Open Access

Inflammation‑based prognostic markers of metastatic pancreatic cancer using real‑world data in Japan: The Tokushukai REAl‑world Data (TREAD) project

  • Authors:
    • Rai Shimoyama
    • Yoshinori Imamura
    • Kiyoaki Uryu
    • Takahiro Mase
    • Megumi Shiragami
    • Yoshiaki Fujimura
    • Maki Hayashi
    • Megu Ohtaki
    • Keiko Ohtani
    • Nobuaki Shinozaki
    • Hironobu Minami
  • View Affiliations

  • Published online on: January 31, 2024     https://doi.org/10.3892/ol.2024.14269
  • Article Number: 136
  • Copyright: © Shimoyama et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Inflammation‑based prognostic markers based on a combination of blood‑based parameters, including the modified Glasgow prognostic score (mGPS), have been associated with clinical outcomes in patients with various types of cancer. The present study aimed to evaluate and compare the accuracy of these previously reported markers in patients with metastatic pancreatic cancer receiving first‑line chemotherapy. A total of 846 patients were identified between April 2010 and March 2020 as part of a nationwide real‑world study from 46 Tokushukai medical group hospitals in Japan. Blood laboratory data collected within 14 days of starting first‑line chemotherapy assessed 17 inflammation‑based prognostic markers. Information from patients with no missing data was used to compare the accuracy and performance of the inflammation‑based prognostic markers. A total of 487 patients were eligible for this supplemental analysis. The 17 inflammation‑based markers demonstrated significant prognostic value. Among them, the concordance rate with overall survival (OS) was highest for mGPS. The median OS time of patients with mGPS 0, 1 and 2 was 8.2, 6.0 and 2.9 months, respectively. Compared with mGPS 0, mGPS 1 and 2 showed hazard ratios of 1.39 (95% confidence interval, 1.07‑1.81) and 2.63 (2.00‑3.45), respectively. The present real‑world data analysis showed that various previously reported inflammation‑based markers had significant prognostic value in patients with metastatic pancreatic cancer. Among these markers, the mGPS demonstrated the highest level of accuracy. This trial has been registered in the University Hospital Medical Information Network Clinical Trials Registry as UMIN000050590 on April 1, 2023.

Introduction

Pancreatic cancer is the fourth leading cause of cancer death and has one of the poorest prognoses, with a very low 5-year survival rate of about 10% in Japan and the United States (13). Its incidence is increasing (1,3), and most cases (−80%) are unresectable at diagnosis. FOLFIRINOX (including fluorouracil, folinic acid, oxaliplatin, and irinotecan) (46) and gemcitabine plus nab-paclitaxel (7) have been reported as the standard first-line therapies for advanced/recurrent pancreatic cancer, but the prognosis remains poor with median overall survival (OS) of 9–12 months in clinical trials (4,7). Conversely, our real-world study, encompassing 846 cases of metastatic pancreatic cancer initially treated between 2010 and 2020, revealed a median OS of just 6.8 months (8).

Even in cases with unfavorable prognoses, an accurate prediction of the clinical outcomes is crucial. The usefulness of inflammation-based and nutritional markers in patients with cancer has been widely reported (9). Historically, various prognostic markers and their correlation with outcomes in cancer patients have been reported since the 1980s (1012). To date, numerous markers have been investigated for their utility in diverse scenarios, and due to their convenience, they are widely utilized in actual clinical practice. Nonetheless, data on their direct comparisons are limited. It's essential to validate the most suitable markers for each specific clinical setting.

The objective of this study is to conduct a direct comparison of the various inflammation-based prognostic markers reported to date, utilizing the aforementioned dataset (8), in order to identify the most accurate markers for assessing prognosis in metastatic pancreatic cancer.

Patients and methods

Study overview

The Tokushukai Real-world Data (TREAD) project is a retrospective cohort study conducted at Tokushukai Medical Group hospitals. Tokushukai Medical Group is a leading medical group in Japan, encompassing 71 general hospitals nationwide. The study utilizes a shared medical record system across these hospitals. (e-Karte and Newtons2; Software Service Inc., Osaka, Japan) and chemotherapy protocol system (srvApmDrop; Software Service Inc., Osaka, Japan), the details of which can be found in a separate article (13). The project adhered to the ethical guidelines for medical and biological research involving human subjects in Japan (14) and followed the principles of the Declaration of Helsinki. Approval for the study was obtained from the Ethics Committee of the Tokushukai Group in April 2020 (approval no. TGE01427-024). Patients were informed about the opt-out method, and the study was registered in the UMIN Clinical Trial Registry under the number UMIN000050590.

Patients

We identified 846 patients with pathologically or radiologically confirmed primary metastatic pancreatic cancer who underwent first-line chemotherapy at Tokushukai Medical Group hospitals between April 1, 2010, and March 31, 2020 (8). Briefly, the patients were treated with gemcitabine, S-1, gemcitabine plus S-1, gemcitabine plus nab-paclitaxel, or FOLFIRINOX as their first-line treatment. Patients with pathological diagnoses of adenocarcinoma, adenosquamous carcinoma, and carcinoma/malignant neoplasms were included in the analysis. Patients with active double cancer, inadequate treatment history, and missing fundamental patient data were excluded from the study.

Data collection

As separately described, information on patients, tumor-related factors, study period (A: 2010–2013, B: 2014–2016, C: 2017–2020), hospital volume (high- and low-volume hospitals), hospital type (government-designated cancer hospital, prefecture-designated cooperative cancer hospital, or non-designated general hospital), and first-line chemotherapy regimens was extracted from the medical record system, the chemotherapy protocol system, and the National Cancer Registry Data in Japan (15).

