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Development and validation of a multiparameter prognostic model for extranodal natural killer/T-cell lymphoma: Integration of clinical, pathological, and molecular biomarkers
MM, Yunjun Wang1MM, Guohua Yu1,MD
-
Received: ,
Accepted: ,
How to cite this article: Wu S, Liu Y, Jiang L, An L, Zhang Y, Pan Y, et al. Development and validation of a multiparameter prognostic model for extranodal natural killer/T-cell lymphoma: Integration of clinical, pathological, and molecular biomarkers. CytoJournal. 2026;23:24. doi: 10.25259/Cytojournal_34_2025
Abstract
Objectives:
The objectives of the study are to develop and validate a novel prognostic model for extranodal natural killer/T-cell lymphoma (ENKTCL) by integrating clinical and pathological parameters.
Material and Methods:
We retrospectively analyzed 106 patients with ENKTCL (2008–2020) from the Department of Pathology of Yantai Yuhuangding Hospital and the Affiliated Hospital of Nantong University, constructing the novel international prognostic index (NIPI) model through multivariable Cox regression of immunohistochemistry (IHC) markers (age, MTP53, Ki-67, lactate dehydrogenase, hemoglobin, platelet-tolymphocyte ratio) and quantitative dot blot (QDB) tumor microenvironment features.
Results:
The model demonstrated significant risk stratification (P < 0.001) with a 3-year area under the curve of 0.72 (IHC) and 0.80 (QDB), outperforming Ann Arbor staging (P > 0.05) and existing international prognostic index (P = 0.00036)/natural killer lymphoma prognostic index (P = 0.00017) models. The QDB-based implementation showed superior prognostic discrimination (P = 0.00014), highlighting its potential for precise individualized therapy.
Conclusion:
NIPI provides improved risk stratification for ENKTCL, and QDB-based analysis offers enhanced precision in individualized therapy. This model addresses the unmet needs for ENKTCL prognostication and warrants further multicenter validation.
Keywords
Monoclonal antibody Ki-67
Mutant P53
NK/T-cell lymphoma
Prognostic model
Quantitative dot blot
INTRODUCTION
Extranodal natural killer/T-cell lymphoma (ENKTCL) is classified as a non-Hodgkin lymphoma and is known for its aggressive nature and heterogeneity. Previous studies have indicated that T-cell and NK-cell lymphomas are the second most prevalent categories of lymphoma after B-cell lymphoma.[1] In recent years, the incidence of ENKTCL has notably increased, with a trend toward a younger age of onset among patients. ENKTCL pathogenesis is closely associated with Epstein–Barr virus infection. This lymphoma typically arises outside the lymph nodes and is characterized by vascular lesions, mucosal ulcers, and diffuse infiltration of tumor cells.
The high invasiveness and heterogeneity of ENKTCL contribute to poor prognosis in patients with this disease. However, the optimal clinical treatment of ENKTCL remains unclear.[1,2] While active chemotherapy and radiotherapy are commonly advocated by clinicians, the disease often progresses rapidly, resulting in unfavorable patient outcomes. Clinicians frequently use the Ann Arbor staging (AAS) system and the international prognostic index (IPI) to assess the prognosis of patients with ENKTCL; however, the accuracy of these methods has been debated.[3,4]
Prognostic models that demonstrate strong generalization and high predictive capability are essential, as they can provide accurate assessments of patients’ prognostic risks and facilitate individualized risk stratification. This, in turn, allows for the development of tailored treatment plans to maximize patient outcomes. Recent studies have indicated that the natural killer lymphoma prognostic index (PINK) and other related models are garnering increasing attention from clinicians.[5-10] Nonetheless, these models primarily rely on clinical indicators or adjust for the factors included in the IPI. At present, relevant models that incorporate both clinical and pathological factors are lacking.
Pathological indicators, such as MTP53 and the Ki-67 proliferation index, have been associated with poor prognosis in ENKTCL. However, existing models, including the Korean Prognostic Index, PINK, AAS, and IPI, cannot accurately classify risks among diverse populations or distinguish high-risk patients at an early stage. Therefore, based on the characteristics of the enrolled cases and the results of the immunohistochemical experiments, we used quantitative dot blot (QDB) to conduct comprehensive quantitative assessments of the studied proteins. We established a prognostic evaluation model that integrates clinical and pathological parameters to assist clinicians in making informed treatment decisions and theoretically guide the stratification of patients into different risk categories, ultimately facilitating individualized medical services.
