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. 2025 Aug 12;9:100221. doi: 10.1016/j.esmogo.2025.100221

Real-world evidence of chemotherapy effects in advanced pancreatic ductal adenocarcinoma: prognostic significance of TP53 status in gemcitabine plus nab-paclitaxel therapy

M Sugimori 1,2,3, A Hirotani 1,2, H Yamazaki 4, M Oshi 5, K Kawashima 5, H Tsuchiya 2, Y Kanemaru 6, Y Suzuki 6, S Onodera 6, H Miwa 2, A Nozaki 2, K Sugimori 2, C Kunisaki 1, M Kudo 1,7, M Morimoto 2, S Maeda 6,∗
PMCID: PMC12836593  PMID: 41647981

Abstract

Background

Gemcitabine plus nab-paclitaxel (GnP) and FOLFIRINOX (FFX) therapies are widely used to treat advanced pancreatic ductal adenocarcinoma (PDAC). This study aimed to identify the prognostic factors associated with these regimens, focusing on key genomic alterations in the ‘Big Four’ genes (KRAS, TP53, CDKN2A, and SMAD4).

Materials and methods

This retrospective observational study analysed real-world data from 5205 PDAC patients registered in the national database, Center for Cancer Genomics and Advanced Therapeutics (C-CAT), who underwent comprehensive genomic profiling between June 2019 and December 2023 in Japan. Clinical characteristics and genomic alterations were analysed. Time to progression (TTP) was compared between patients treated with GnP or FFX as first-line therapy. Gene alterations were classified as truncating or missense mutations to assess prognostic relevance.

Results

GnP was more frequently selected than FFX as first-line treatment (2315 versus 1181). FFX was more commonly used in younger, male patients without prior adjuvant therapy. After matching for age, sex, and adjuvant history, GnP demonstrated superior TTP compared with FFX (median TTP: 6.0 versus 5.5 months, P = 0.019). In the GnP group, TP53 alterations were associated with significantly shorter TTP compared with wild-type TP53 (median TTP: 5.8 versus 7.0 months; P < 0.0001). Furthermore, truncating TP53 mutations were linked to shorter TTP than missense mutations (median TTP: 5.3 versus 5.9 months; P = 0.021).

Conclusions

In Japanese real-world data, GnP showed superior TTP compared with FFX for advanced PDAC. TP53 status may serve as a prognostic biomarker in patients receiving GnP therapy.

Key words: genomic profiles; pancreatic ductal adenocarcinoma; time to progression; tumour protein p53 mutations; gemcitabine plus nab-paclitaxel therapy; leucovorin, fluorouracil, irinotecan, and oxaliplatin therapy

Highlights

  • •

    GnP therapy is more frequently selected than FFX for first-line treatment of advanced PDAC in Japan.

  • •

    GnP outperforms FFX in terms of TTP for advanced PDAC patients.

  • •

    Patients with pathogenic TP53 mutations exhibited poorer TTP in the GnP treatment group.

  • •

    Truncating TP53 mutations are linked to worse TTP over missense TP53 mutations in GnP-treated patients.

  • •

    TP53 status impacts TTP in GnP therapy, but not in FFX, suggesting regimen-specific prognostic significance.

Introduction

Pancreatic ductal adenocarcinoma (PDAC) is among the most aggressive cancers, with an extremely poor prognosis and an overall 5-year survival rate of ∼10%.1 PDAC is one of the leading causes of cancer-related deaths worldwide, currently ranking third or fourth. With an estimated 511 000 new cases annually, its incidence is projected to continue rising globally.2 Although early-stage diagnosis and surgical resection are the only curative treatments, 80% of cases are unresectable at the time of diagnosis. Therefore, the development of more effective systemic chemotherapeutic strategies remains an urgent priority.

Recent advances in genome analysis technology, including the development of next-generation sequencers, have promoted the use of precision medicine. Nevertheless, owing to the limited availability of targeted therapies, the number of patients who can benefit from mutation-matched treatments remains low, which limits the advantages of precision medicine. In 2020, the utility of precision medicine in PDAC in real-world settings was reported through the ‘Know Your Tumor’ program in the United States. In this program, a cohort of 46 patients with actionable genetic mutations who received corresponding therapies showed significantly prolonged overall survival compared with a cohort of 488 patients without actionable genetic alterations [2.58 years versus 1.32 years, hazard ratio (HR) 0.34, P < 0.0001].3 However, this approach is limited to patients with specific genetic abnormalities. Representative drugs for genetic mutations include poly ADP-ribose polymerase inhibitors for germline mutations in BRCA1/2, immune checkpoint inhibitors for tumours with high microsatellite instability (MSI-H) or high tumour mutational burden (TMB-H), NTRK inhibitors for NTRK1/2/3 fusion genes, and BRAF and MEK inhibitors for BRAF V600E mutations. However, the frequency of these gene alterations is extremely low in pancreatic adenocarcinomas.

The results of large-scale genome analysis, including The Cancer Genome Atlas data, have revealed that the gene alterations in PDAC are mainly clustered in four genes called the ‘Big Four’: KRAS, TP53, CDKN2A, and SMAD4. The reported frequencies of alteration in each of these genes are ∼90% for KRAS, 70% for TP53, 50% for CDKN2A, and 30% for SMAD4, suggesting a relatively uniform cancer type with less genetic variation.4 KRAS mutations, present in most PDACs, are particularly important, as pathogenic KRAS mutations impair intrinsic GTPase activity, leading to the constitutive activation of oncogenic signalling pathways. Development of drugs targeting KRAS mutations, such as RAS inhibitors and KRAS-selective degraders, is progressing; however, their clinical application requires time.5, 6, 7, 8 Although the development of novel drugs is indispensable in the long term, the effective utilisation of existing drugs is an urgent issue.