For supplemental analysis, blood laboratory data for different parameters [white blood cells, neutrocytes, lymphocytes, monocytes, hemoglobin, platelets, total bilirubin, aspartate aminotransferase, alanine aminotransferase, lactate dehydrogenase (LDH), γ-glutamyl transpeptidase, alkaline phosphatase, creatinine, creatinine clearance, c-reactive protein (CRP), albumin, glucose, hemoglobin A1c, carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9)] collected within 14 days of first-line treatment were extracted from the electronic medical record, and levels of inflammation-based prognostic markers were calculated (1629). The markers used in this study are listed in Table I. For the markers, previously defined cutoff values were used, but for those that were not defined, we referred to previous studies (24,25).

Table I.

Systemic inflammation-based prognostic scores and ratios.

Table I.

Systemic inflammation-based prognostic scores and ratios.

DefinitionScore or ratio
PNI
  PNI; Onodera et al (16)
    10 × albumin (g/dl) + 0.005 × lymphocyte count (/dl)≥45
    10 × albumin (g/dl) + 0.005 × lymphocyte count (/dl)<45
GPS
  GPS; original (17)
    C-reactive protein ≤1.0 (mg/dl) and albumin ≥3.5 (g/dl)0
    C-reactive protein >1.0 (mg/dl) or albumin <3.5 (g/dl)1
    C-reactive protein >1.0 (mg/dl) and albumin <3.5 (g/dl)2
  J-mGPS (20)
    C-reactive protein ≤0.5 (mg/dl) and albumin ≥3.5 (g/dl)0
    C-reactive protein >0.5 (mg/dl) or albumin <3.5 (g/dl)1
    C-reactive protein >0.5 (mg/dl) and albumin <3.5 (g/dl)2
CAR (18)
  C-reactive protein (mg/dl)/albumin (g/dl)≤0.22
  C-reactive protein (mg/dl)/albumin (g/dl)>0.22
NLR (19)
  Neutrophil count (/µl)/lymphocyte count (/µl)<3
  Neutrophil count (/µl)/lymphocyte count (/µl)≥3-<5
  Neutrophil count (/µl)/lymphocyte count (/µl)≥5
PLR (20)
  Platelet count (/µl)/lymphocyte count (/µl)≤150
  Platelet count (/µl)/lymphocyte count (/µl)>150
LMR (21)
  Lymphocyte count (/µl)/monocyte count (/µl)≥2.40
  Lymphocyte count (/µl)/monocyte count (/µl)<2.40
dNLR (22)
  Neutrophil count (/µl)/(leukocyte count (/µl)-neutrophil count (/µl))<3
  Neutrophil count (/µl)/(leukocyte count (/µl)-neutrophil count (/µl))≥3-<5
  Neutrophil count (/µl)/(leukocyte count (/µl)-neutrophil count (/µl))≥5
NPS (23)
  Neutrophil count ≤7,500 (/µl) and platelet count ≤400,000 (/µl)0
  Neutrophil count >7,500 (/µl) or platelet count >400,000 (/µl)1
  Neutrophil count >7,500 (/µl) and platelet count >400,000 (/µl)2
NLS (24,25)
  Neutrophil count ≤7,500 (/µl) and lymphocyte count ≥1,500 (/µl)0
  Neutrophil count >7,500 (/µl) or lymphocyte count <1,500 (/µl)1
  Neutrophil count >7,500 (/µl) and lymphocyte count <1,500 (/µl)2
PLS (24,25)
  Platelet count ≤400,000 (/µl) and lymphocyte count ≥1,500 (/µl)0
  Platelet count >400,000 (/µl) or lymphocyte count <1,500 (/µl)1
  Platelet count >400,000 (/µl) and lymphocyte count <1,500 (/µl)2
LMS (24,25)
  Lymphocyte count ≥1,500 (/µl) and monocyte count ≤800 (/µl)0
  Lymphocyte count <1,500 (/µl) or monocyte count >800 (/µl)1
  Lymphocyte count <1,500 (/µl) and monocyte count >800 (/µl)2
PI (23)
  C-reactive protein ≤1.0 (mg/dl) and leukocyte count ≤11,000 (/µl)0
  C-reactive protein >1.0 (mg/dl) or leukocyte count >11,000 (/µl)1
  C-reactive protein >1.0 (mg/dl) and leukocyte count >11,000 (/µl)2
SII (26)
  Neutrophil count (/µl) × 10 × platelet count (/µl)/lymphocyte count (/µl)<300
  Neutrophil count (/µl) × 10 × platelet count (/µl)/lymphocyte count (/µl)≥300-<600
  Neutrophil count (/µl) × 10 × platelet count (/µl)/lymphocyte count (/µl)≥600-<1,000
  Neutrophil count (/µl) × 10 × platelet count (/µl)/lymphocyte count (/µl)≥1,000
SIRI (27)
  Neutrophil count (/µl) × monocyte count (/µl)/lymphocyte count (/µl)<500
  Neutrophil count (/µl) × monocyte count (/µl)/lymphocyte count (/µl)≥500-<1,000
  Neutrophil count (/µl) × monocyte count (/µl)/lymphocyte count (/µl) ≥1,000-<2,000
  Neutrophil count (/µl) × monocyte count (/µl)/lymphocyte count (/µl)≥2,000
LIPI (28)
  dNLR ≤3 and lactate dehydrogenase ≤245 (U/l)0
  dNLR >3 or lactate dehydrogenase >245 (U/l)1
  dNLR >3 and lactate dehydrogenase >245 (U/l)2
CALLY (29)
  Albumin (g/dl) × lymphocyte count (/µl)/C-reactive protein (mg/dl)<5
  Albumin (g/dl) × lymphocyte count (/µl)/C-reactive protein (mg/dl)≥5

[i] PNI, prognostic nutritional index; GPS, Glasgow Prognostic Score; J-mGPS, Japanese-modified GPS; CAR, C-reactive protein-to-albumin ratio; NLR, neutrophil-to-lymphocyte ratio; LMR, lymphocyte-to-monocyte ratio; dNLR, derived NLR; NPS, neutrophil-platelet score; NLS, neutrophil-lymphocyte score; PLS, platelet-lymphocyte score; LMS, lymphocyte-monocyte score; PI, prognostic index; SII, systemic immune-inflammation index; SIRI, systemic inflammation response index; LIPI, lung immune prognostic index; CALLY, CRP albumin lymphocyte index; CRP, C-reactive protein.