MATERIAL AND METHODS
Clinicopathological data
We obtained archived pathological section data from the Department of Pathology at Yantai Yuhuangding Hospital and the Affiliated Hospital of Nantong University. A total of 106 paraffin-embedded specimens from patients diagnosed with ENKTCL by surgical resection between 2008 and 2020 were collected. After re-embedding and sectioning, the wax blocks were subjected to conventional hematoxylin and eosin staining. Three senior pathologists independently re-evaluated the specimens and made diagnoses based on the new classification outlined in the 5th edition of the 2022 World Health Organization classification of tumours of haematopoietic and lymphoid tissues.
By reviewing the clinical histories of the patients, we recorded various parameters, including age, sex, disease site, AAS, IPI, Eastern Cooperative Oncology Group (ECOG) performance status, lactate dehydrogenase (LDH) levels, hemoglobin (HB) levels, white blood cell count, lymphocyte count, platelet count, and platelet-to-lymphocyte ratio (PLR). All patients were followed up by telephone. Overall survival time was calculated from the date of diagnosis, and the endpoint was defined as the time of death. The detailed survival information for each patient was meticulously documented. This study was approved by the Ethics Committee of Yantai Yuhuangding Hospital (Approval No: [2021] No. 039). Informed consent was obtained from all patients .
Major reagents and manufacturers
Major reagents used in this study, including their commercial sources, catalog numbers, and manufacturers, are systematically summarized in Table 1. These reagents were selected based on their established reliability in prior studies and were validated for specificity and batch-to-batch consistency prior to experimental use .
| Reagent item number | Main reagent name | Manufacturers |
|---|---|---|
| 1 | MTP53 (clone number: DO-7; 20220818) | Beijing Zhongshan Jinqiao Biotechnology Co., Ltd. |
| 2 | Ki-67 (clone number: MIB-1; 20220920) | Beijing Zhongshan Jinqiao Biotechnology Co., Ltd. |
| 3 | Acetone, trichloroacetic acid, phosphatase inhibitor; UM-9002 | Thermo Corporation, USA |
| 4 | Phosphate buffered saline (PBS) buffer, skimmed milk powder, Tris Buffered Saline Tween-20 (TBST) detergents; 23091907 | Hyclone Corporation, USA |
| 5 | Enhanced chemiluminescence substrate, WBKLS0100 | Beijing Yaanda Biotech Co., Ltd. |
| 6 | Tris-HCL, bovine serum albumin (BSA), ammonium persulfate; 24103002 | Amresco Corporation, USA |
Quantitative Dot Blot (QDB) testing process
The QDB measurements were provided by Quanticision Diagnostics Inc (Yantai, China). The QDB process has been described in detail elsewhere with slight modifications.[11,12] In short, formalin-fixed, paraffin-embedded (FFPE) tissue lysates were prepared by homogenization in precipitation assay buffer containing protease/phosphatase inhibitors (P8340, Sigma-Aldrich, St. Louis, MO, USA), followed by centrifugation (12,000 × g, 15 min, 4°C). The protein concentration was determined using the bicinchoninic acid assay (23225, Thermo Fisher Scientific, Waltham, MA, USA). The final concentration of the FFPE tissue lysates was adjusted to 0.25 μg/μL, and 2 μL/unit was used for QDB analysis as well as a serially diluted recombinant protein (0–50 nmol/g range) in triplicate. The loaded QDB plate (2022031280, Quanticision, Yantai, China) was dried for 1 h at room temperature (RT) and then blocked in 4% non-fat milk (SC10523018250144, Nestle, Heilongjiang, China) for an hour. For the primary antibody, anti-Ki-67 monoclonal antibody (clone MIB-1MIB-1, 20220920, Zhongshan, Beijing, China) or MTP53 (D0-7, 20220818, Zhongshan, Beijing, China) was diluted at 1:1000 in blocking buffer and incubated with QDB plate at 100 μL/well overnight at 4℃, followed by three washes with tris-buffered saline with tween 20 (TBST) (5 min each). For the secondary antibody, rabbit anti-mouse IgG-HRP conjugate (1:5000 dilution in blocking buffer, 31460; Thermo Fisher Scientific, Waltham, MA, USA) was incubated for 4 h at RT. The QDB plate was inserted into a white 96-well plate pre-filled with 100 μL/ well enhanced chemiluminescence (ECL) (WBKLS0100, Yaanda, Beijing, China) working solution for 3 min for quantification with Tecan Infinite 200pro Microplate reader using luminescence mode (integration time: 3 min) with the option “plate with cover.”