Although PDACs display relatively uniform genetic variability, their responses to chemotherapy often vary between cases in clinical practice. Thus far, fluorouracil, folinic acid, irinotecan, and oxaliplatin (FOLFIRINOX; FFX) and gemcitabine plus nab-paclitaxel (GnP) therapies are considered equally effective regimens for advanced PDAC, with separate randomised clinical trials demonstrating their superiority over gemcitabine monotherapy.9, 10, 11 Additionally, although not yet approved in Japan, the superiority of nanoliposomal irinotecan, fluorouracil, folinic acid, and oxaliplatin (NALIRIFOX) over GnP therapy was demonstrated in 2023.12 Both GnP and FFX regimens have been recommended as primary treatment options for advanced PDAC in Japan. However, the efficacy and prognostic factors of GnP and FFX therapies remain unclear, necessitating further investigation.

Since 2019, comprehensive genomic profiling (CGP) has been implemented under the national insurance system in Japan to facilitate precision medicine.13 The Center for Cancer Genomics and Advanced Therapeutics (C-CAT), a national data centre for CGP, was established to securely transfer CGP genomic data.14 The collected data are provided to third parties for research and development through the C-CAT. Additionally, CGP is intended for patients with locally advanced or metastatic solid tumours who have completed or are nearing completion of standard treatment. The clinical course of the standard treatments administered before CGP is recorded in the C-CAT database. Importantly, this database encompasses not only genetic alteration data but also clinical information, including treatment history, thereby establishing a real-world dataset that provides valuable insights into the current landscape of cancer care in Japan. Therefore, we aimed to identify the prognostic factors for PDAC in a real-world setting using the C-CAT national database of Japan.

Material and methods

Study population

This retrospective observational study used data from the C-CAT database. In addition to basic clinical information such as age, sex, cancer type, pathological diagnosis, and metastatic organs at the time of registration, the database also provides information regarding the chemotherapy regimens administered and treatment duration. Consent was obtained from each patient for their registration in this database and research use of their data by third parties.

This study analysed data from 5205 patients with PDAC who underwent CGP using tissue samples from either the OncoGuide NCC Oncopanel System (Sysmex Corporation, Hyogo, Japan) or FoundationOne CDx (Foundation Medicine, Boston, MA) between June 2019 and December 2023, the period during which the database was constructed.13,14 This retrospective observational study was approved by the Regional Committee for Medical and Health Research Ethics of Yokohama City University (approval number F230300007) and the review board of C-CAT (C-CAT control number CDU2023-014E02). According to Japanese insurance guidelines, CGP was conducted after or near the end of standard therapy, using tumour tissue typically collected at initial diagnosis. Re-biopsy was carried out if archival samples were inadequate.

Methods

We extracted data on patient age, patient sex, administered chemotherapy regimens, treatment courses, and CGP results from the C-CAT database. Regarding treatment courses, as data on regimens administered before CGP were entered based on the best-effort obligation of each medical institution, we extracted data for cases treated with GnP or FFX (including modified FFX10) regimens as first-line therapy, considering the data reliability. To assess treatment efficacy, we included only patients whose first-line therapy ended due to progression, excluding those discontinued for other reasons. Time to progression (TTP) was defined as the interval from treatment initiation to the final administration. Owing to the registry’s nature, some clinical variables were inconsistently entered, and implausible or incomplete data were treated as missing and excluded.

From the CGP results, we extracted data on the Big Four genes, BRCA1, BRCA2, MSI status, and TMB. Each mutation variant was cross-referenced using OncoKB (https://www.oncokb.org) and ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/) to determine its pathogenicity. Gene alteration patterns, such as loss, splice mutations, and nonsense mutations, were categorised as truncating changes (truncation), whereas missense mutations were classified as single-nucleotide variants (SNV).

Cell line data

The profiles of the Big Four genes in PDAC cell lines were obtained from publicly available databases, including Cellosaurus (https://www.cellosaurus.org) and Johns Hopkins University (https://pathology.jhu.edu/pancreas/research/genetics). The 50% inhibitory concentration (IC50) values of gemcitabine were retrieved from the Genomics of Drug Sensitivity in Cancer database (https://www.cancerrxgene.org).15

Statistical analysis

Fisher’s exact test was used to compare all categorical variables, whereas the Mann–Whitney U test was used to compare age. For multiple comparisons, one-way analysis of variance followed by Tukey’s multiple comparison test was used. TTP was estimated using the Kaplan–Meier method with 95% confidence intervals (CIs) and compared using the log-rank test. For variables showing significant differences, propensity score matching was carried out for adjustment. Additionally, a Cox proportional hazards regression model was used to estimate the HR and 95% CI for the association between mutation subsets and TTP. All statistical tests were two-sided, and the significance level was set at P < 0.05. Statistical analysis was carried out using Prism 10 (GraphPad Software, San Diego, CA) and R version 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria).

