Pretreatment C-reactive protein-to-albumin ratio predicts clinical outcomes in patients with peripheral T-cell lymphoma

Peripheral T-cell lymphoma (PTCL) is an aggressive and heterogenous T-cell lymphoid malignancy. The prognostic value of C-reactive protein-to-albumin ratio (CAR) has never been assessed in PTCL. This study retrospectively reviewed the medical records of 76 patients diagnosed with various subtypes of PTCL. A CAR cutoff value of 0.794 was determined, and clinical outcomes, including response rate, overall survival (OS), and progression-free survival (PFS), were compared between the high (> 0.794) and low (≤ 0.794) CAR groups. After induction therapy, complete response was achieved in 8 (32.0%) and 39 patients (76.5%) in the high and low CAR groups, respectively. During the median follow-up of 57.5 months, the high CAR group had significantly worse 5-year PFS and 5-year OS rates. Even with adjustment for the International Prognostic Index (≥ 3), Prognostic Index for PTCL-unspecified (≥ 3), and T cell score (≥ 2), high CAR remained a significant prognostic factor for PFS (hazard ratio [HR]: 4.01, 95% confidence interval [CI] 2.04–7.86, p < 0.001) and OS (HR: 2.97, 95% CI: 1.33–6.64, p = 0.008). CAR may play a complementary role in predicting prognosis in patients with PTCL, considering its simplicity, objectivity, and easy accessibility.


Introduction
Peripheral T-cell lymphomas (PTCLs) are a category of heterogeneous neoplasms that constitute 10-15% of non-Hodgkin's lymphomas [1]. Combination chemotherapy such as CHO(E)P (cyclophosphamide, doxorubicin, vincristine, and prednisone (plus etoposide) is generally required as an initial treatment, and high-dose chemotherapy with autologous hematopoietic stem cell transplantation (HSCT) can be offered to a select population of patients [2]. Most patients with PTCL not otherwise specified (PTCL-NOS) and angioimmunoblastic T-cell lymphoma (AITL) have been reported to present with advanced-stage disease at diagnosis, with a survival rate limited to 30-45% [3]. Importantly, the prognosis of anaplastic large cell lymphoma (ALCL) is largely dependent on the presence of anaplastic lymphoma kinase (ALK). Indeed, one study found that the 5-year survival rate in ALK-negative ALCL patients was 49%, which was significantly lower than that among ALK-positive ALCL patients (70%) [4]. Although new strategies have been developed to improve the prognosis of PTCL, the majority of patients do not achieve favorable clinical outcomes despite aggressive treatment strategies.
Traditionally, the International Prognostic Index (IPI), which is based on information for age (> 60 years), stage (III or IV), increased serum lactate dehydrogenase (LDH) levels, Eastern Cooperative Oncology Group (ECOG) Performance Status (> 1), and the number of extranodal involvement (> 1), has been used as a prognostic marker for PTCL [5]. Jongheon Jung and Ja Yoon Heo have contributed equally to this work and share first authorship. While many studies have demonstrated its usefulness in predicting clinical outcomes, there have been some objections to its use in certain types of PTCL [6]. Other prognostic tools have been proposed, such as the Prognostic Index for PTCL-unspecified (PIT), which is based on age (> 60 years), increased LDH, ECOG Performance Status (> 1), bone marrow involvement, and the T cell score developed by the International T cell Project Network that defined advanced stage III or IV, as well as ECOG performance status (> 1), serum albumin (< 3.5 g/dl), and absolute neutrophil count (6.5 × 10 9 /l) as prognostic factors. However, their uses are generally limited for PTCL-NOS [7,8]. Therefore, there is still a demand for an accurate and simple tool for this heterogeneous group of aggressive diseases.
The C-reactive protein (CRP) to albumin ratio (CAR) has been investigated as a prognostic marker for various malignancies and has proven clinical value [9]. The utility of CAR has also been demonstrated in diffuse large B-cell lymphoma and extranodal natural killer T-cell lymphoma [10,11]. Additionally, various blood-based biomarkers, such as platelets, serum globulin, ferritin, neutrophils, and lymphocytes, have been verified to facilitate prognostication [12,13]. Importantly, these markers have been shown to reflect the tumor microenvironment and offer an easily accessible and objective index. However, the values of these markers have not yet been assessed in PTCL. Therefore, in this study, we evaluated the prognostic significance of these biomarkers in patients with newly diagnosed PTCL.

Patient population
In this study, we reviewed the medical records of 76 patients who were diagnosed with PTCL, including PTCL-NOS, AITL, and ALCL, who were either ALK-positive or -negative. The diagnosis of all patients was histologically confirmed and they received combination chemotherapy as an induction treatment between 2007 and 2019 at the National Cancer Center, Korea, and at the National Health Insurance Service Ilsan Hospital, Korea. Patients aged > 18 years at diagnosis who had available clinical and laboratory data at baseline before the initiation of treatment were included. Those without available medical records and those who did not receive chemotherapy were excluded from the study. Medical records were retrospectively reviewed. The responses to chemotherapy were stratified based on the Lugano classification, which includes complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD) [14].
The study protocol was approved by the Institutional Review Board (IRB) (National Cancer Center 2020-0110, National Health Insurance Service Ilsan Hospital 2020-04-020) and complied with the Declaration of Helsinki. The requirement for informed consent was waived by the IRB, considering that no intervention was involved due to the retrospective nature of this study.