Statistical analysis

Patients with complete data available were evaluated. The primary endpoint evaluated in the current study was OS, defined as the time from the start date of initial palliative chemotherapy to the date of death or final survival confirmation.

Basic statistics (absolute and relative frequencies for categorical variables; quartiles, maximum values, minimum values, means, or medians for continuous variables) were obtained to summarize the distribution of variables related to patient background factors, complications, other prognostic factors, and primary and secondary endpoints. Survival analyses were performed using OS as the primary endpoint. The censored cases included patients who were alive at the end date of the study or had dropped out of the study for any reason.

Kaplan-Meier curves (univariate analyses) were obtained for each inflammation-based prognostic markers associated with OS, and the log-rank test was utilized to compare survival curves. We compared the predictive quality of markers against OS using the Cox regression analysis concordance (rate) and the Akaike Information Criterion (AIC). Concordance was defined as follows. Assuming that (si, yi) is a pair of observed survival time (y) and scores (s), a pair of observations (i, j) was considered ‘concordant’ if (yi > yj, si < sj) or (yi < yj, si >sj), as these conditions are symmetrical. Conversely, it was considered ‘discordant’ if the conditions (yi < yj, si < sj) or (yi > yj, si > sj) applied. If c, d, and ts are counts of pairs that are concordant, discordant, or tied when using score s, then concordance C is defined as C=(c + ts/2)/(c + d + ts) using the proportion of concordant pairs. Although the above definition of ‘concordant’ and ‘discordant’ pairs appears to be reversed, survival is inversely correlated with the height of the hazard, and this definition has validity.

All analyses were performed using R, version 4.2.2 (R Foundation for Statistical Computing, Vienna, Austria). All statistical assessments were conducted as two-sided, and significance was determined with a threshold of P<0.05. All statistical analyses were two-sided, and P<0.05 was considered to indicate a statistically significant difference.

Results

Patients' characteristics

Among the 846 patients initially identified for this study, a total of 487 individuals were selected in the analysis due to the availability of complete data. Table II displays the characteristics of both the entire patient cohort and the subset included in the analysis. The two populations showed similar characteristics.

Table II.

Medical and demographic characteristics of patients.

Table II.

Medical and demographic characteristics of patients.

No. of patients

CharacteristicAll cases (n=846)Analyzed cases (n=487)
Age, years
  Median (quantile)70 (36, 64, 70, 76, 90)71 (37, 65, 71, 76, 90)
  ≥75, n (%)266 (31.4)166 (34.1)
Sex, n (%)
  Male503 (59.5)279 (57.3)
  Female343 (40.5)208 (42.7)
PS, n (%)
  0232 (27.4)124 (25.5)
  1290 (34.3)188 (38.6)
  253 (6.3)36 (7.4)
  N/A271 (32.0)139 (28.5)
BMI, kg/m2
  Median (quantile)19.7 (11.2, 17.4, 19.7, 21.9, 35.4)19.7 (11.2, 17.4, 19.7, 21.9, 35.4)
  Smoking status, n (%)
  Current or former (BI >0)217 (25.7)125 (25.7)
  Never smoked (BI=0)562 (66.4)333 (68.4)
  N/A67 (7.9)29 (5.9)
Pathology, n (%)
  Yes745 (88.1)435 (89.3)
    Adenocarcinoma418 (49.4)243 (49.9)
    Adenosquamous carcinoma7 (0.8)4 (0.8)
    Carcinoma/malignant neoplasm320 (37.8)188 (38.6)
  No (Radiological diagnosis only)101 (11.9)52 (10.7)
Primary disease site, n (%)
  Pancreas head359 (42.5)199 (40.9)
  Pancreas body232 (27.4)140 (28.7)
  Pancreas tail220 (26.0)129 (26.5)
  Not evaluable35 (4.1)19 (3.9)
Previous procedures, n (%)
  Surgery123 (14.5)47 (9.7)
  Endoscopic procedure44 (5.2)19 (3.9)
  Radiotherapy47 (5.6)25 (5.1)
Study period, n (%)
  Period A (2010–2013)268 (31.7)135 (27.7)
  Period B (2014–2016)251 (29.6)159 (32.7)
  Period C (2017–2020)327 (38.7)193 (39.6)
Hospital scale, n (%)
  High volume (n ≥50)509 (60.2)303 (62.2)
  Low volume (n <50)337 (39.8)184 (37.8)
Hospital type, n (%)
  Government-designated cancer hospital218 (25.7)137 (28.1)
  Prefectural designated cancer hospital316 (37.4)181 (37.2)
  General hospital312 (36.9)169 (34.7)
First-line systemic therapy, n (%)
  Gemcitabine monotherapy302 (35.7)167 (34.3)
  S-1 monotherapy197 (23.3)102 (20.9)
  Gemcitabine plus S-166 (7.8)38 (7.8)
  Gemcitabine plus nab-paclitaxel229 (27.1)146 (30.0)
  FOLFIRINOX52 (6.1)34 (7.0)

[i] PS, performance status; BMI, body mass index; N/A, not accessed; BI, Brickman index.

Comparison of the ratios and scores

Table III summarizes the OS statistics by 17 inflammation-based prognostic markers. All markers had statistically significant prognostic value. In addition, when comparing ratios computed as continuous variables with scores determined using categorical variables employing specific cutoff values, it was evident that similar hazard ratio (HR)s were observed. For instance, when we analyzed the HRs and their corresponding 95% confidential intervals for NLR (<3 compared to ≥3-<5 and ≥5) and NLS (0 compared to 1 and 2), the outcomes were as follows: 1.76 (1.35–2.29) and 2.67 (2.07–3.44) for NLR, and 1.60 (1.26–2.03) and 2.46 (1.75–3.47) for NLS, respectively.