Results

Overview of gene alterations

Figure 1 presents an oncoplot overview of gene alterations in the 5205 patients with PDAC. Only variants identified as pathogenic genetic alterations are shown. Among the Big Four genes, the breakdown was as follows: KRAS [SNV 4854 patients (93.3%), amplification 105 patients (2.0%)], TP53 [truncation 1303 patients (25.0%), SNV 2607 patients (50.1%)], CDKN2A [truncation 2380 patients (45.7%), SNV 259 patients (5.0%)], and SMAD4 [truncation 1127 patients (21.7%), SNV 421 patients (8.1%)]. Other pathogenic alterations included BRCA1/2 in 186 patients (3.6%), MSI-H in 21 patients (0.4%), and TMB-H in 107 patients (2.1%). The pathogenic KRAS mutations in 4854 patients included G12D [2095 patients (43.2%)], G12V [1685 patients (34.7%)], G12R [604 patients (12.4%)], and Q61H [186 patients (3.8%)], and other mutations [284 patients (5.9%)] (Figure 1B).

Figure 1.

Figure 1

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Overview of gene alteration profiles of 5205 patients with pancreatic ductal adenocarcinoma (PDAC). (A) Oncoplot. Only pathogenic variants were plotted using OncoKB (https://www.oncokb.org) and ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/) for gene variant annotation. (B) Distribution of mutation variants in 4854 KRAS-mutated patients. Amp, gene amplification; MSI-H, high microsatellite instability; SNV, single-nucleotide variant (missense mutation); TMB-H, high tumour mutational burden; trun, truncating changes (loss, splice mutation, and nonsense mutation).

Overview of patients receiving first-line GnP or FFX regimens

A total of 2315 and 1181 patients received GnP and FFX, respectively, as first-line treatment. Among chemotherapy-naive patients, 1712 received GnP, and 941 received FFX. Among patients who received prior S-1 monotherapy as adjuvant chemotherapy after surgery, 359 received GnP, and 119 received FFX. For patients who underwent gemcitabine plus S-1 (GS) therapy as neoadjuvant chemotherapy, 106 received GnP, and 53 received FFX. Additionally, among those who received GS therapy as neoadjuvant chemotherapy and subsequent S-1 monotherapy as adjuvant chemotherapy, 138 patients received GnP, and 68 received FFX. Chemotherapy was discontinued due to disease progression in 994 patients treated with GnP (690 chemotherapy-naive, 304 adjuvant) and 527 patients treated with FFX (425 chemotherapy-naive, 102 adjuvant) (Figure 2). Data deemed implausible or incomplete were treated as missing and excluded (Supplementary Table S1, available at https://doi.org/10.1016/j.esmogo.2025.100221).

Figure 2.

Figure 2

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Overview of patients treated with GnP or FFX regimens as first-line treatment. The numbers in parentheses indicate cases in which the regimen was discontinued due to tumour progression, allowing for the evaluation of time to progression. C-CAT, The Center for Cancer Genomics and Advanced Therapeutics; FFX, FOLFIRINOX (fluorouracil, folinic acid, irinotecan, and oxaliplatin); GnP, gemcitabine plus nab-paclitaxel; GS, gemcitabine plus S-1.

Comparison of patient background factors between the first-line GnP and FFX groups

Patients receiving first-line GnP and FFX therapy for whom chemotherapy was discontinued due to disease progression showed significant differences in age, sex, and history of adjuvant therapy but not in the presence of pathogenic alterations in the Big Four genes and BRCA1/2 (Table 1). The FFX group had a significantly lower mean age [GnP 66.5 ± 0.3 years, FFX 61.4 ± 0.4 years, P < 0.0001 (Mann–Whitney U test)] and a higher proportion of males [GnP 512/994 (51.5%), FFX 324/527 (61.5%), P = 0.0002 (Fisher’s exact test)], whereas the GnP group showed significantly more cases with a history of adjuvant therapy [GnP 304/994 (30.6%), FFX 102/527 (19.4%), P < 0.0001 (Fisher’s exact test)]. Cox regression modelling to evaluate the impact of age, sex, history of adjuvant therapy, and first-line treatment regimen (GnP or FFX) on TTP showed that the FFX regimen was associated with worse TTP (Figure 3A, HR 1.21, 95% CI, 1.08-1.35). Comparison of TTP after propensity score matching based on age, sex, and history of adjuvant therapy revealed the superiority of the GnP regimen [Figure 3B, median TTP, GnP (498 patients) 6.0 months versus FFX (499 patients) 5.5 months, P = 0.019 (log-rank test)]. A subgroup analysis was carried out in the adjuvant chemotherapy-naive cohort. The FFX group had a lower mean age and more male patients (Supplementary Table S2, available at https://doi.org/10.1016/j.esmogo.2025.100221). After propensity score matching for age and sex, the GnP group showed superior TTP (Supplementary Figure S1, available at https://doi.org/10.1016/j.esmogo.2025.100221).

Table 1.