The consistency of the experiments was ensured by including cell lysates of known Ki-67 or MTP53 levels (11.9 nmol/g and 0.16 nmol/g, respectively) in all the experiments. The result was considered valid when the calculated Ki-67 or MTP53 level was within 20% of the known Ki-67 or MTP53 level at 11.9 nmol/g and 0.16 nmol/g, respectively. The absolute Ki-67 or MTP53 levels were determined based on the dose curve of the protein standard. For quality control, inter-assay coefficient of variation (CV) was maintained at <15%. Ki-67 level <11.9 nmol/g or MTP53 level <0.16 nmol/g was defined as meaningless and entered as 0 for data analysis.
Statistical methods
The Statistical Package for the Social Sciences (SPSS) 19.0 (version 19.0, IBM, Armonk, NY, USA) was used for statistical analysis. Kaplan–Meier performed univariate survival analysis on the results of MTP53, Ki-67, and QDB complete quantitative techniques, with P < 0.05 considered statistically significant. The receiver operating characteristic (ROC) curve of each model was drawn using the ROC curve function, and the area under the ROC curve (AUC) was calculated. For multiple group comparisons, post hoc tests were conducted when the omnibus log-rank test indicated statistical significance (P < 0.05). Regarding pairwise survival comparisons, adjusted P-values were calculated by multiplying the original P-values by the number of comparisons (Bonferroni correction). Analyses were performed using SPSS 19.0 (IBM Corp.) using the Kaplan– Meier method with pairwise log-rank tests, followed by manual Bonferroni adjustment.
R software analysis
The cutoffs for P53 and Ki-67 were derived through rigorous statistical analysis using R software (version 4.2.0, R Foundation for Statistical Computing, Vienna, Austria) with the following workflow:
Step 1 (ROC analysis)
The receiver operating characteristic (ROC) analysis in R package (pROC) was used to determine the optimal thresholds maximizing Youden’s index (sensitivity+specificity−1) for the 2-year progression-free survival.
Example code (P53 cutoff):
library(pROC)
roc_obj <- roc(response = survival_status, predictor = P53_ percentage)
optimal_cut <- coords(roc_obj, “best”, ret = “threshold”, best. method = “youden”)
Step 2 (Bootstrap validation)
A total of 1,000 resamplings were performed using the boot package to assess the cutoff stability.
RESULTS
Establishment of prognostic risk stratification model for ENKTCL based on immunohistochemical results
Development and Validation of a Novel International Prognostic Index Based on Immunohistochemical Profiling (NIPI-IHC).
Based on univariate and multivariate analyses, we established the NIPI-IHC, a risk stratification model incorporating laboratory indicators (LDH, HB, and PLR) and pathological parameters (age, MTP53, and Ki-67; scoring criteria in Table 2).
