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The Concept of Stroma AReactive Invasion Front Areas (SARIFA) as a new prognostic biomarker for lipid-driven cancers holds true in pancreatic ductal adenocarcinoma

Abstract

Background

Pancreatic ductal adenocarcinoma (PDAC) is a ‘difficult-to-treat’ entity. To forecast its prognosis, we introduced a new biomarker, SARIFA (stroma areactive invasion front areas), which are areas at the tumour invasion front lacking desmoplastic stroma reaction upon malignant invasion in the surrounding tissue, leading to direct contact between tumour cells and adipocytes. SARIFA showed its significance in gastric and colorectal carcinoma, revealing lipid metabolism alternations that promote tumour progression.

Methods

We reviewed the SARIFA status of 166 PDAC cases on all available H&E-stained tumour slides from archival Whipple-resection specimens. SARIFA positivity was defined as SARIFA detection in at least 66% of the available slides. To investigate alterations in tumour metabolism and microenvironment, we performed immunohistochemical staining for FABP4, CD36 and CD68. To verify and quantify a supposed delipidation of adipocytes, adipose tissue was digitally morphometrised.

Results

In total, 53 cases (32%) were classified as SARIFA positive and 113 (68%) as SARIFA negative. Patients with SARIFA-positive PDAC showed a significantly worse overall survival compared with SARIFA-negative cases (median overall survival: 11.0 months vs. 22.0 months, HR: 1.570 (1.082–2.278), 95% CI, p = 0.018), which was independent from other prognostic markers (p = 0.014). At the invasion front of SARIFA-positive PDAC, we observed significantly higher expression of FABP4 (p < 0.0001) and higher concentrations of CD68+ macrophages (p = 0.031) related to a higher risk of tumour progression. CD36 staining showed no significant expression differences. The adipocyte areas at the invasion front were significantly smaller, with mean values of 4021 ± 1058 µm2 and 1812 ± 1008 µm2 for the SARIFA-negative and -positive cases, respectively (p < 0.001).

Conclusions

SARIFA is a promising prognostic biomarker for PDAC. Its assessment is characterised by simplicity and low effort. The mechanisms behind SARIFA suggest a tumour-promoting increased lipid metabolism and altered immune background, both showing new therapeutic avenues.

Peer Review reports

Background

Worldwide, pancreatic cancer is the fourteenth most common malignancy but ranks seventh in cancer-related deaths [1] and is even prognosed to become the second most common cancer-related cause of mortality by 2030 [2]. The therapy still mainly relies on surgery (Whipple procedure) and adjuvant chemotherapy. However, in 85–90% of cases, tumours are primarily unresectable because of the infiltration of neighbouring structures or the presence of distant metastases [3]. Therapeutic improvements over the past two decades have been limited, and the disease is rightly described as a ‘difficult-to-treat’ entity with a five-year survival rate of only 11% [4]. Compared with other entities such as breast or lung cancer, there are only a few widely accepted prognostic factors routinely implemented in pathological diagnostic workups, including the factors of tumour-node-metastasis (TNM) classification, microsatellite instability status [5] and BRCA mutational analyses [6]; hence, there is a lack of further established and routinely applicable markers.

In our recent studies on gastric and colon adenocarcinomas [7, 8], we established a new histomorphological biomarker called SARIFAs (stroma areactive invasion front areas), which proved to be of independent prognostic relevance in these entities. Also in prostate cancer, a prognostic value could be demonstrated [9]. By definition, a SARIFA is characterised as an area at the tumour invasion front where there is an absence of desmoplastic stroma reaction on malignant invasion in the surrounding inobtrusive tissue, hence leading to direct contact between tumour cells and adipocytes. Detectable on haematoxylin and eosin (H&E)–stained slides, without the necessity for additional immunohistochemistry, simple to learn, and assessable in a short period with low interobserver variability, SARIFAs can be easily implemented in routine diagnostic workflow [7, 8].

Moreover, SARIFA positivity potentially reflects metabolic reprogramming in which tumour cells gain advantage from enhanced lipid supply, as a part of lipidomic remodeling which accompanies malignant transformation [10]. The access to exogenous lipid acids can be obtained through elevated expression of transport proteins including CD36, also known as fatty acid translocase, which with high affinity binds lipoproteins [11] and has been shown to be a negative prognostic marker e. g. in ovarian cancer, enabling the tumour cells a direct uptake of long chain fatty acids from neighboring adipocytes [12]. An alternative pathway of transportation of saturated and unsaturated lipids and fatty acids between tumour cells and adipocytes is conducted through fatty acid binding protein 4 (FABP4) [13, 14]. Both CD36 and FABP4 were shown to be upregulated in SARIFA-positive colorectal and gastric carcinomas [8, 15], an observation suggesting an altered lipid metabolism, which is a promising target for the development of new therapy concepts [16].