Patient background in the GnP and FFX therapy groups

GnP (n = 994) FFX (n = 527) P value
Age, years (mean ± SEM) 66.5 ± 0.3 61.4 ± 0.4 <0.0001a
Male n, (%) 512 (51.5) 324 (61.5) 0.0002b
KRAS alt n, (%) 916 (92.2) 486 (92.2) >0.9999b
TP53 alt n, (%) 768 (77.3) 407 (77.2) >0.9999b
CDKN2A alt n, (%) 494 (49.7) 290 (55.0) 0.0523b
SMAD4 alt n, (%) 695 (69.9) 369 (70.0) >0.9999b
BRCA1/2 alt n, (%) 42 (4.2) 14 (2.7) 0.1520b
Adj chemotherapy n, (%) 304 (30.6) 102 (19.4) <0.0001b
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P values < 0.05 are shown in bold.

Adj, post- or neoadjuvant chemotherapy; alt, pathogenic gene alteration; FFX, FOLFIRINOX (fluorouracil, folinic acid, irinotecan, and oxaliplatin); GnP, gemcitabine plus nanoparticle albumin-bound paclitaxel; SEM, standard error of the mean.

a

Mann–Whitney U test.

b

Fisher’s exact test.

Figure 3.

Figure 3

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Comparison of time to progression (TTP) between the first-line GnP and FFX groups. (A) Forest plot illustrating the hazard ratios for the impact of age, sex, history of adjuvant therapy, and first-line treatment regimen (GnP or FFX) on TTP using Cox regression modelling. (B) Comparison of TTP between GnP and FFX therapy using the Kaplan–Meier method; propensity score matching was carried out based on age, sex, and history of adjuvant therapy. Adj., preoperative or postoperative adjuvant therapy; FFX, FOLFIRINOX (fluorouracil, folinic acid, irinotecan, and oxaliplatin); GnP, gemcitabine plus nab-paclitaxel; HR, hazard ratio; mTTP, median time to progression.

Impact of Big Four gene alteration on TTP

Using Cox regression modelling, we investigated the impact of KRAS status on TTP in the GnP-treated cohort. KRAS status was categorised as wild-type KRAS, KRAS amplification, or one of the most common mutation variants (G12D, G12V, G12R, and Q61H). The analysis revealed that KRAS status had little to no impact on TTP. (Figure 4A, Supplementary Figure S2, available at https://doi.org/10.1016/j.esmogo.2025.100221). Examination of the effects of KRAS, TP53, CDKN2A, and SMAD4 alterations on TTP showed that pathogenic changes in TP53 were associated with worse TTP (Figure 4B, TP53 alteration. HR 1.36, 95% CI 1.17-1.59). A comparison of TTP based on the presence or absence of pathogenic TP53 alterations revealed a significantly better TTP for wild-type TP53 [Figure 4C, median TTP wild-type TP53 (226 patients), 7.0 months versus TP53-alteration (768 patients), 5.8 months, P < 0.0001 (log-rank test)]. In contrast, no significant association between TTP and Big Four gene or TP53 status was observed in the FFX group (Supplementary Figure S3A and B, available at https://doi.org/10.1016/j.esmogo.2025.100221). To further assess TP53 status, we stratified the propensity-matched cohort (as in Figure 3B) and compared TTP. The TTP difference by TP53 status remained significant only in the GnP group, not in FFX (Supplementary Figure S3C, available at https://doi.org/10.1016/j.esmogo.2025.100221).

Figure 4.

Figure 4

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Impact of ‘Big Four’ gene alterations on time to progression (TTP) in GnP therapy. (A) Forest plot illustrating hazard ratios for the impact of KRAS status on TTP using Cox regression modelling. (B) Forest plot illustrating the hazard ratios for the impact of Big Four gene status on TTP using Cox regression modelling. (C) Kaplan–Meier analysis of TTP based on the presence or absence of pathogenic TP53 alterations. alt, pathogenic gene alteration; amp, gene amplification; GnP, gemcitabine plus nab-paclitaxel; HR, hazard ratio; mTTP, median time to progression; mut, mutation; SNV, single-nucleotide variant (pathogenic missense mutation); wt, wild-type.

Impacts of Big Four gene alteration patterns on TTP

Cox regression modelling of the impacts of the types of alterations in KRAS, TP53, CDKN2A, and SMAD4 (truncation or SNV) on TTP in the GnP-treated group showed that both truncation and SNV changes in TP53 were significantly associated with worse TTP, with a higher HR observed for truncation than SNV (Figure 5A, TP53 truncation HR, 1.51, 95% CI 1.27-1.79; TP53 SNV HR, 1.27, 95% CI 1.08-1.49). Conversely, we observed no significant association between the types of alterations in KRAS, CDKN2A, or SMAD4 and TTP. A comparison of TTP based on the patterns of pathogenic TP53 alterations revealed a significantly better prognosis in the SNV group than in the truncation group [Figure 5B, median TTP TP53 SNV (509 patients), 5.9 months versus TP53 truncation (270 patients), 5.3 months, P = 0.021 (log-rank test)].

Figure 5.

Figure 5

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Impact of Big Four gene alteration patterns on time to progression (TTP) in GnP therapy. (A) Forest plot illustrating the hazard ratios for the impact of Big Four gene status on TTP using Cox regression modelling. (B) Kaplan–Meier analysis of TTP in GnP therapy, comparing the TP53 SNV and truncating mutation groups. amp, amplification; GnP, gemcitabine plus nab-paclitaxel; HR, hazard ratio; mTTP, median time to progression; SNV, single-nucleotide variant (pathogenic missense mutation); trun, truncating changes (loss, splice mutation, and nonsense mutation).