Laboratory testing
The initial values of CRP and albumin were collected using laboratory data from the day closest to the beginning of induction therapy. The CAR was calculated as CRP divided by albumin. Other blood-based values, such as hemoglobin, platelet, neutrophil count, lymphocyte count, serum globulin, beta-2 microglobulin, ferritin, and LDH, were also obtained from the patient's medical records. All laboratory data were obtained from the XE-2100 system (Sysmex, Kobe, Japan) at the National Cancer Center, Korea, and at the National Health Insurance Service Ilsan Hospital, Korea.

Statistical analysis
Overall survival (OS) was defined as the time from the start of induction therapy to death from any cause or the last available follow-up date. Progression-free survival (PFS) was calculated from the first day of therapy to the date of disease progression or death. The cutoff finder method described by Budczies et al. was applied to investigate an appropriate CAR cutoff value for patients [15]. Using this method, the optimal cutoff of CAR, that split the results in the most significant log-rank test from the survival analysis, was designated as 0.794. The same method was used to define appropriate values for albumin-globulin ratio (AGR), beta-2 microglobulin, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and ferritin.
Based on the cutoff value of CAR, patients were classified into high and low groups, and their baseline characteristics were compared. Continuous variables were analyzed using the two-sample t test or the Mann-Whitney U test, depending on the results of the normality test. Categorical values were analyzed using the χ 2 test or Fisher's exact test, as appropriate. Survival analyses were performed using the Kaplan-Meier method and log-rank test. Univariate and multivariate Cox proportional hazards models were applied to evaluate prognostic values, and hazard ratios (HRs) and 95% confidence intervals (CIs) were obtained. Differences were considered significant at a two-sided p value of < 0.05. All statistical analyses were performed using R software (version 3.5.1; R Foundation for Statistical Computing, Vienna, Austria).

Patient characteristics
In total, 76 patients with available data were included in this study. All patients were histologically diagnosed with PTCL and received chemotherapy as induction therapy. Of these, 25 (32.9%) patients were classified into the high CAR group, while 51 (67.1%) patients were classified into the low CAR group. Baseline characteristics of both groups are presented in Table 1. The most common diagnosis was PTCL-NOS (n = 31, 40.8%), followed by AITL (n = 24, 31.6%), ALK-negative ALCL (n = 12, 15.8%), and ALK-positive ALCL (n = 9, 11.8%). The high CAR group showed a significantly higher proportion of patients with advanced-stage, poor performance status, elevated LDH, and extranodal involvement (> 1), which are all components of the IPI. The percentage of patients with B-symptoms at diagnosis was also significantly higher in the high CAR group than in the low group (72.0 vs. 29.4%, p < 0.001). More than two-thirds of the high CAR group initially had bone marrow involvement, while only approximately 20% of the low CAR group presented with it. The distributions of IPI, PIT, and T cell scores revealed significant differences between the two groups, as all factors predicted favorable outcomes for the low CAR group. Importantly, the regimens of induction therapy and the number of patients for whom autologous HSCT was performed were not significantly different between the groups.

Univariable and multivariable analyses of survival outcomes according to CAR
The results of the univariate analysis for PFS and OS demonstrated that most components of the IPI and PIT were significantly associated with survival outcomes. High CAR was also significantly correlated with both PFS (HR: 5.12, 95% CI 2.74-9.59, p < 0.001) and OS (HR: 3.85, 95% CI 2.13-6.97, p < 0.001) ( Table 2). After adjustment for components of the IPI, PIT, and T cell score, high CAR remained a significant factor for both PFS (HR: 2.83, 95% CI 1.34-5.95, p = 0.006) and OS (HR 3.02, 95% CI 1.23-7.43, p = 0.016) ( Table 3).

Subgroup analyses according to CAR
Considering that patients with ALK-positive ALCL showed clearly superior survival rate compared to other PTCLs, we performed separate analyses according to subtypes. First, all patients who were diagnosed with PTCLs except ALK-positive ALCL were analyzed together in which cut-off value of CAR was not different with the same statistical method as previously mentioned. The results of the univariate and multivariate analyses revealed