Our previous observations led to the question of whether SARIFAs also occur in pancreatic ductal adenocarcinoma (PDAC), an entity known for its pronounced stromal desmoplastic component, and if this concept could be adapted for a neoplasm with a considerably different biology compared with the originally addressed ones.

Therefore, we hypothesised that this phenomenon (i) also occurs in PDAC, (ii) is significantly prognostic and (iii) shows signs of an enhanced lipid metabolism. To confirm these hypotheses, we conducted the first analysis of a local PDAC patient collective and additionally explored the biochemical and immune background via immunohistochemistry.

Methods

Patient cohort and ethical approval

The study collection consisted of 166 patients who underwent the Whipple procedure at the University Medical Centre Augsburg between 2005 and 2015. The inclusion criteria were a postoperative survival of > 30 days and histologically confirmed diagnosis of PDAC in the resection specimen. Histopathological diagnoses other than PDAC, incomplete data on staging or death within the first 30 postoperative days led to exclusion.

Staging was performed according to the 8th Union for International Cancer Control staging system [17], grading according to WHO system [18], R-status was assessed according to the criteria proposed by Esposito et al. [19]. Both intrapancreatic and retroperitoneal resection margins were considered. The sample size was not statistically determined prior to investigation.

Histologic subtyping was not investigated. Because of the limited number of cases, a division between test and validation collections, as recommended by REMARK [20] and STROBE [21] guidelines, could not be conducted. The study was performed in compliance with the Declaration of Helsinki. The protocol was evaluated and approved by the ethical committee of the Ludwig Maximilian University of Munich (reference: 22–0437), with no declaration of consent from the patients required.

The clinical data were derived from Tumour Data Management, University Hospital of Augsburg, and completed with the information acquired from the patient files. The gathered data included: age at diagnosis, sex, adjuvant therapy, local recurrence and/or distant metastasis, last recorded medical contact or for deceased patients date of death, and in some cases additionally body mass index (BMI). The endpoint of the study was overall survival (OS), which was measured from the moment of diagnosis to death of any cause or last registered follow-up (censored entries). The median follow-up was calculated using the reverse Kaplan–Meier method [22]. The estimated median follow-up for the whole study collection was 78.2 months (66.5–113.5) and did not differ significantly between SARIFA-positive and -negative cases (p = 0.405).

The power of our survival analysis was calculated with the R-package ‘powerSurvEpi’. The power of our study was moderate with 0.685 (sample size n = 166, hazard ratio for SARIFA-positivity on OS from univariate analysis 1.56, significance level 0.05, event rate 0.75).

Histopathological SARIFA assessment

All given H&E-stained tumour slides (total 931, median 5 per case), each covering an area of approximately 220 mm2, were examined by two independent investigators (PG and BM) who were blinded to the clinicopathological data. A SARIFA was defined as the direct contact between at least five tumorous cells or a malignant gland and inconspicuous adipocytes at the invasion front or within the pancreas, as described recently by our group [7, 8]. Due to its histologic structure, there is also intraparenchymal adipose tissue in the pancreas. As we believe that direct tumour-adipocyte interactions represent an underlying tumour biology, we also considered this tumour-adipocyte contact as SARIFA-positivity—even though it is not at the invasion front. For consistency with our previous publications, we still refer to this as SARIFA. Representative images of both SARIFA-positive and SARIFA-negative cases are presented in Fig. 1. Because the morphological feature of a SARIFA itself occurs at a high frequency in PDAC and not only at the invasion front, we decided to renounce the restriction of the invasion front and counted also intra-parenchymal interactions with adipocytes. Moreover, we established a quantitative cut-off for classifying a case as SARIFA-positive based on the number of SARIFA-positive slides within each PDAC case, similarly to our study on SARIFA in prostate cancer [9]. For cut-off calculation, we used the R-package ‘bhm’ [23, 24]. The estimated optimal cut-off threshold based on the percentage of slides showing SARIFA was 0.6680 (95% confidence interval: 0.5842—0.711). By using this cut-off, we reached a SARIFA-positive frequency of 31.9%, which is similar to the SARIFA frequency in colorectal and gastric cancer [8, 15, 25]. For more details on our biomarker cut-off, refer to Figure S1. Following the independent assessment by two investigators, the cases with discrepant SARIFA scores were re-evaluated jointly by the same investigators, and a consensus diagnosis was made using a double-headed microscope.