Comparison of gemcitabine sensitivity and TP53 status in PDAC cell lines

PDAC cell lines were categorised according to TP53 status into loss of function (LOF), gain of function (GOF), and no pathogenic alteration (Supplementary Table S3, available at https://doi.org/10.1016/j.esmogo.2025.100221). The IC50 values for gemcitabine were significantly lower for GOF and non-pathogenic alteration, suggesting greater sensitivity to gemcitabine than that in the LOF group [Supplementary Figure S4, available at https://doi.org/10.1016/j.esmogo.2025.100221; mean IC50 81.1 μM for LOF, 2.4 μM for GOF, and 13.2 μM for no alteration; LOF versus GOF P = 0.0011, LOF versus no alteration P = 0.016 (Tukey’s multiple comparison test)].

Discussion

In this study, we analysed real-world data from the C-CAT database to identify the prognostic factors for PDAC. Our findings demonstrated (i) the genomic profile of PDAC in Japan was consistent with previous reports,4 confirming the accumulation of alterations in the Big Four genes. (ii) GnP therapy was more frequently administered than FFX therapy as a first-line treatment. Moreover, FFX therapy was significantly more common in patients who were younger, male, and without a history of preoperative or postoperative adjuvant therapy. (iii) The GnP group showed significantly better TTP than the FFX group. (iv) Among patients administered GnP, the presence of pathogenic TP53 alterations was identified as a factor associated with poor prognosis, with TP53 truncating alterations being particularly associated with worse outcomes. Furthermore, the effect of TP53 status on TTP was observed only in the GnP group and not in the FFX group.

In Japan, the NALIRIFOX regimen is not yet approved, and GnP and FFX therapies are considered standard first-line regimens for advanced PDAC. However, following the results of the NAPOLI-1 trial,16 nanoliposomal irinotecan plus 5-fluorouracil and leucovorin(nal-IRI plus 5-FU/LV) was approved as a second-line treatment. This may have led to the frequent selection of gemcitabine-based regimens as the first-line therapy. Additionally, regimen selection may have been influenced by the high incidence of severe adverse effects associated with FFX therapy.17 The difference in efficacy between the GnP and FFX regimens remains unclear, as do prognostic factors; thus, further investigation is required. However, retrospective studies comparing GnP and FFX therapies have often reported a survival benefit favouring FFX.18 Conversely, a prospective randomised clinical trial in Japan comparing GnP, FFX, and SIROX (irinotecan, oxaliplatin, and S-1) demonstrated the superiority of GnP over the other treatments.19 Similarly, the results of the present study revealed the superiority of GnP over FFX therapy using real-world data. Gemcitabine has long been used as a key drug in PDAC treatment, though most combination regimens have shown limited efficacy.20,21 However, GnP has demonstrated clinical benefit.11 Importantly, it reduces cytidine deaminase expression by inducing reactive oxygen species, thereby suppressing gemcitabine inactivation.22,23 Based on these mechanisms, the GnP regimen maximally utilises the efficacy of gemcitabine.

TP53 is a tumour suppressor gene that is a key transcriptional regulator of cell cycle arrest, apoptosis, senescence, and DNA damage response. Tumours with pathogenic TP53 alterations result in poor prognosis across various cancer types. In general, tumour suppressor genes are inactivated via LOF alterations in both alleles. However, a notable feature of TP53 is that its most common mutations are missense mutations, typically involving single amino acid substitutions in the DNA-binding domain, leading to the accumulation of mutant Tp53 protein.24, 25, 26 These mutations promote tumourigenesis through dominant-negative effects or GOF activities.24, 25, 26 Differences between TP53 LOF and GOF have also been observed in mouse models. Genetically engineered mice harbouring heterozygous GOF mutations such as Tp53 R270H or Tp53 R172H develop metastatic epithelial tumours (carcinomas), lymphomas, and sarcomas. Interestingly, TP53 knockout mice do not develop epithelial tumours, either in the heterozygous or homozygous state, highlighting the distinct phenotypic differences between TP53 LOF and GOF mutations.27 In addition, genetically engineered mouse models that faithfully reproduce mutations in genes such as KRAS and TP53 have been developed, providing a platform for understanding disease mechanisms and drug development.28,29 Using a Cre-loxP system for pancreas ductal epithelium-specific gene engineering, models such as KPΔC (LSL-KrasG12D and Tp53flox/flox) and KPC (LSL-KrasG12D and Tp53R172H/+) have been established.29 Comparative studies using these models have reported differences in tumour malignancy and drug sensitivity, which have been attributed to variations in TP53 status.30, 31, 32, 33 Although studies on the relationship between TP53 status and gemcitabine sensitivity are limited, gemcitabine reportedly stabilizes nuclear Tp53, inducing DNA damage responses that activate Tp53 and trigger cell cycle arrest and apoptosis. Thus, pathogenic TP53 alterations contribute to gemcitabine resistance.34, 35, 36, 37

Despite preclinical evidence linking TP53 status to gemcitabine sensitivity, clinical evidence remains limited. In the CONKO-001 trial of postoperative adjuvant gemcitabine monotherapy, TP53-mutant patients had better outcomes than those in the observation group, but prognosis did not differ by TP53 status within the treatment arm.38 Notably, that study was conducted in a relatively small cohort receiving adjuvant gemcitabine monotherapy, and therefore differs in both clinical context and treatment setting from the present study, which focuses on GnP as first-line therapy in advanced PDAC. Li et al. reported that TP53 LOF, as suggested by immunohistochemistry-negative staining, was associated with poor prognosis in 1183 patients with PDAC who underwent resection.39 However, the association between specific chemotherapy regimens and their efficacy in treating advanced PDAC, as examined in the present study, remains unclear. In the present study, patients with pathogenic TP53 alterations administered GnP had worse TTP than those with wild-type TP53. Furthermore, patients with TP53 truncation mutations (corresponding to TP53 LOF) showed worse TTP compared with those with pathogenic SNV mutations (corresponding to TP53 GOF). This trend was not observed in the FFX-treated group, suggesting that TP53 truncation mutations may not solely confer higher biological malignancy but rather represent a characteristic specific to the GnP regimen.