| Indices | Scoring criteria | Risk groups | |
|---|---|---|---|
| 0-point standard | 1-point standard | ||
| Age | ≤60 years old | >60 years old | 0 point: Low risk group |
| MTP53 | <30% or≤0.16 nmol/g✶ | ≥30% or>0.16 nmol/g | 1 and 2 point: Low-intermediate risk |
| Ki-67 | ≤60% or≤11.9 nmol/g# | >60% or>11.9 nmol/g | 3 and 4 point: Intermediate-high risk |
| LDH | ≤250 U/L | >250 U/L | 5 and 6 point: High risk |
| PLR& | ≤216 | >216 | |
| Anemia | Non-anemia | Anemia | |
Survival analysis
The log-rank test revealed no significant differences among the AAS groups (P = 0.073). In contrast, NIPI-IHC, IPI, PINK, and nomogram-revised risk index (NRI) classifications showed significant survival disparities (all P < 0.001). Post hoc Bonferroni-adjusted comparisons revealed the following:
NIPI-IHC: Low vs. high risk (adjusted P = 0.0001); intermediate-low vs. high risk (adjusted P = 0.003)
IPI: Low vs. high risk (adjusted P = 0.0002)
PINK: Group 1 versus Group 3 (adjusted P = 0.0001)
NRI: All intergroup comparisons (adjusted P < 0.01).
Predictive performance
The NIPI-IHC model achieved an AUC of 0.72 (95% confidence interval: 0.68–0.76), outperforming IPI (AUC = 0.65, P = 0.003), PINK (AUC = 0.68, P = 0.021), and NRI (AUC=0.70, P = 0.015) in ROC analysis [Figure 1].
- Analysis of survival probabilities under different risk - stratification models and presentation of ROC curves for related diagnostic indicators (a) NIPI-IHC model had statistical significance. (b) IPI model had statistical significance. (c) AAS model had no statistical significance. (d) PINK model had statistical significance. (e) NRI model had statistical significance. (f) NIPI-IHC had the largest area under the curve. NIPI: Novel international prognostic index, IHC: Immunohistochemistry, IPI: International prognostic index, AAS: Ann Arbor staging, PINK: Natural killer lymphoma prognostic index, NRI: Nomogram-revised risk index.
Patients were divided into different risk groups according to the AAS, IPI, NRI, and PINK prognostic risk classifications. The survival curve was plotted, and the AUC was calculated. The results showed no significant difference in prognosis among the groups according to AAS (P = 0.073), whereas the results of the IPI, PINK, and NRI grading methods showed statistically significant differences in prognosis among the groups (PIPI < 0.001, PPINK < 0.001, and PNRI < 0.001) [Figure 1]. The predictive abilities of the four prognostic models were AUCAAS=0.55, AUCIPI = 0.71, AUCPINK = 0.68, and AUCNRI = 0.72.
Establishment of prognostic risk stratification model for ENKTCL based on QDB method
The QDB method was used to quantitatively detect the concentrations of MTP53 and Ki-67 proteins in tumor tissues and conduct a survival analysis [Figure 2]. The results showed a statistically significant difference between the two groups when the MTP53 protein concentration in the tumor tissue was grouped with 0.16 nmol/g as the threshold value (P = 0.0046). In addition, a statistically significant difference was observed between the two groups when the Ki-67 protein concentration was grouped with 11.9 nmol/g as the threshold value (P = 0.0021).
- Graphical Representation of Survival Probabilities of QDB - related Indicators and Diagnostic Performance of NIPI (a) The threshold value of MTP53 by QDB was 0.16 nmol/g. (b) The threshold value of Ki-67 by QDB was 11.9 nmol/g. (c) NIPI-QDB had statistical significance. (d) The AUC of NIPI-QDB was 0.8. NIPI: Novel international prognostic index, QDB: Quantitative dot blot, AUC: Area under curve.
After the complete quantitative results of MTP53 and Ki-67 were substituted for the MTP53 and Ki-67 immunohistochemical results of the NIPI (IHC) prognostic model, the NIPI (QDB) was established (scoring method shown in Table 2). After the sum for each patient was calculated, prognostic analysis was performed, and the AUC was calculated. The results showed that the NIPI prognostic risk stratification model which included the quantitative results of QDB could divide the patients into different prognostic risk groups (P = 0.00014), and the AUC was 0.8 in Figure 2.