Fig. 1
figure 1

SARIFA-positive and -negative cases; H&E staining. A Exemplary SARIFA-positive PDAC with tumorous cells directly adjacent to adipocytes at the invasion front; scale bar 200 µm. B Detailed picture of SARIFAs (marked with a dashed line); scale bar 100 µm. C SARIFA-negative PDAC with desmoplastic tumorous stroma separating malignant cells from surrounding fatty tissue; scale bar 200 µm. D Detailed picture of SARIFA-negative PDAC; H&E; scale bar 100 µm. SARIFA – stroma areactive invasion front area; PDAC – pancreatic ductal adenocarcinoma

Immunohistochemical studies

Additional immunohistochemical staining was performed to analyse and compare the expression of fatty acid metabolism–related proteins and the role of macrophages in SARIFAs, here corresponding to the results of preceding analyses on gastric carcinoma [8]. FABP4, CD36 and CD68 immunohistochemistry was performed on 30 SARIFA-positive and 30 SARIFA-negative representative cases, using 2- to 4-µm-thick, whole-slide, formalin-fixed paraffin-embedded sections. The staining was performed on a Leica Bond RX automated staining system (Leica, Wetzlar, Germany) according to the automated immunohistochemical protocol optimised for use on this platform (antibodies and dilution in Supplementary Information Table S1). The assessment of FABP4 and CD36 at both the invasion front and tumour centre was conducted using the immunoreactive score, which is a seven-tier semiquantitative scoring system, as proposed by Remmele and Stegner [26]. Therefore, staining intensity and the percentage of positive tumour cells were evaluated to calculate the score accordingly. The number of CD68-positive macrophages was counted on a representative high-power field at the tumour centre and invasion front. Representative areas at the invasion front and tumour centre were selected by visual impression.

Adipocyte morphometry

To verify and quantify a supposed delipidation of adipocytes, areas were digitally morphometrised. For that, H&E slides of 10 randomly selected SARIFA-positive and 10 SARIFA-negative cases from the above-described immunohistochemistry cohort were scanned using a 3D Histech Panoramic Scan II (3D Histech, Budapest, Hungary), and the morphometric measurements were performed using the CaseViewer 2.4 software (3DHistech, Budapest, Hungary). Two adipocytic areas each of the invasion front and of locations distanced at least 1 mm from the tumour were analysed by one investigator (BM) by measuring the area of 4 to 13 adipocytes (mean: 10 ± 2) (Fig. 2).

Fig. 2
figure 2

Principle of morphometric measurement of adipocytes. Exemplary PDAC slide with marked adipocytes at invasion front (InvF-1 and -2) and in distant locations (DistL-1 and -2), each with approx. 10 adipocytes; H&E; scale bar 200 µm. PDAC – pancreatic ductal adenocarcinoma

Statistical analysis

SPSS version 29.0 (SPSS, IBM, Chicago, IL, USA) and RStudio 2022.07.0 (R Foundation for Statistical Computing, Vienna, Austria) were used for statistical analysis. Correlations between clinicopathological data and SARIFA status were tested using Chi-squared tests or Fisher’s exact tests. The Kaplan–Meier method was used to depict the survival rates and the log-rank test to prove the significance of survival between the tested groups. The assessment of interobserver agreement was measured using kappa statistics. Relative risks were estimated by hazard ratios (HRs) calculated via Cox proportional hazard models.

Neither large language models nor artificial intelligence solutions were used in conducting the study.

Results

Clinicopathological characteristics of the cohort

In the examined population of 166 PDACs, 21 patients were diagnosed with a primary tumour (pT) in pT1 stage 108 with a pT2 stage and 37 with a pT3 stage. 123 patients presented nodal and 95 distant metastases (seven during surgery, on suspicion of intraoperatively detected abdominal lesions). A total of 112 (78%) patients received adjuvant chemotherapy (CTx) with different treatment regimens: Here, 74 were treated primarily with gemcitabine in monotherapy in a standard scheme of six courses and two with FOLFIRINOX schema. The remaining 36 patients received chemotherapy in other regimens (e.g., gemcitabine combined with erlotinib or radiotherapy) or did not complete the full treatment.

The median age at diagnosis was 68 years (range 44 to 85 years).

SARIFA in PDAC and correlation with clinicopathological characteristics

Overall, 53 cases (32%) were classified as SARIFA positive and 113 (68%) as SARIFA negative. SARIFA positivity was significantly associated with a higher rate of vascular invasion (p = 0.029) and lower frequency of adjuvant therapy (p = 0.009).

Other characteristics, including extension of pT, lymph node metastasis (pN) or distant metastasis, and R-status were not associated with SARIFA status (each p > 0.05). Detailed clinicopathological data are summarised in Table 1.

Table 1 Clinicopathological characteristics

Because obesity has previously shown significant correlations with the alternation of PDAC cell metabolism towards higher fatty acid uptake and a higher rate of tumour progression, we compared the body mass index between positive and negative patients (30 cases each) and found no significant correlation with the SARIFA status (p = 0.32; corresponding boxplot in Supplementary Information Figure S2).

Interobserver variability

Considering distinctive stromal desmoplasia in PDAC, the assessment of SARIFA status appeared to be a demanding task. Nevertheless, the interobserver variability between the first and last author corresponded with a kappa value of 0.56, showing moderate interobserver agreement.