Consistent with the findings of the present study, a comparison of TP53 status and gemcitabine sensitivity in human PDAC cell lines revealed significantly higher IC50 values in the LOF group. One exception was the PANC-08-13 cell line, which, despite lacking pathogenic TP53 alterations, exhibited a relatively high IC50 value (64.77 μM). Recent reports suggest that TP53-induced apoptosis is strongly influenced by transactivation of miR-34a.40 In the PANC-08-13 cell line, the deletion of the 1p36 locus containing miR-34a may partly explain its low sensitivity to gemcitabine.40 Taken together, although the GnP regimen is designed to maximize the therapeutic efficacy of gemcitabine, TP53-mediated cell cycle arrest and apoptosis in response to gemcitabine-induced DNA damage are critical for its antitumour effects. Therefore, TP53 status may play an important role in determining gemcitabine sensitivity.

This study has several limitations. First, the C-CAT data used in this study primarily contained information on treatment courses before CGP because data were entered based on the best-effort obligations of each medical institution. Consequently, treatment information post-CGP and overall survival could not be evaluated. Additionally, CGP was carried out during first-line treatment in some cases, while these data were missing in other cases. Data on key clinical factors—such as metastatic status at first-line initiation, surgery history, recurrence timing, and tumour markers—were unavailable. To ensure data reliability and assess antitumour efficacy, we included only patients who discontinued first-line therapy due to progression and compared TTP. However, as a retrospective study, selection bias and unmeasured confounding remain possible, representing a major limitation. While this is a study limitation, the large sample size allowed for meaningful evaluation.

Regarding the assessment of TP53 status, the genomic profile data utilised in this study did not allow the evaluation of the role of heterozygosity, making it impossible to distinguish between heterozygous and homozygous states. Consequently, the presence of wild-type alleles in tumour cells remains unknown. However, this study categorised truncating and SNV alterations as LOF and GOF changes, respectively, which is a limitation. Immunohistochemical staining and additional molecular analyses would provide a more precise evaluation of TP53 status. Although the NALIRIFOX regimen has not yet been approved in Japan, the superiority of NALIRIFOX over GnP therapy has been demonstrated.12 In the future, NALIRIFOX may become a commonly used first-line regimen; however, as highlighted in this study, regimen selection based on TP53 status may also become a valuable strategy for optimizing treatment.

In conclusion, our findings demonstrated the superiority of the GnP regimen over the FFX regimen as pharmacotherapy for advanced PDAC using real-world data from Japan. Among the Big Four genes associated with PDAC, pathological alterations in TP53, particularly truncation changes suggestive of LOF, may serve as prognostic factors for poor outcomes following GnP therapy.

Acknowledgements

We thank all the patients, investigators, colleagues, and their families. We also thank Hana Yamazaki, Hideki Yamamoto, and Eri Ikeda (Comprehensive Cancer Center, Yokohama City University Medical Center) as well as Nene Sakai, Yugo Ishino, Shotaro Tsunoda, Kazuki Endo, Ritsuko Oishi, Yuichi Suzuki, Akihiro Funaoka, Satoshi Komiyama, and Takashi Kaneko (Department of Gastroenterology, Gastroenterological Center, Yokohama City University Medical Center) for their support in this work. Finally, we thank Editage (www.editage.com) for the English language editing.

Funding

This work was supported in part by the Japan Society for the Promotion of Science with a Grant-in-Aid for Scientific Research (KAKENHI) [grant number 21K07946] and by the Yokohama Foundation for Advancement of Medical Science Research Grant for Medical Digital Transformation (no grant number).

Disclosure

The authors have declared no conflicts of interest.

Data sharing

All data generated or analysed in this study are stored in a secured research database. Although they are not publicly available, they can be made available by the corresponding author upon reasonable request.

Supplementary data

Supplementary Material
mmc1.docx (393.3KB, docx)