DISCUSSION
ENKTCL is a highly aggressive and heterogeneous form of non-Hodgkin lymphoma, accounting for 1.23% of all lymphoma cases in Asia.[13,14] Although clinicians advocate active chemotherapy and radiotherapy, the optimal treatment for ENKTCL remains unclear. The disease progresses rapidly, and the prognosis of patients is poor.[13] The AAS and IPI are widely used by clinicians to evaluate the prognosis of patients with ENKTCL; however, their accuracy remains controversial.[3,4]
According to the literature, the PINK and other relevant research models have attracted increasing attention from clinicians.[3-10] However, these models are based on the factors included in the IPI, and the PINK and other related research models cannot accurately stratify different populations or distinguish high-risk patients from those in the early stages of the disease. Therefore, the generalizability of these prognostic models remains unclear. To address this urgent need for clinicians and patients, a prognostic model with strong generalizability and high predictive ability must be established. This model should guide clinicians’ treatment decision-making, theoretically assist in categorizing patients into different risk groups, and facilitate individualized and precise diagnosis and treatment. Although the NIPI model demonstrated a robust prognostic value in our institutional cohort, we acknowledge that external validation across diverse populations and healthcare settings is essential for clinical implementation.
According to the literature, many factors affecting the prognosis of patients with ENKTCL have been reported.[14-21] Following thorough research and screening of various factors, some elements that were not easily obtainable for evaluating patient conditions, such as the location of distant lymph node involvement and primary tumor invasion of adjacent tissues or structures, were eliminated. In addition, factors that were easily influenced by clinicians’ subjective assessments, such as the ECOG score, were excluded. After this screening process, six prognostic factors – age, LDH, PLR, HB, MTP53, and Ki-67 – were included to establish a NIPI prognostic risk stratification evaluation model. The results of the statistical analysis indicated that the NIPI model effectively distinguished patients in this group, even in the early stages of the disease, and could differentiate patients across various risk groups. The AUC was used to assess the predictive ability of the model. The AUC of the NIPI prognostic model was 0.707, indicating that the NIPI model possesses a high recognition capability.
Compared with the NIPI prognostic model, we found that IPI, PINK, AAS, and NRI were utilized in this group of patients. Statistical analysis indicated that the AAS could not effectively distinguish between patients with different prognostic risks. Although the IPI, PINK, and NRI models can differentiate various prognostic risks, they share a common limitation: When patients are in the early stages of the disease, these models demonstrate poor discriminative ability and cannot accurately predict patient prognosis.
In terms of diagnostic ability, the AUC for the IPI and NRI was greater than that of the NIPI model following statistical analysis. Thus, although the IPI and NRI exhibit strong diagnostic capabilities, they may only show higher diagnostic accuracy in certain patient populations or demonstrate particular advantages in distinguishing the prognosis of advanced high-risk patients. Moreover, these two prognostic models lack universality because of notable differences in their applicability across various regions and different racial groups of patients.
The quality of immunohistochemical staining and interpretation of immunohistochemical results by different pathologists exhibited a certain degree of subjectivity. To eliminate errors in the interpretation of immunohistochemical findings and verify whether the establishment of the NIPI model was coincidental, we used the QDB technique for the comprehensive quantitative detection of MTP53 and Ki-67 proteins.
QDB detects protein expression levels in high-throughput samples by streamlining the experimental process of western blotting. Only one type of antibody suitable for immunohistochemistry was used. QDB technology not only complements the limitations of immunohistochemistry, which cannot be completely quantified, but also avoids false-positive results caused by enzyme-linked immunosorbent assays, thereby reducing errors.[12,22]
After statistical analysis, the results of the QDB experiment demonstrated a significant difference in the prognosis of patients when the thresholds for MTP53 and Ki-67 were set at 0.16 nmol/g and 11.9 nmol/g, respectively. This difference was statistically significant (P < 0.05). Based on the quantitative results from the QDB, the MTP53 and Ki-67 immunohistochemical results of the NIPI prognostic model were updated, as shown in Figure 2. A total of 106 patients were categorized into four groups with differing prognostic risks, and the prognostic differences among these groups were statistically significant (P < 0.05). Following complete protein quantification, the predictive ability of the NIPI prognostic model was further enhanced, with an AUC of 0.8.