Survival analysis

To analyse the prognostic relevance of SARIFA status in PDAC, we performed a Kaplan–Meier analysis and observed a distinct separation of survival curves (Fig. 3, log-rank, p = 0.018). Our analyses showed that patients with SARIFA-positive PDAC had a significantly worse OS compared with SARIFA-negative cases (median OS: 11.0 months vs. 22.0 months, HR: 1.570, 95% CI 1.082–2.278, p = 0.018).

Fig. 3
figure 3

PDAC patient survival dependency on SARIFA status. Kaplan–Meier curve of patients with SARIFA-positive and SARIFA-negative PDAC. P value of the log-rank test. SARIFA – stroma areactive invasion front areas; PDAC – pancreatic ductal adenocarcinoma

To assess the prognostic relevance of SARIFA status compared with other risk factors, we performed uni- and multivariate Cox regression analyses. In the univariate analysis, patients’ age, tumour grading, adjuvant chemotherapy and SARIFA status were significantly related to worse OS (Table 2). In the multivariate analysis, the following common risk factors were included: tumour grading, pT category, lymph node metastasis and invasion in blood or lymphatic vessels. Besides grading, SARIFA status remained significantly associated with shorter OS (p = 0.014), indicating that SARIFA status was an independent risk factor (Table 2).

Table 2 Uni- and multivariate Cox regression analysis regarding overall survival

To assess the effect of SARIFA status on the impact of adjuvant therapy and, hence, whether SARIFA status may be predictive, we performed further subgroup analyses. A significantly lower percentage of SARIFA-positive PDAC patients received adjuvant therapy compared to SARIFA-negative PDAC patients (64.4% vs. 83.8%, p = 0.009). In SARIFA-positive PDACs, adjuvant therapy was significantly associated with better OS (HR: 0.344, 95% CI 0.171–0.692, p = 0.002) but with a limited number of included patients (n total: 45, adjuvant therapy n = 29, no adjuvant therapy n = 16). This was also true within SARIFA-negative PDACs (n total: 99, adjuvant therapy n = 83, no adjuvant therapy n = 16) because adjuvant treatment was again associated with better OS (HR: 0.315, 95% CI 0.176–0.564, p < 0.001). These findings show that patients with PDAC benefit from adjuvant chemotherapy regardless of SARIFA-status [27]. Corresponding Kaplan–Meier curves are provided in the Supplementary Information (Figure S3).

Immunohistochemical expression of FABP4, CD36 and CD68 at SARIFAs

As mentioned above, we completed additional immunohistochemical studies focusing on lipid metabolism and tumour-associated macrophages at SARIFAs. Therefore, we investigated FABP4 and CD36 expression and the number of CD68+ macrophages. Tumour cells in SARIFA-positive cases showed higher expression of FABP4 at the invasion front than in SARIFA-negative cases (p < 0.0001). CD36 expression showed no statistically significant SARIFA-dependent changes (each p > 0.05). Moreover, CD68+ macrophages showed a higher density at the invasion front of SARIFA-positive than SARIFA-negative PDACs (p = 0.031). In SARIFA-negative regions, no differences regarding FABP4 and CD36 expression, as well as CD68+ macrophages, could be found (each p > 0.05). Immunohistochemical stains and the corresponding results are visualised in Fig. 4.

Fig. 4
figure 4

Expression of FABP4, CD36 and the presence of CD68-positive macrophages in SARIFA-positive and -negative cases at the tumour centre and invasion front. Boxplot showing differences in A FABP4 expression, B CD36 expression, and C CD68 + macrophage count at invasion front (IF) and tumour centre (TC) with exemplary images. SARIFA – stroma areactive invasion front area; IRS – immunoreactive score; HPF – high-power field. Scale bar 50 µm

Adipocyte morphometry

The adipocyte size in tumour-distanced locations did not differ between SARIFA-negative and -positive cases, with mean values of 5356 ± 1514 µm2 and 5140 ± 1559 µm2 (p = 0.659), respectively. The adipocyte areas at the invasion front were significantly smaller, with mean values of 4021 ± 1058 µm2 and 1812 ± 1008 µm2 for the SARIFA-negative and -positive cases, respectively. The differences between the SARIFA-positive invasion front adipocyte areas were highly significant (p < 0.001) (Fig. 5).