References

  • 1.Park W., Chawla A., O’Reilly E.M. Pancreatic cancer: a review. JAMA. 2021;326:851–862. doi: 10.1001/jama.2021.13027. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.World Cancer Research Fund International Pancreatic cancer statistics. 2022. https://www.wcrf.org/cancer-trends/pancreatic-cancer-statistics/ Available at. Accessed March 31, 2025.
  • 3.Pishvaian M.J., Blais E.M., Brody J.R., et al. Overall survival in patients with pancreatic cancer receiving matched therapies following molecular profiling: a retrospective analysis of the Know Your Tumor registry trial. Lancet Oncol. 2020;21:508–518. doi: 10.1016/S1470-2045(20)30074-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.The Cancer Genome Atlas Research Network Integrated genomic characterization of pancreatic ductal adenocarcinoma. Cancer Cell. 2017;32:185–203.e113. doi: 10.1016/j.ccell.2017.07.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Park W., Kasi A., Spira A.I., et al. 608O Preliminary safety and clinical activity of ASP3082, a first-in-class, KRAS G12D selective protein degrader in adults with advanced pancreatic (PC), colorectal (CRC), and non-small cell lung cancer (NSCLC) Ann Oncol. 2024;35:S486–S487. [Google Scholar]
  • 6.Popow J., Farnaby W., Gollner A., et al. Targeting cancer with small-molecule pan-KRAS degraders. Science. 2024;385:1338–1347. doi: 10.1126/science.adm8684. [DOI] [PubMed] [Google Scholar]
  • 7.Wei D., Wang L., Zuo X., Maitra A., Bresalier R.S. A small molecule with big impact: MRTX1133 targets the KRASG12D mutation in pancreatic cancer. Clin Cancer Res. 2024;30:655–662. doi: 10.1158/1078-0432.CCR-23-2098. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Yang J., Wang Q.L., Wang G.N., et al. A pan-KRAS degrader for the treatment of KRAS-mutant cancers. Cell Discov. 2024;10:70. doi: 10.1038/s41421-024-00699-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Conroy T., Desseigne F., Ychou M., et al. FOLFIRINOX versus gemcitabine for metastatic pancreatic cancer. N Engl J Med. 2011;364:1817–1825. doi: 10.1056/NEJMoa1011923. [DOI] [PubMed] [Google Scholar]
  • 10.Stein S.M., James E.S., Deng Y., et al. Final analysis of a phase II study of modified FOLFIRINOX in locally advanced and metastatic pancreatic cancer. Br J Cancer. 2016;114:737–743. doi: 10.1038/bjc.2016.45. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Von Hoff D.D., Ervin T., Arena F.P., et al. Increased survival in pancreatic cancer with nab-paclitaxel plus gemcitabine. N Engl J Med. 2013;369:1691–1703. doi: 10.1056/NEJMoa1304369. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Wainberg Z.A., Melisi D., Macarulla T., et al. NALIRIFOX versus nab-paclitaxel and gemcitabine in treatment-naive patients with metastatic pancreatic ductal adenocarcinoma (NAPOLI 3): a randomised, open-label, phase 3 trial. Lancet. 2023;402:1272–1281. doi: 10.1016/S0140-6736(23)01366-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Ebi H., Bando H. Precision oncology and the universal health coverage system in Japan. JCO Precis Oncol. 2019;3 doi: 10.1200/po.19.00291. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Kohno T., Kato M., Kohsaka S., et al. C-CAT: the national datacenter for cancer genomic medicine in Japan. Cancer Discov. 2022;12:2509–2515. doi: 10.1158/2159-8290.CD-22-0417. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Iorio F., Knijnenburg T.A., Vis D.J., et al. A landscape of pharmacogenomic interactions in cancer. Cell. 2016;166:740–754. doi: 10.1016/j.cell.2016.06.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Wang-Gillam A., Li C.P., Bodoky G., et al. Nanoliposomal irinotecan with fluorouracil and folinic acid in metastatic pancreatic cancer after previous gemcitabine-based therapy (NAPOLI-1): a global, randomised, open-label, phase 3 trial. Lancet. 2016;387:545–557. doi: 10.1016/S0140-6736(15)00986-1. [DOI] [PubMed] [Google Scholar]
  • 17.Muranaka T., Kuwatani M., Komatsu Y., et al. Comparison of efficacy and toxicity of FOLFIRINOX and gemcitabine with nab-paclitaxel in unresectable pancreatic cancer. J Gastrointest Oncol. 2017;8:566–571. doi: 10.21037/jgo.2017.02.02. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Klein-Brill A., Amar-Farkash S., Lawrence G., Collisson E.A., Aran D. Comparison of FOLFIRINOX vs gemcitabine plus nab-paclitaxel as first-line chemotherapy for metastatic pancreatic ductal adenocarcinoma. JAMA Netw Open. 2022;5 doi: 10.1001/jamanetworkopen.2022.16199. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Ohba A., Ozaka M., Ogawa G., et al. 1616O Nab-paclitaxel plus gemcitabine versus modified FOLFIRINOX or S-IROX in metastatic or recurrent pancreatic cancer (JCOG1611, GENERATE): a multicentred, randomized, open-label, three-arm, phase II/III trial. Ann Oncol. 2023;34 [Google Scholar]
  • 20.Burris H.A., Moore M.J., Andersen J., et al. Improvements in survival and clinical benefit with gemcitabine as first-line therapy for patients with advanced pancreas cancer: a randomized trial. J Clin Oncol. 1997;15:2403–2413. doi: 10.1200/JCO.1997.15.6.2403. [DOI] [PubMed] [Google Scholar]