Table III.

Overall survival summary statistics for each score.

Table III.

Overall survival summary statistics for each score.

Score/ration (%)EventsMedian overall survival (95% CI)Hazard ratio (95% CI)P-value
PNI
  ≥45200 (41.1)1397.4 (6.4–9.1)Reference-
  <45287 (58.1)2303.9 (3.0–5.1)1.82 (1.47–2.27)<0.0001
GPS
  0184 (37.8)1297.4 (6.6–9.1)Reference-
  1157 (32.2)1185.7 (4.1–7.9)1.33 (1.03–1.71)0.0286
  2146 (30.0)1222.7 (1.8–4.6)2.40 (1.86–3.11)<0.0001
mGPS
  0137 (28.1)918.2 (7.2–9.9)Reference-
  1185 (38.0)1436.0 (4.8–8.3)1.39 (1.07–1.81)0.0150
  2165 (33.9)1352.9 (1.9–4.6)2.63 (2.00–3.45)<0.0001
CAR
  ≤0.22216 (46.4)1478.3 (7.2–10.5)Reference-
  >0.22271 (55.6)2224.5 (3.2–5.5)2.06 (1.65–2.57)<0.0001
NLR
  <3177 (24.0)1198.3 (7.2–10.7)Reference-
  ≥3-<5152 (31.2)1155.9 (4.8–7.4)1.76 (1.35–2.29)<0.0001
  ≥5158 (32.4)1353.3 (2.8–4.6)2.67 (2.07–3.44)<0.0001
PLR
  ≤150203 (41.7)1517.1 (5.6–8.7)Reference-
  >150284 (58.3)2184.8 (4.0–6.4)1.35 (1.09–1.67)0.0056
LMR
  ≥2.40329 (67.6)2377.1 (6.0–8.6)Reference-
  <2.40158 (32.4)1323.9 (2.8–5.1)1.83 (1.47–2.28)<0.0001
dNLR
  <3326 (66.9)2337.2 (6.0–8.7)Reference-
  ≥3-<5114 (23.4)943.9 (3.0–5.2)2.02 (1.58–2.58)<0.0001
  ≥547 (9.7)422.2 (1.0–4.8)2.05 (1.46–2.88)<0.0001
NLS
  0157 (32.2)1128.3 (7.2–10.6)Reference-
  1273 (56.1)2045.0 (4.0–6.8)1.60 (1.26–2.03)0.00011
  257 (11.7)533.4 (2.8–4.8)2.46 (1.75–3.47)<0.0001
PLS
  0172 (35.3)1257.4 (6.4–9.1)Reference-
  1307 (63.0)2384.8 (3.9–6.0)1.48 (1.18–1.84)0.00066
  28 (1.6)64.7 (0.8-N/A)1.54 (0.64–3.69)0.33526
LMS
  0174 (35.7)1257.9 (6.6–9.5)Reference-
  1293 (60.2)2244.8 (4.0–6.1)1.52 (1.21–1.90)<0.0001
  220 (4.1)201.6 (1.0–4.6)4.34 (2.64–7.15)<0.0001
NPS
  0386 (79.3)2866.5 (5.3–8.1)Reference-
  188 (18.1)724.1 (2.9–5.3)1.56 (1.18–2.05)0.00155
  213 (2.7)113.7 (1.9-N/A)3.32 (1.78–6.18)<0.0001
PI
  0235 (48.3)1667.9 (6.9–9.6)Reference-
  1195 (40.0)1525.1 (4.1–6.6)1.58 (1.26–1.99)<0.0001
  257 (11.7)511.9 (1.6–3.9)3.35 (2.39–4.72)<0.0001
SII
  <30040 (8.2)308.7 (7.2–13.0)Reference-
  ≥300-<600131 (26.9)948.0 (6.7–10.9)1.10 (0.72–1.66)0.6690
  ≥600-<1,000121 (24.8)876.0 (4.8–8.7)1.76 (1.15–2.70)0.0089
  ≥1,000195 (40.0)1583.9 (3.0–4.9)2.50 (1.68–3.72)<0.0001
SIRI
  <50039 (8.0)279.6 (8.6–15.5)Reference-
  ≥500-<1,000120 (24.6)847.2 (5.3–10.1)1.60 (1.03–2.50)0.0370
  ≥1,000-<2,000140 (28.7)997.1 (5.1–8.7)1.96 (1.26–3.03)0.00266
  ≥2,000188 (38.6)1593.7 (2.9–4.9)3.37 (2.20–5.17)<0.0001
LIPI
  0267 (54.8)1847.9 (7.1–9.9)Reference-
  1153 (31.4)1264.2 (3.6–6.1)1.86 (1.47–2.34)<0.0001
  267 (13.8)592.2 (1.5–4.3)3.33 (2.45–4.53)<0.0001
CALLY
  <5431 (88.5)3325.1 (4.2–6.6)Reference-
  ≥556 (11.5)3710.3 (8.2–13.9)0.50 (0.35–0.70)<0.0001

[i] Data were analyzed using log-rank test. PNI, prognostic nutritional index; GPS, Glasgow Prognostic Score; mGPS, modified GPS; CAR, CRP-to-albumin ratio; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; LMR, lymphocyte-to-monocyte ratio; dNLR, derived neutrophil-to-lymphocyte ratio; NLS, neutrophil-lymphocyte score; PLS, platelet-lymphocyte score; LMS, lymphocyte-monocyte score; NPS, neutrophil-platelet score; PI, prognostic index; SII, systemic immune-inflammation index; SIRI, systemic inflammation response index; LIPI, lung immune prognostic index; CALLY, CRP albumin lymphocyte index; CRP, C-reactive protein; CI, confidence interval.

Kaplan-Meier curves

The Kaplan-Meier curves for OS by each inflammation-based prognostic marker are shown in Fig. 1. Each marker alone showed a significant correlation with prognosis. Additionally, for the majority of markers, each increment in the numerical value was linked to a gradual deterioration in prognosis.