Fig. 5
figure 5

Adipocyte morphometry in SARIFA-positive and -negative cases at invasion front and locations distant from tumour. Boxplot showing differences in size of areas of approx. 10 adipocytes between SARIFA-positive and—negative cases in distant locations (DL) and invasion front (IF); SARIFA – stroma areactive invasion front areas

Discussion

The role of lipid metabolism has gained an increasing recognition in cancer research, as it offers a potential for new therapeutic targets [16]. With SARIFAs, we introduced a new prognostic biomarker whose biological significance lies in reflecting lipid-driven changes of tumour metabolism, so far demonstrated in gastric, colorectal and prostate cancer. In contrast to what might be expected, the occurrence of SARIFAs did not correlate with obesity [8, 9]. In the current study, we tested the hypotheses that SARIFA classification is applicable to and prognostic in PDAC and reveals signs of enhanced lipid metabolism.

Indeed, the SARIFA classification is applicable to PDAC, however, it had to be adapted compared to previous studies on gastric and colorectal cancer. The restriction to evaluate only the invasion front had to be abandoned and a quantitative cut-off had to be established. The evaluation was more challenging compared with the previous applications, resulting in a lower but still acceptable kappa value similar to the range of other experimental histological features [28]. Ongoing research on the classification of PDAC using deep neural networks opens up perspectives for further improvement of the evaluation of SARIFAs and is reassuring regarding the reproducibility of assessment [29].

Compared with other tumours, in PDAC, there is a lack of biomarkers, and the 8th Union for International Cancer Control TNM staging system plays the most important yet debatable role in this context [30, 31], indicating the need for new biomarkers. DNA/RNA-based or subtype analysis and gene expression profiling are cost- and time-consuming assays and currently often have limited availability [5, 32,33,34].

In line with our findings, several studies deploying deep-learning algorithms on digitised slides of colorectal cancer were able to identify a morphologically similar phenomenon described as a ‘tumour adipose feature’ or ‘adipocytes close to tumour cells’, proven to be prognostically highly relevant [35,36,37]. These studies support our hypothesis regarding the relevance of direct interactions between tumour cells and adipocytes. This is further strengthened by animal and in vitro PDAC models [38, 39], which have shown that adipocytes interact with and directly promote proliferation of malignant cells by increasing their fat uptake. To verify these findings in context of SARIFA-phenomenon in PDAC in human tissue, we performed immunohistochemistry for CD36 and FABP4, two proteins that play major roles in lipid metabolism. As a result, we were able to demonstrate a significantly increased FABP4 expression, particularly at SARIFAs. The immunohistochemical expression of CD36, a multiligand translocase enabling transmembranous allocation of oxidised low-density lipoproteins, does not differ between SARIFA-positive and SARIFA-negative PDAC. These findings deviate from the analyses by Grosser et al. in gastric cancers, where CD36 expression was more pronounced in SARIFA-positive tumours [8], indicating that the regulation mechanism in these two entities differs. Therefore, the uptake of fatty acid could rely on an alternative transport mechanism like extracellular vesicles [40]. FABP4 is responsible for intracellular transportation and metabolism of fatty acids and was previously reported to be associated with poor prognosis in PDAC [41] and other malignancies [42], which is in line with our findings. Its upregulated expression in the context of direct contact between malignant cells and adipocytes, even in a highly glucose-dependent malignancy such as pancreatic cancer [43], suggests a more distinctive role of fatty acids as an energy source and supply of building blocks for cellular membranes in SARIFA-positive PDAC. The fact that adipocytes shrink when coming into contact particularly with tumour cells, as shown by our morphometric analyses, suggests adipocytes’ delipidation and uptake of lipids by the tumour cells. There is a large body of evidence indicating that lipids play a fundamental role in tumour progression [16, 44]. Metabolic reprogramming has been included in the hallmarks of cancers [45]. It seems likely that SARIFAs could serve as a biomarker that is not only prognostic but also effective for the selection of tumours that are particularly driven by lipids, what on the other hand could pave the way for new treatment approaches specifically targeting lipid metabolism in SARIFA-positive PDAC, for example, by using metformin, CPT1 or FABP4 inhibitors [16, 46,47,48].

Among the several cell populations influencing both the growth and chemotherapy resistance of PDAC, tumour-associated macrophages drew our attention as an essential component of its microenvironment, playing a significant role in its biology [49, 50]. Moreover, CD68 + macrophages were upregulated at the SARIFAs in our study of gastric cancer [8]. In line with this, we observed higher concentrations of CD68 + macrophages at the invasion front of SARIFA-positive PDAC compared with SARIFA-negative cases, whereas in the tumour centre, there was no difference. Di Caro et al. showed that a higher density of macrophage infiltration at the tumour–stroma interface is associated with progression and distant metastasis of therapy-naïve PDAC [51] as a result of tumour-associated macrophages’ immunosuppressive activity. This mechanism could be co-responsible for both the development of SARIFAs and the non-favourable prognosis of SARIFA-positive PDAC cases, along with other alterations in local immune response [25].