  • 21.Hidalgo M. Pancreatic cancer. N Engl J Med. 2010;362:1605–1617. doi: 10.1056/NEJMra0901557. [DOI] [PubMed] [Google Scholar]
  • 22.Ciccolini J., Mercier C., Dahan L., André N. Integrating pharmacogenetics into gemcitabine dosing--time for a change? Nat Rev Clin Oncol. 2011;8:439–444. doi: 10.1038/nrclinonc.2011.1. [DOI] [PubMed] [Google Scholar]
  • 23.Frese K.K., Neesse A., Cook N., et al. nab-Paclitaxel potentiates gemcitabine activity by reducing cytidine deaminase levels in a mouse model of pancreatic cancer. Cancer Discov. 2012;2:260–269. doi: 10.1158/2159-8290.CD-11-0242. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Liu Y., Su Z., Tavana O., Gu W. Understanding the complexity of p53 in a new era of tumor suppression. Cancer Cell. 2024;42:946–967. doi: 10.1016/j.ccell.2024.04.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Chen X., Zhang T., Su W., et al. Mutant p53 in cancer: from molecular mechanism to therapeutic modulation. Cell Death Dis. 2022;13:974. doi: 10.1038/s41419-022-05408-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Giacomelli A.O., Yang X., Lintner R.E., et al. Mutational processes shape the landscape of TP53 mutations in human cancer. Nat Genet. 2018;50:1381–1387. doi: 10.1038/s41588-018-0204-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Bangham J. Tumour-suppressor super models. Nat Rev Cancer. 2005;5 84-84. [Google Scholar]
  • 28.Hingorani S.R., Petricoin E.F., Maitra A., et al. Preinvasive and invasive ductal pancreatic cancer and its early detection in the mouse. Cancer Cell. 2003;4:437–450. doi: 10.1016/s1535-6108(03)00309-x. [DOI] [PubMed] [Google Scholar]
  • 29.Hingorani S.R., Wang L., Multani A.S., et al. Trp53R172H and KrasG12D cooperate to promote chromosomal instability and widely metastatic pancreatic ductal adenocarcinoma in mice. Cancer Cell. 2005;7:469–483. doi: 10.1016/j.ccr.2005.04.023. [DOI] [PubMed] [Google Scholar]
  • 30.Morton J.P., Timpson P., Karim S.A., et al. Mutant p53 drives metastasis and overcomes growth arrest/senescence in pancreatic cancer. Proc Natl Acad Sci U S A. 2010;107:246–251. doi: 10.1073/pnas.0908428107. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Weissmueller S., Manchado E., Saborowski M., et al. Mutant p53 drives pancreatic cancer metastasis through cell-autonomous PDGF receptor β signaling. Cell. 2014;157:382–394. doi: 10.1016/j.cell.2014.01.066. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Escobar-Hoyos L.F., Penson A., Kannan R., et al. Altered RNA splicing by mutant p53 activates oncogenic RAS signaling in pancreatic cancer. Cancer Cell. 2020;38:198–211.e198. doi: 10.1016/j.ccell.2020.05.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Wörmann S.M., Song L., Ai J., Diakopoulos K.N., et al. Loss of P53 function activates JAK2-STAT3 signaling to promote pancreatic tumor growth, stroma modification, and gemcitabine resistance in mice and is associated with patient survival. Gastroenterology. 2016;151:180–193.e112. doi: 10.1053/j.gastro.2016.03.010. [DOI] [PubMed] [Google Scholar]
  • 34.Galmarini C.M., Clarke M.L., Falette N., Puisieux A., Mackey J.R., Dumontet C. Expression of a non-functional p53 affects the sensitivity of cancer cells to gemcitabine. Int J Cancer. 2002;97:439–445. doi: 10.1002/ijc.1628. [DOI] [PubMed] [Google Scholar]
  • 35.Fiorini C., Cordani M., Padroni C., Blandino G., Di Agostino S., Donadelli M. Mutant p53 stimulates chemoresistance of pancreatic adenocarcinoma cells to gemcitabine. Biochim Biophys Acta. 2015;1853:89–100. doi: 10.1016/j.bbamcr.2014.10.003. [DOI] [PubMed] [Google Scholar]
  • 36.Konaté M.M., Krushkal J., Li M.C., et al. Insights into gemcitabine resistance in pancreatic cancer: association with metabolic reprogramming and TP53 pathogenicity in patient derived xenografts. J Transl Med. 2024;22:733. doi: 10.1186/s12967-024-05528-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Hill R., Rabb M., Madureira P.A., et al. Gemcitabine-mediated tumour regression and p53-dependent gene expression: implications for colon and pancreatic cancer therapy. Cell Death Dis. 2013;4 doi: 10.1038/cddis.2013.307. e791-e791. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Sinn M., Sinn B.V., Treue D., et al. TP53 mutations predict sensitivity to adjuvant gemcitabine in patients with pancreatic ductal adenocarcinoma: next-generation sequencing results from the CONKO-001 trial. Clin Cancer Res. 2020;26:3732–3739. doi: 10.1158/1078-0432.CCR-19-3034. [DOI] [PubMed] [Google Scholar]
  • 39.Li B., Wang Y., Wang J., et al. Negative p53 expression confers worse prognosis in patients with resected pancreatic ductal adenocarcinoma: research focused on reinterpretation of immunohistochemical staining. Pancreas. 2022;51:1217–1224. doi: 10.1097/MPA.0000000000002175. [DOI] [PubMed] [Google Scholar]
  • 40.Chang T.-C., Wentzel E.A., Kent O.A., et al. Transactivation of miR-34a by p53 broadly influences gene expression and promotes apoptosis. Mol Cell. 2007;26:745–752. doi: 10.1016/j.molcel.2007.05.010. [DOI] [PMC free article] [PubMed] [Google Scholar]

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