Comparison of the inflammation-based prognostic markers

The concordance rates and AICs calculated for the 17 inflammation-based prognostic markers are shown in Table IV. The concordance values ranged from 0.616 to 0.679, and the AIC values ranged from 3784 to 3836. Among them, the mGPS correlated best with OS, followed by GPS and lung immune prognostic index (LIPI).

Table IV.

Prognostic scores/ratios, and their concordance rates and AIC for overall survival.

Table IV.

Prognostic scores/ratios, and their concordance rates and AIC for overall survival.

ScoreConcordanceAIC
mGPS0.6793,796
GPS0.6723,802
LIPI0.6693,784
PI0.6663,799
CAR0.6663,801
NLR0.6653,788
SIRI0.6573,797
SII0.6533,801
dNLR0.6503,808
PNI0.6503,814
LMR0.6453,817
NLS0.6423,816
LMS0.6323,814
NPS0.6283,827
CALLY0.6263,825
PLS0.6183,834
PLR0.6163,836

[i] AIC, Akaike Information Criterion; mGPS, modified GPS; GPS, Glasgow Prognostic Score; LIPI, lung immune prognostic index; PI, prognostic index; CAR, CRP-to-albumin ratio; NLR, neutrophil-to-lymphocyte ratio; SIRI, systemic inflammation response index; SII, systemic immune-inflammation index; dNLR, derived neutrophil-to-lymphocyte ratio; PNI, prognostic nutritional index; LMR, lymphocyte-to-monocyte ratio; NLS, neutrophil-lymphocyte score; LMS, lymphocyte-monocyte score; NPS, neutrophil-platelet score; CALLY, CRP albumin lymphocyte index; PLS, platelet-lymphocyte score; PLR, platelet-to-lymphocyte ratio; CRP, C-reactive protein.

Discussion

In this study, we compared inflammation-based prognostic markers helpful in predicting prognosis in patients undergoing chemotherapy for metastatic pancreatic cancer using real-world data from the Tokushukai medical database. While numerous prognostic and predictive markers have been reported, to the best of our knowledge, this study represents the most comprehensive comparison of inflammation-based prognostic markers to date. All 17 markers we evaluated demonstrated significant prognostic value, irrespective of whether they were ratio-based or scored systems. Among them, the mGPS, GPS, and LIPI emerged as the most accurate in predicting prognosis following first-line treatment of metastatic pancreatic cancer.

GPS is probably the most widely used prognostic score, with numerous reports supporting its usefulness. It was defined and reported by Forrest et al (17) in 2003 as CRP and albumin levels in patients with unresectable non-small cell lung cancer. According to multivariate analyses, the combined score of CRP and albumin was identified as an independent prognostic factor, and a validation study was subsequently reported in 2004 (30). The validity of the GPS has been reported in the Glasgow Inflammation Outcome Study (18,19), and the GPS is now widely used as an inflammation-based prognostic marker (11). In addition, several modifications of the GPS with adjusted cutoff values have been reported with improved accuracy. In studies of patients with colorectal cancer, low albumin levels did not correlate with poor prognosis because few patients have low albumin levels without elevated CRP levels. Thus, low albumin level alone looked less associated with poor prognosis (31), and a modified GPS that partially excludes the albumin level has been suggested (11). In a study examining the correlation between the mGPS and prognosis in Japanese patients with colorectal cancer, the best cutoff value of 0.5 mg/dl was reported for CRP based on its receiver operating characteristic curve (32).

LIPI, another recently reported score, is based on a combination of dNLR and LDH scores. The prognostic correlation of LIPI was reported by Mezquita et al in 2018 in patients treated with immune checkpoint inhibitors for advanced non-small cell lung cancer (28). LIPI has been developed as a prognostic score because dNLR and LDH level were independent prognostic factors in two large retrospective studies of patients with metastatic melanoma treated with ipilimumab (33) or pembrolizumab (34). The combination of these two factors has been reported to be of prognostic value in patients treated with immune checkpoint inhibitors for lung cancer (35,36), urothelial carcinoma (37), or solid tumors in general (38). Although the LIPI score was developed for patients treated with immune checkpoint inhibitors, the present study shows it is also a useful prognostic indicator in metastatic pancreatic cancer. The literature examining the usefulness of LIPI is still limited, and further studies are needed to determine whether it is reproducible in other cancers.

This study has a few limitations. First, due to the retrospective design, a number of patients had deficiencies in blood tests such as CRP, albumin, and LDH, which are not essential for chemotherapy induction. Accordingly, fewer patients qualified for the complete analysis. Second, new inflammation-based prognostic markers are introduced each year, and not all of them are included in this study. Third, this study included only Japanese subjects. Hence, its external validity may be limited in non-Asian populations. However, a nationwide study conducted in the Netherlands on metastatic pancreatic cancer identified CA19-9, albumin, CRP, LDH, C-reactive protein-to-albumin ratio, and GPS/mGPS as easily measurable prognostic biomarkers (39). Additionally, a systematic review from 2013 confirmed the prognostic potential of GPS/mGPS and NLR (40). Hence, it is safe to assume that at least GPS/mGPS are applicable prognostic biomarkers for both Asian and non-Asian patients with metastatic pancreatic cancer.

Lastly, additional parameters such as hemoglobin, CEA, and CA19-9 may also hold prognostic significance. Despite these limitations, the robustness of this study lies in the incorporation of a sizable cohort and the simultaneous assessment of real-world data for numerous inflammation-based prognostic markers. Our future research will evaluate each laboratory parameter and develop a novel prognostic score.

In conclusion, our real-world data analysis demonstrated that 17 inflammation-based markers that previously reported held significant prognostic value for patients with metastatic pancreatic cancer. Among these markers, the mGPS exhibited the highest accuracy.