The retrospective nature of the present study and the relatively low case numbers constitute its major limitations. A retrospective investigation of available material is inevitably combined with loss of some information, e. g. on clinical data. In similar retrospective studies, for example on tumor budding in PDAC [52] a cohort of 173 patients was investigated, a similar number comparing to our study. Nonetheless it was not possible to introduce test and validation groups in the study design. Therefore a future prospect study on SARIFA in PDAC would be of unpresented value, providing further aspects of the phenomenon described by us.

For our cohort only overall survival, which is considered the most important survival endpoint in cancer medicine [53], was available. Nevertheless, future studies should include disease-specific and progression-free survival to gain further insights into the prognostic relevance of SARIFA-status in PDAC.

Additional limitations are due to single site and temporal constraint of our study. We investigated a cohort of patients who were treated at our surgical department between 2005 and 2015. A multicenter study design could reassure objectivity of the observations and confirm our findings. Finally incorporating advanced methodologies, such as spatial expression profiling (similarly to earlier studies on SARIFA by Grosser et al.), could provide deeper scope in local tumor environment and contribute to more detailed description of changes in SARIFA, which seem to be of an immune nature [25] and still require further investigation, not only in PDAC but also in other entities.

Conclusions

The present study has shown first evidence for SARIFA status as a negative prognostic factor in PDAC. Compared with other novel biomarker approaches, which can only partly be evaluated on H&E-stained slides, SARIFA assessment is characterised by its simplicity and low effort, enabling reliable patient stratification. The mechanisms behind SARIFAs suggest the major role of an increased tumour-promoting lipid metabolism and altered immune background. Therefore, we propose SARIFA status as a novel H&E-based biomarker in PDAC that potentially could not only help better stratify patients but also guide new therapeutic avenues by interfering in the lipid metabolism of tumour cells, if subsequent studies build upon our findings.

Availability of data and materials

The datasets generated throughout the analysis can be obtained from the corresponding author upon reasonable request.

Abbreviations

CI:

Confidence interval

CTx:

Adjuvant chemotherapy

H&E:

Haematoxylin and eosin

HR:

Hazard ratio

OS:

Overall survival

PDAC:

Pancreatic ductal adenocarcinoma

pT:

Primary tumour

pN:

Lymph node metastasis

SARIFAs:

Stroma areactive invasion front areas

TNM classification:

Tumour-node-metastasis classification

References

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49.

    Article  PubMed  Google Scholar 

  2. Rahib L, Smith BD, Aizenberg R, Rosenzweig AB, Fleshman JM, Matrisian LM. Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res. 2014;74(11):2913–21.

    Article  CAS  PubMed  Google Scholar 

  3. Park W, Chawla A, O’Reilly EM. Pancreatic cancer: a review. JAMA. 2021;326(9):851–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin. 2022;72(1):7–33.

    Article  PubMed  Google Scholar 

  5. Dell’Aquila E, Fulgenzi CAM, Minelli A, Citarella F, Stellato M, Pantano F, Russano M, Cursano MC, Napolitano A, Zeppola T, et al. Prognostic and predictive factors in pancreatic cancer. Oncotarget. 2020;11(10):924–41.

    Article  PubMed  PubMed Central  Google Scholar 

  6. O’Reilly EM, Lee JW, Zalupski M, Capanu M, Park J, Golan T, Tahover E, Lowery MA, Chou JF, Sahai V, et al. Randomized, multicenter, phase II trial of gemcitabine and cisplatin with or without veliparib in patients with pancreas adenocarcinoma and a germline BRCA/PALB2 Mutation. J Clin Oncol. 2020;38(13):1378–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Martin B, Grosser B, Kempkens L, Miller S, Bauer S, Dhillon C, Banner BM, Brendel EM, Sipos E, Vlasenko D, et al. Stroma AReactive Invasion Front Areas (SARIFA)-a new easily to determine biomarker in colon cancer-results of a retrospective study. Cancers (Basel). 2021;13(19):4880.

    Article  PubMed  Google Scholar 

  8. Grosser B, Gluckstein MI, Dhillon C, Schiele S, Dintner S, VanSchoiack A, Kroeppler D, Martin B, Probst A, Vlasenko D, et al. Stroma AReactive Invasion Front Areas (SARIFA) - a new prognostic biomarker in gastric cancer related to tumor-promoting adipocytes. J Pathol. 2022;256(1):71–82.

    Article  CAS  PubMed  Google Scholar 

  9. Enke JS, Gross M, Grosser B, Sipos E, Steinestel J, Lohr P, Waidhauser J, Lapa C, Markl B, Reitsam NG. SARIFA as a new histopathological biomarker is associated with adverse clinicopathological characteristics, tumor-promoting fatty-acid metabolism, and might predict a metastatic pattern in pT3a prostate cancer. BMC Cancer. 2024;24(1):65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Koundouros N, Poulogiannis G. Reprogramming of fatty acid metabolism in cancer. Br J Cancer. 2020;122(1):4–22.