Discussion
In this study, we performed a retrospective analysis of blood-based biomarkers as prognostic factors in patients with newly diagnosed PTCL. The results demonstrated that pretreatment CAR predicted the clinical behavior of PTCL Data are presented as numbers (frequency) Bold italic values indicate statistically significant p values CAR C-reactive protein-to-albumin ratio, PTCL-NOS peripheral T-cell lymphoma-not otherwise specified, AITL angioimmunoblastic T-cell lymphoma, ALCL anaplastic large cell lymphoma, ALK anaplastic lymphoma kinase, ECOG PS Eastern Cooperative Oncology Group Performance Status, LDH lactate dehydrogenase, EN extranodal involvement, BM bone marrow, IPI International Prognostic Index, PIT Prognostic Index for PTCL-unspecified, CHO(E)P cyclophosphamide, doxorubicin, vincristine, and prednisone (plus etoposide); ICED ifosfamide, carboplatin, etoposide, dexamethasone; IMEP ifosfamide, methotrexate, etoposide, prednisone; ESHAOx etoposide, methylprednisolone, high-dose cytarabine, oxaliplatin; CR complete response, PR partial response, SD stable disease, PD progressive disease, HSCT hematopoietic stem cell transplantation in terms of response to induction chemotherapy and survival outcomes. In particular, we compared the statistical significance of CAR to previously established prognostic indexes, including the IPI, PIT, and T cell score, and high CAR was significantly associated with poor PFS and OS after adjusting for IPI, PIT, and T cell scores separately and together. Additionally, the prognostic significance of other biomarkers such as beta-2 microglobulin, NLR, AGR, PLR, and ferritin was assessed, revealing clinically meaningful outcomes, as previously reported for other lymphomas [16][17][18][19]. Importantly, CRP is a well-known marker of infectious diseases. It is primarily synthesized in hepatocytes and acts as an acute inflammatory protein that increases up to 1000fold at sites of inflammation [20]. Furthermore, a relationship between elevated CRP levels and the development of cancer has been established. In fact, a previous study suggested that the association between CRP level and cancer risk might be due to three possibilities. First, elevated CRP levels may cause cancer. Second, cancer increases CRP levels. Third, inflammation induces both increased CRP levels and the development of cancer [21]. In terms of the tumor microenvironment, inflammatory conditions have been shown to be associated with elevated CRP levels and cancer biology [22]. Although the causality between CRP and cancer has yet to be further elucidated, it is indisputable that CRP may be a useful marker in malignancies.
Serum albumin is known as a nutritional marker, but its physiological role also includes an anti-inflammatory function [23]. Notably, hypoalbuminemia has been independently associated with inflammatory conditions represented  by elevated CRP levels, thought to be driven from inflammatory-induced capillary leakage [24]. Low serum albumin levels might also be caused by the secretion of inflammatory cytokines, such as interleukin-6 and tumor necrosis factor-α, which are released by cancer cells [25]. On the basis of these mechanisms, albumin has been suggested as an independent prognostic marker of malignancies [26]. In summary, high levels of pretreatment CAR may reflect inflammatory conditions and/or malnutritional status, which could be the result of cancer development or a high degree of comorbidity and frailty, which are directly associated with survival outcomes in malignancies [27]. Although high CAR was independent of previously known prognostic indexes, IPI and PIT were robust in our analyses. The T cell score was also significantly associated with OS in our patients. Taken together, our results showed that these previously established markers were effective in predicting clinical outcomes in PTCL and identifying CAR as another valuable marker considering its simplicity and objectivity. We also evaluated other biomarkers including beta-2 microglobulin, NLR, AGR, PLR, and ferritin. The analyses of these novel markers suggested that they could be used as simple and objective biomarkers of PTCL. Although we did not include other possible prognostic markers, such as the Epstein-Barr virus and Ki-67 index due to the retrospective nature of our study, these markers have also been suggested to predict clinical outcomes [28]. Gene expression profiles also have been used as a prognostic marker in patients with diffuse large B-cell lymphoma [29], and suggested in PTCL, but their use is still limited to defining prognosis [6]. Recently, brentuximab vedotin, an antibody-drug conjugate composed of an anti-CD30 monoclonal antibody conjugated with a microtubule-disrupting drug, was approved as a first-line treatment for CD30-positive PTCL [30]. Importantly, other drugs such as pralatrexate, belinostat, or romidepsin, are available for the initial treatment of PTCL [1]. Therefore, the use of these indexes to predict clinical outcomes precisely in an era of novel agents warrants further attention.
Our study has several limitations. First, although we included numerous cases despite of the relatively low incidence of PTCL, some of separate analyses for each type did not show statistically significant results possibly due to insufficient numbers. Second, the induction regimens were heterogeneous due to the retrospective nature of the study and might confound survival outcomes. However, this is unlikely given that no significant differences were detected in the selection of initial therapy. Third, we used our cutoff values of biomarkers, which could not be validated in the independent cohort due to the limited number of cases. Nevertheless, it is obvious that the blood-based biomarkers that we investigated have the potential to be clinically meaningful prognostic indexes. Therefore, further studies are needed to evaluate the cutoff values for these biomarkers, maximizing their clinical utility.

Conclusion
In conclusion, elevated CAR was associated with a worse response to treatment and lower survival rates in patients with PTCL. CAR might play a complementary role in predicting prognosis in patients with PTCL, considering its simplicity, objectivity, and easy accessibility.