Update on mantle cell lymphoma. Blood 132, 1647-1656, doi:10.1182/blood-2018-03-791392 (2018).
Ladha, A., Zhao, J., Epner, E. M. & Pu, J. J. Mantle cell lymphoma and its management: where are we now? Experimental hematology & oncology 8, 2, doi:10.1186/s40164-019-0126-0 (2019).
Le Gouill, S. et al. Ibrutinib, obinutuzumab, and venetoclax in relapsed and untreated patients with mantle cell lymphoma: a phase 1/2 trial. Blood 137, 877-887, doi:10.1182/blood.2020008727 (2021).
Dobrovolsky, D. et al. Bruton tyrosine kinase degradation as a therapeutic strategy for cancer. Blood 133, 952-961, doi:10.1182/blood-2018-07-862953 (2019).
Wang, M. et al. KTE-X19 CAR T-Cell Therapy in Relapsed or Refractory Mantle-Cell Lymphoma. The New England journal of medicine 382, 1331-1342, doi:10.1056/NEJMoa1914347 (2020).
Hoster, E. et al. A new prognostic index (MIPI) for patients with advanced-stage mantle cell lymphoma. Blood 111, 558-565, doi:10.1182/blood-2007-06-095331 (2008).
Hoster, E. et al. Confirmation of the mantle-cell lymphoma International Prognostic Index in randomized trials of the European Mantle-Cell Lymphoma Network. J Clin Oncol 32, 1338-1346, doi:10.1200/JCO.2013.52.2466 (2014).
Chihara, D. et al. Ki-67 is a strong predictor of central nervous system relapse in patients with mantle cell lymphoma (MCL). Annals of oncology : official journal of the European Society for Medical Oncology 26, 966-973, doi:10.1093/annonc/mdv074 (2015).
Determann, O. et al. Ki-67 predicts outcome in advanced-stage mantle cell lymphoma patients treated with anti-CD20 immunochemotherapy: results from randomized trials of the European MCL Network and the German Low Grade Lymphoma Study Group. Blood 111, 2385-2387, doi:10.1182/blood-2007-10-117010 (2008).
Mohanty, A. et al. Regulation of SOX11 expression through CCND1 and STAT3 in mantle cell lymphoma. Blood 133, 306-318, doi:10.1182/blood-2018-05-851667 (2019).
Yoon, D. H. et al. Treatment of mantle cell lymphoma in Asia: a consensus paper from the Asian Lymphoma Study Group. Journal of hematology & oncology 13, 21, doi:10.1186/s13045-020-00855-9 (2020).
Kang, B. W. et al. Clinical features and treatment outcomes in patients with mantle cell lymphoma in Korea: Study by the Consortium for Improving Survival of Lymphoma. Blood Res 49, 15-21, doi:10.5045/br.2014.49.1.15 (2014).
Chuang, S. S., Huang, W. T., Hsieh, P. P., Tseng, H. H. & Chang, H. M. Striking male predominance of mantle cell lymphoma in Taiwan. J Clin Pathol 59, 780, doi:10.1136/jcp.2005.035071 (2006).

No competing interests reported.

Progression of Disease Within 24 Months (POD24) Based Prognostic Signature: A New Method for Personalized Risk Assessment in Patients with Mantle Cell Lymphoma

Status:

Version 1

Abstract

Figures

Introduction

Results

Clinical features and characteristics

Univariate analysis and multivariate analysis

Patient outcomes

Construction of the prognostic nomogram

Validation of the POD24 combined with clinical prognostic factors signature

Discussion

Methods

Patients

Statistics

Construction and validation of the prognostic nomogram

Declarations

Ethical statements

References

Additional Declarations

Status:

Version 1