    Article  CAS  PubMed  Google Scholar 

  11. Calvo D, Gomez-Coronado D, Suarez Y, Lasuncion MA, Vega MA. Human CD36 is a high affinity receptor for the native lipoproteins HDL, LDL, and VLDL. J Lipid Res. 1998;39(4):777–88.

    Article  CAS  PubMed  Google Scholar 

  12. Ladanyi A, Mukherjee A, Kenny HA, Johnson A, Mitra AK, Sundaresan S, Nieman KM, Pascual G, Benitah SA, Montag A, et al. Adipocyte-induced CD36 expression drives ovarian cancer progression and metastasis. Oncogene. 2018;37(17):2285–301.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Sun N, Zhao X. Therapeutic implications of FABP4 in cancer: an emerging target to tackle cancer. Front Pharmacol. 2022;13:948610.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Hotamisligil GS, Bernlohr DA. Metabolic functions of FABPs–mechanisms and therapeutic implications. Nat Rev Endocrinol. 2015;11(10):592–605.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Reitsam NG, Grozdanov V, Loffler CML, Muti HS, Grosser B, Kather JN, Markl B. el biomarker SARIFA in colorectal cancer: highly prognostic, not genetically driven and histologic indicator of a distinct tumor biology. Cancer Gene Ther. 2024;31(2):207–16.

    Article  CAS  PubMed  Google Scholar 

  16. Butler LM, Perone Y, Dehairs J, Lupien LE, de Laat V, Talebi A, Loda M, Kinlaw WB, Swinnen JV. Lipids and cancer: emerging roles in pathogenesis, diagnosis and therapeutic intervention. Adv Drug Deliv Rev. 2020;159:245–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Brierley J, Gospodarowicz MK, Wittekind C. TNM classification of malignant tumours. 8th ed. Chichester, West Sussex; Hoboken: Wiley Blackwell; 2017.

    Google Scholar 

  18. Luttges J, Schemm S, Vogel I, Hedderich J, Kremer B, Kloppel G. The grade of pancreatic ductal carcinoma is an independent prognostic factor and is superior to the immunohistochemical assessment of proliferation. J Pathol. 2000;191(2):154–61.

    Article  CAS  PubMed  Google Scholar 

  19. Esposito I, Kleeff J, Bergmann F, Reiser C, Herpel E, Friess H, Schirmacher P, Buchler MW. Most pancreatic cancer resections are R1 resections. Ann Surg Oncol. 2008;15(6):1651–60.

    Article  PubMed  Google Scholar 

  20. Altman DG, McShane LM, Sauerbrei W, Taube SE. Reporting recommendations for tumor marker prognostic studies (REMARK): explanation and elaboration. BMC Med. 2012;10: 51.

    Article  PubMed  PubMed Central  Google Scholar 

  21. von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP, Initiative S. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370(9596):1453–7.

    Article  Google Scholar 

  22. Schemper M, Smith TL. A note on quantifying follow-up in studies of failure time. Control Clin Trials. 1996;17(4):343–6.

    Article  CAS  PubMed  Google Scholar 

  23. Chen BE, Jiang W, Tu D. A hierarchical Bayes model for biomarker subset effects in clinical trials. Comput Stat Data Anal. 2014;71:324–34.

    Article  Google Scholar 

  24. Biomarker Threshold Models. https://cran.r-project.org/web/packages/bhm/bhm.pdf. Accessed 30 May 2024.

  25. Reitsam NG, Markl B, Dintner S, Sipos E, Grochowski P, Grosser B, Sommer F, Eser S, Nerlinger P, Jordan F, et al. Alterations in natural killer cells in colorectal cancer patients with Stroma AReactive Invasion Front Areas (SARIFA). Cancers (Basel). 2023;15(3):994.

    Article  CAS  PubMed  Google Scholar 

  26. Remmele W, Stegner HE. Recommendation for uniform definition of an immunoreactive score (IRS) for immunohistochemical estrogen receptor detection (ER-ICA) in breast cancer tissue. Pathologe. 1987;8(3):138–40.

    CAS  PubMed  Google Scholar 

  27. Turner KM, Delman AM, Ammann AM, Sohal D, Olowokure O, Choe KA, Smith MT, Kharofa JR, Ahmad SA, Wilson GC, et al. Is there a benefit to adjuvant chemotherapy in resected, early stage pancreatic ductal adenocarcinoma? Ann Surg Oncol. 2022;29(7):4610–9.

    Article  Google Scholar 

  28. Karamitopoulou E, Wartenberg M, Zlobec I, Cibin S, Worni M, Gloor B, Lugli A. Tumour budding in pancreatic cancer revisited: validation of the ITBCC scoring system. Histopathology. 2018;73(1):137–46.