Acknowledgements

The authors would like to thank Dr Shinnichi Higashiue (chair of the Medical Corporation Tokushukai, Tokyo, Japan and General Incorporated Association Tokushukai, Tokyo, Japan) and Dr Hisaaki Afuso (chief advisor of the Medical Corporation Tokushukai, Tokyo, Japan) for their support in conducting clinical research within the Tokushukai Group, and Mr. Katsuhiko Ozaki (President of Tokushukai Information System, Inc., Osaka, Japan) for their assistance in using the medical database.

Funding

Funding: No funding was received.

Availability of data and materials

The data generated in the present study may be requested from the corresponding author.

Authors' contributions

RS, YI and MO made substantial contributions to the study design and conception. RS, YF, MS and MH were responsible for data acquisition. RS and YI interpreted the data and drafted the manuscript. KU, TM, KO, NS and HM provided advice on research design and aided in the critical interpretation of this research for critical content. RS and YI confirm the authenticity of all the raw data. NS and HM comprehensively reviewed and approved the final version of this manuscript. All authors have read and approved the final version of the manuscript.

Ethics approval and consent to participate

The project adhered to the ethical guidelines for medical and biological research involving human subjects in Japan and followed the principles of The Declaration of Helsinki. Approval for the study was obtained from the Ethics Committee of the Tokushukai Group in April 2020 (approval no. TGE01427-024), and the study was registered in the UMIN Clinical Trial Registry under the number UMIN000050590. Patients were provided with information using opt-out methods and no patient declared not to participate.

Patient consent for publication

Patient consent for publication was obtained through opt-out methods.

Competing interests

The authors declare that they have no competing interests.

Glossary

Abbreviations

Abbreviations:

AIC

akaike information criterion

BMI

body mass index

CALLY

CRP albumin lymphocyte index

CAR

CRP-to-albumin ratio

CIs

confidence intervals

dNLR

derived neutrophil-to-lymphocyte ratio

FOLFIRINOX

fluorouracil, folic acid, oxaliplatin and irinotecan

GPS

Glasgow prognostic score

HRs

hazard ratios

LIPI

lung immune prognostic index

LMR

lymphocyte-to-monocyte ratio

LMS

lymphocyte-monocyte score

mGPS

modified Glasgow prognostic score

NLR

neutrophil-to-lymphocyte ratio

NLS

neutrophil-lymphocyte score

NPS

neutrophil-platelet score

OS

overall survival

PI

prognostic index

PLR

platelet-to-lymphocyte ratio

PLS

platelet-lymphocyte score

PNI

prognostic nutritional index

S-1

tegafur/gimeracil/oteracil

SII

systemic immune-inflammation index

SIRI

systemic inflammation response index

TREAD

tokushukai real-world data

UMIN

university hospital medical information network

References

1 

Siegel RL, Miller KD, Fuchs HE and Jemal A: Cancer statistics, 2021. CA Cancer J Clin. 71:7–33. 2021. View Article : Google Scholar : PubMed/NCBI

2 

SEER 5-year relative survival rate, 2012–2018, . Cancer Statistics Explorer Network. National Cancer Institute; United States: https://seer.cancer.gov/statistics-network/explorer/application.htmlApril 1–2023

3 

Cancer Information Service, National Cancer Center, Japan and National Cancer Registry (In Japanese) (Ministry of Health, Labour and Welfare), . https://ganjoho.jp/reg_stat/statistics/data/dl/en.htmlApril 1–2023

4 

Conroy T, Desseigne F, Ychou M, Bouché O, Guimbaud R, Bécouarn Y, Adenis A, Raoul JL, Gourgou-Bourgade S, de la Fouchardière C, et al: FOLFIRINOX versus gemcitabine for metastatic pancreatic cancer. N Engl J Med. 364:1817–1825. 2011. View Article : Google Scholar : PubMed/NCBI

5 

Okusaka T, Ikeda M, Fukutomi A, Ioka T, Furuse J, Ohkawa S, Isayama H and Boku N: Phase II study of FOLFIRINOX for chemotherapy-naïve Japanese patients with metastatic pancreatic cancer. Cancer Sci. 105:1321–1326. 2014. View Article : Google Scholar : PubMed/NCBI

6 

Ozaka M, Ishii H, Sato T, Ueno M, Ikeda M, Uesugi K, Sata N, Miyashita K, Mizuno N, Tsuji K, et al: A phase II study of modified FOLFIRINOX for chemotherapy-naïve patients with metastatic pancreatic cancer. Cancer Chemother Pharmacol. 81:1017–1023. 2018. View Article : Google Scholar : PubMed/NCBI

7 

Von Hoff DD, Ervin T, Arena FP, Chiorean EG, Infante J, Moore M, Seay T, Tjulandin SA, Ma WW, Saleh MN, et al: Increased survival in pancreatic cancer with nab-paclitaxel plus gemcitabine. N Engl J Med. 369:1691–1703. 2013. View Article : Google Scholar : PubMed/NCBI

8 

Shimoyama R, Imamura Y, Uryu K, Mase T, Fujimura Y, Hayashi M, Ohtaki M, Ohtani K, Shinozaki N and Minami H: Real-world treatment outcomes among patients with metastatic pancreatic cancer in Japan: The Tokushukai real-world data project. Mol Clin Oncol. 19:982023. View Article : Google Scholar : PubMed/NCBI

9 

McMillan DC: An inflammation-based prognostic score and its role in the nutrition-based management of patients with cancer. Proc Nutr Soc. 67:257–262. 2008. View Article : Google Scholar : PubMed/NCBI

10 

Buzby GP, Mullen JL, Matthews DC, Hobbs C and Rosato EF: Prognostic nutritional index in gastrointestinal surgery. Am J Surg. 139:160–167. 1980. View Article : Google Scholar : PubMed/NCBI

11 

McMillan DC: Systemic inflammation, nutritional status and survival in patients with cancer. Curr Opin Clin Nutr Metab Care. 12:223–226. 2009. View Article : Google Scholar : PubMed/NCBI