Based on the analysis and literature review presented above, the generalizability of the NRI, PINK, IPI, and AAS systems is insufficient, and these models do not represent an optimal approach for evaluating the prognosis of patients with ENKTCL. The NIPI prognostic risk stratification model, which integrates clinical and pathological parameters, offers a novel perspective. Compared with similar predictive models, patients classified using the NIPI model exhibited a balanced distribution across the four groups and demonstrated superior prognostic discrimination ability and potential. This model can identify patients with varying prognostic risks at an early stage of the disease, thereby providing clinical treatment guidance and facilitating more individualized and precise diagnosis and treatment.
According to relevant research findings, the role of QDB technology in providing absolute quantitative protein data in high-throughput studies is well established and can significantly reduce bias at the quantitative level.[12,22] We anticipate that the NIPI model will prove to be an effective predictive tool for prognostic risk assessment in patients with ENKTCL. However, several limitations should be acknowledged: (1) Model validation was performed in a single-center cohort, which may limit generalizability to populations with different ethnic backgrounds or treatment protocols; (2) dynamic monitoring of prognostic markers was not implemented in this study, whereas serial assessment could improve risk stratification accuracy; and (3) the current model integrates clinicopathological parameters but does not incorporate emerging biomarkers. Multicenter prospective studies with standardized assay protocols are needed to verify these findings. We encourage other researchers to validate this new prognostic model in future studies. While the NIPI model demonstrates strong prognostic capability, its clinical adoption requires further validation of utility in therapeutic decision-making. This will be systematically addressed in our forthcoming implementation science research.
SUMMARY
The prognostic risk model for ENKTCL, based on clinical and pathological parameters, can predict the prognostic risk of patients with ENKTCL and facilitate individualized and accurate diagnosis and treatment.
QDB analysis offers significant advantages for accurate diagnosis and treatment and provides an objective means of complete protein quantification. It may be an important experimental technique for clinical laboratory detection.
AVAILABILITY OF DATA AND MATERIALS
The anonymized datasets and analytical materials supporting this study are available from the corresponding author upon reasonable request, subject to ethical approvals and data protection agreements.
ABBREVIATIONS
AAS: Ann Arbor staging
AUC: Area under the curve
ECOG: Eastern Cooperative Oncology Group
ENKTCL: Extranodal NK/T-cell lymphoma
FFPE: Formalin-fixed paraffin-embedded
HB: Hemoglobin
IHC: Immunohistochemistry
IPI: International prognostic index
LDH: Lactate dehydrogenase
MIB-1: Monoclonal antibody Ki-67
NIPI: Novel international prognostic index
NRI: Nomogram-revised risk index
PINK: Natural killer lymphoma prognostic index
PLR: Platelet-to-lymphocyte ratio
QDB: Quantitative dot blot
ROC: Receiver operating characteristic
TBST: Tris-buffered saline with Tween
ECL: Enhanced chemiluminescence
CV: Coefficient of Variation
AUTHOR CONTRIBUTIONS
SSW and YFL: Contributed equally to this work; GHY, YFL, and SSW: Designed the study and drafted the manuscript; LCA and LJ: Participated in immunohistochemical staining; YP and LLS: Searched the literature and performed the histological evaluation; YFZ, YJW, and LLS: Participated in providing the clinical information; YJW: Participated in revising the manuscript. All authors read and approved the final manuscript. All authors have agreed to authorship and order of authorship for this manuscript and that all authors have the appropriate permissions and rights to the reported data. All authors meet ICMJE authorship requirements.
ACKNOWLEDGMENT
Not applicable.
ETHICS APPROVAL AND CONSENT TO PARTICIPATE
The research/study approved by the Institutional Review Board at Yantai Yuhuangding Hospital, number 2021-039, dated 2021-02-05. Informed consent was obtained from all patients, and the study complied with the Declaration of Helsinki.
CONFLICTS OF INTEREST
Given his role as editorial member, Guohua Yu had no involvement in the peer-review of this article and has no access to information regarding its peer-review.
EDITORIAL/PEER REVIEW
To ensure the integrity and highest quality of CytoJournal publications, the review process of this manuscript was conducted under a double-blind model (authors are blinded for reviewers and vice versa) through an automatic online system.
FUNDING: The present study was supported by Yantai Science and Technology Planning Project (grant no. 2021MSGY043) and Research and Development Fund of Yantai Yuhuangding Hospital(2022-02).
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