12 

Dolan RD, McSorley ST, Horgan PG, Laird B and McMillan DC: The role of the systemic inflammatory response in predicting outcomes in patients with advanced inoperable cancer: Systematic review and meta-analysis. Crit Rev Oncol Hematol. 116:134–146. 2017. View Article : Google Scholar : PubMed/NCBI

13 

Shimoyama R, Imamura Y, Uryu K, Mase T, Fujimura Y, Hayashi M, Ohtaki M, Ohtani K, Shinozaki N and Minami H: Real-world outcomes of systemic therapy in Japanese patients with cancer (Tokushukai REAl-world data project: TREAD): Study protocol for a nationwide cohort study. Healthcare (Basel). 10:21462022. View Article : Google Scholar : PubMed/NCBI

14 

Eba J and Nakamura K: Overview of the ethical guidelines for medical and biological research involving human subjects in Japan. Jpn J Clin Oncol. 52:539–544. 2022. View Article : Google Scholar : PubMed/NCBI

15 

National Cancer Registry (Ministry of Health, Labour and Welfare), tabulated by Cancer Information Service, National Cancer Center, Japan, . https://ganjoho.jp/reg_stat/statistics/data/dl/en.htmlApril 1–2023

16 

Onodera T, Goseki N and Kosaki G: Prognostic nutritional index in gastrointestinal surgery of malnourished cancer patients. Nihon Geka Gakkai Zasshi. 85:1001–1005. 1984.(In Japanese). PubMed/NCBI

17 

Forrest LM, McMillan DC, McArdle CS, Angerson WJ and Dunlop DJ: Evaluation of cumulative prognostic scores based on the systemic inflammatory response in patients with inoperable non-small-cell lung cancer. Br J Cancer. 89:1028–1030. 2003. View Article : Google Scholar : PubMed/NCBI

18 

Fairclough E, Cairns E, Hamilton J and Kelly C: Evaluation of a modified early warning system for acute medical admissions and comparison with C-reactive protein/albumin ratio as a predictor of patient outcome. Clin Med (Lond). 9:30–33. 2009. View Article : Google Scholar : PubMed/NCBI

19 

Nakahara K, Monden Y, Ohno K, Fujii Y, Hashimoto J, Kitagawa Y and Kawashima Y: Importance of biologic status to the postoperative prognosis of patients with stage III nonsmall cell lung cancer. J Surg Oncol. 36:155–160. 1987. View Article : Google Scholar : PubMed/NCBI

20 

Smith RA, Bosonnet L, Raraty M, Sutton R, Neoptolemos JP, Campbell F and Ghaneh P: Preoperative platelet-lymphocyte ratio is an independent significant prognostic marker in resected pancreatic ductal adenocarcinoma. Am J Surg. 197:466–472. 2009. View Article : Google Scholar : PubMed/NCBI

21 

Wilcox RA, Ristow K, Habermann TM, Inwards DJ, Micallef IN, Johnston PB, Colgan JP, Nowakowski GS, Ansell SM, Witzig TE, et al: The absolute monocyte and lymphocyte prognostic score predicts survival and identifies high-risk patients in diffuse large-B-cell lymphoma. Leukemia. 25:1502–1509. 2011. View Article : Google Scholar : PubMed/NCBI

22 

Proctor MJ, McMillan DC, Morrison DS, Fletcher CD, Horgan PG and Clarke SJ: A derived neutrophil to lymphocyte ratio predicts survival in patients with cancer. Br J Cancer. 107:695–699. 2012. View Article : Google Scholar : PubMed/NCBI

23 

Watt DG, Proctor MJ, Park JH, Horgan PG and McMillan DC: The neutrophil-platelet score (NPS) predicts survival in primary operable colorectal cancer and a variety of common cancers. PLoS One. 10:e01421592015. View Article : Google Scholar : PubMed/NCBI

24 

Dolan RD, McSorley ST, Park JH, Watt DG, Roxburgh CS, Horgan PG and McMillan DC: The prognostic value of systemic inflammation in patients undergoing surgery for colon cancer: comparison of composite ratios and cumulative scores. Br J Cancer. 119:40–51. 2018. View Article : Google Scholar : PubMed/NCBI

25 

Dolan RD, Alwahid M, McSorley ST, Park JH, Stevenson RP, Roxburgh CS, Horgan PG and McMillan DC: A comparison of the prognostic value of composite ratios and cumulative scores in patients with operable rectal cancer. Sci Rep. 10:179652020. View Article : Google Scholar : PubMed/NCBI

26 

Kasymjanova G, MacDonald N, Agulnik JS, Cohen V, Pepe C, Kreisman H, Sharma R and Small D: The predictive value of pre-treatment inflammatory markers in advanced non-small-cell lung cancer. Curr Oncol. 17:52–58. 2010.PubMed/NCBI

27 

Qi Q, Zhuang L, Shen Y, Geng Y, Yu S, Chen H, Liu L, Meng Z, Wang P and Chen Z: A novel systemic inflammation response index (SIRI) for predicting the survival of patients with pancreatic cancer after chemotherapy. Cancer. 122:2158–2167. 2016. View Article : Google Scholar : PubMed/NCBI

28 

Mezquita L, Auclin E, Ferrara R, Charrier M, Remon J, Planchard D, Ponce S, Ares LP, Leroy L, Audigier-Valette C, et al: Association of the lung immune prognostic index with immune checkpoint inhibitor outcomes in patients with advanced non-small cell lung cancer. JAMA Oncol. 4:351–357. 2018. View Article : Google Scholar : PubMed/NCBI

29 

Müller L, Hahn F, Mähringer-Kunz A, Stoehr F, Gairing SJ, Michel M, Foerster F, Weinmann A, Galle PR, Mittler J, et al: Immunonutritive scoring for patients with hepatocellular carcinoma undergoing transarterial chemoembolization: Evaluation of the CALLY index. Cancers (Basel). 13:50182021. View Article : Google Scholar : PubMed/NCBI

30 

Forrest LM, McMillan DC, McArdle CS, Angerson WJ and Dunlop DJ: Comparison of an inflammation-based prognostic score (GPS) with performance status (ECOG) in patients receiving platinum-based chemotherapy for inoperable non-small-cell lung cancer. Br J Cancer. 90:1704–1706. 2004. View Article : Google Scholar : PubMed/NCBI

31 

McMillan DC, Crozier JEM, Canna K, Angerson WJ and McArdle CS: Evaluation of an inflammation-based prognostic score (GPS) in patients undergoing resection for colon and rectal cancer. Int J Colorectal Dis. 22:881–886. 2007. View Article : Google Scholar : PubMed/NCBI

32 

Toiyama Y, Miki C, Inoue Y, Tanaka K, Mohri Y and Kusunoki M: Evaluation of an inflammation-based prognostic score for the identification of patients requiring postoperative adjuvant chemotherapy for stage II colorectal cancer. Exp Ther Med. 2:95–101. 2011. View Article : Google Scholar : PubMed/NCBI

33 

Ferrucci PF, Ascierto PA, Pigozzo J, Del Vecchio M, Maio M, Antonini Cappellini GC, Guidoboni M, Queirolo P, Savoia P, Mandalà M, et al: Baseline neutrophils and derived neutrophil-to-lymphocyte ratio: Prognostic relevance in metastatic melanoma patients receiving ipilimumab. Ann Oncol. 27:732–738. 2016. View Article : Google Scholar : PubMed/NCBI

34 

Weide B, Martens A, Hassel JC, Berking C, Postow MA, Bisschop K, Simeone E, Mangana J, Schilling B, Di Giacomo AM, et al: Baseline biomarkers for outcome of melanoma patients treated with pembrolizumab. Clin Cancer Res. 22:5487–5496. 2016. View Article : Google Scholar : PubMed/NCBI

35 

Huang L, Han H, Zhou L, Chen X, Xu Q, Xie J, Zhan P, Chen S, Lv T and Song Y: Evaluation of the lung immune prognostic index in non-small cell lung cancer patients treated with systemic therapy: A retrospective study and meta-analysis. Front Oncol. 11:6702302021. View Article : Google Scholar : PubMed/NCBI

36 

Zhang Q, Gong X, Sun L, Miao L and Zhou Y: The predictive value of pretreatment lactate dehydrogenase and derived neutrophil-to-lymphocyte ratio in advanced non-small cell lung cancer patients treated with PD-1/PD-L1 inhibitors: A meta-analysis. Front Oncol. 12:7914962022. View Article : Google Scholar : PubMed/NCBI

37 

Banna GL, Di Quattro R, Malatino L, Fornarini G, Addeo A, Maruzzo M, Urzia V, Rundo F, Lipari H, De Giorgi U and Basso U: Neutrophil-to-lymphocyte ratio and lactate dehydrogenase as biomarkers for urothelial cancer treated with immunotherapy. Clin Transl Oncol. 22:2130–2135. 2020. View Article : Google Scholar : PubMed/NCBI

38 

Liu H, Yang XL, Yang XY, Dong ZR, Chen ZQ, Hong JG and Li T: The prediction potential of the pretreatment lung immune prognostic index for the therapeutic outcomes of immune checkpoint inhibitors in patients with solid cancer: A systematic review and meta-analysis. Front Oncol. 11:6910022021. View Article : Google Scholar : PubMed/NCBI

39 

Strijker M, van Veldhuisen E, van der Geest LG, Busch OR, Bijlsma MF, Mohammad NH, Homs MY, van Hooft JE, Verheij J, de Vos-Geelen J, et al: Readily available biomarkers predict poor survival in metastatic pancreatic cancer. Biomarkers. 26:325–334. 2021. View Article : Google Scholar : PubMed/NCBI

40 

Ahmad J, Grimes N, Farid S and Morris-Stiff G: Inflammatory response related scoring systems in assessing the prognosis of patients with pancreatic ductal adenocarcinoma: A systematic review. Hepatobiliary Pancreat Dis Int. 13:474–481. 2014. View Article : Google Scholar : PubMed/NCBI

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Spandidos Publications style
Shimoyama R, Imamura Y, Uryu K, Mase T, Shiragami M, Fujimura Y, Hayashi M, Ohtaki M, Ohtani K, Shinozaki N, Shinozaki N, et al: Inflammation‑based prognostic markers of metastatic pancreatic cancer using real‑world data in Japan: The Tokushukai REAl‑world Data (TREAD) project. Oncol Lett 27: 136, 2024.
APA
Shimoyama, R., Imamura, Y., Uryu, K., Mase, T., Shiragami, M., Fujimura, Y. ... Minami, H. (2024). Inflammation‑based prognostic markers of metastatic pancreatic cancer using real‑world data in Japan: The Tokushukai REAl‑world Data (TREAD) project. Oncology Letters, 27, 136. https://doi.org/10.3892/ol.2024.14269
MLA
Shimoyama, R., Imamura, Y., Uryu, K., Mase, T., Shiragami, M., Fujimura, Y., Hayashi, M., Ohtaki, M., Ohtani, K., Shinozaki, N., Minami, H."Inflammation‑based prognostic markers of metastatic pancreatic cancer using real‑world data in Japan: The Tokushukai REAl‑world Data (TREAD) project". Oncology Letters 27.3 (2024): 136.
Chicago
Shimoyama, R., Imamura, Y., Uryu, K., Mase, T., Shiragami, M., Fujimura, Y., Hayashi, M., Ohtaki, M., Ohtani, K., Shinozaki, N., Minami, H."Inflammation‑based prognostic markers of metastatic pancreatic cancer using real‑world data in Japan: The Tokushukai REAl‑world Data (TREAD) project". Oncology Letters 27, no. 3 (2024): 136. https://doi.org/10.3892/ol.2024.14269