    Article  PubMed  Google Scholar 

  29. Kronberg RM, Haeberle L, Pfaus M, Xu HC, Krings KS, Schlensog M, Rau T, Pandyra AA, Lang KS, Esposito I, et al. Communicator-driven data preprocessing improves deep transfer learning of histopathological prediction of pancreatic ductal adenocarcinoma. Cancers (Basel). 2022;14(8):1964.

    Article  PubMed  Google Scholar 

  30. Allen PJ, Kuk D, Castillo CF, Basturk O, Wolfgang CL, Cameron JL, Lillemoe KD, Ferrone CR, Morales-Oyarvide V, He J, et al. Multi-institutional validation study of the american joint commission on cancer (8th Edition) changes for T and N staging in patients with pancreatic adenocarcinoma. Ann Surg. 2017;265(1):185–91.

    Article  PubMed  Google Scholar 

  31. Schouten TJ, Daamen LA, Dorland G, van Roessel SR, Groot VP, Besselink MG, Bonsing BA, Bosscha K, Brosens LAA, Busch OR, et al. Nationwide validation of the 8th american joint committee on cancer TNM Staging system and five proposed modifications for resected pancreatic cancer. Ann Surg Oncol. 2022;29(9):5988–99.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Collisson EA, Sadanandam A, Olson P, Gibb WJ, Truitt M, Gu S, Cooc J, Weinkle J, Kim GE, Jakkula L, et al. Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy. Nat Med. 2011;17(4):500–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Meijer LL, Garajova I, Caparello C, Le Large TYS, Frampton AE, Vasile E, Funel N, Kazemier G, Giovannetti E. Plasma miR-181a-5p downregulation predicts response and improved survival after FOLFIRINOX in pancreatic ductal adenocarcinoma. Ann Surg. 2020;271(6):1137–47.

    Article  PubMed  Google Scholar 

  34. Luke F, Haller F, Utpatel K, Krebs M, Meidenbauer N, Scheiter A, Spoerl S, Heudobler D, Sparrer D, Kaiser U, et al. Identification of disparities in personalized cancer care-a joint approach of the German WERA consortium. Cancers (Basel). 2022;14(20):5040.

    Article  PubMed  Google Scholar 

  35. Wulczyn E, Steiner DF, Moran M, Plass M, Reihs R, Tan F, Flament-Auvigne I, Brown T, Regitnig P, Chen PC, et al. Interpretable survival prediction for colorectal cancer using deep learning. NPJ Digit Med. 2021;4(1):71.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Foersch S, Glasner C, Woerl AC, Eckstein M, Wagner DC, Schulz S, Kellers F, Fernandez A, Tserea K, Kloth M, et al. Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer. Nat Med. 2023;29(2):430–9.

    Article  CAS  PubMed  Google Scholar 

  37. L’Imperio V, Wulczyn E, Plass M, Muller H, Tamini N, Gianotti L, Zucchini N, Reihs R, Corrado GS, Webster DR, et al. Pathologist validation of a machine learning-derived feature for colon cancer risk stratification. JAMA Netw Open. 2023;6(3):e2254891.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Okumura T, Ohuchida K, Sada M, Abe T, Endo S, Koikawa K, Iwamoto C, Miura D, Mizuuchi Y, Moriyama T, et al. Extra-pancreatic invasion induces lipolytic and fibrotic changes in the adipose microenvironment, with released fatty acids enhancing the invasiveness of pancreatic cancer cells. Oncotarget. 2017;8(11):18280–95.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Grippo PJ, Fitchev PS, Bentrem DJ, Melstrom LG, Dangi-Garimella S, Krantz SB, Heiferman MJ, Chung C, Adrian K, Cornwell ML, et al. Concurrent PEDF deficiency and Kras mutation induce invasive pancreatic cancer and adipose-rich stroma in mice. Gut. 2012;61(10):1454–64.

    Article  CAS  PubMed  Google Scholar 

  40. Maas SLN, Breakefield XO, Weaver AM. Extracellular vesicles: unique intercellular delivery vehicles. Trends Cell Biol. 2017;27(3):172–88.

    Article  CAS  PubMed  Google Scholar 

  41. Luo Y, Yang Z, Li D, Liu Z, Yang L, Zou Q, Yuan Y. LDHB and FABP4 are associated with progression and poor prognosis of pancreatic ductal adenocarcinomas. Appl Immunohistochem Mol Morphol. 2017;25(5):351–7.

    Article  CAS  PubMed  Google Scholar 

  42. Tian W, Zhang W, Zhang Y, Zhu T, Hua Y, Li H, Zhang Q, Xia M. FABP4 promotes invasion and metastasis of colon cancer by regulating fatty acid transport. Cancer Cell Int. 2020;20:512.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Liu YH, Hu CM, Hsu YS, Lee WH. Interplays of glucose metabolism and KRAS mutation in pancreatic ductal adenocarcinoma. Cell Death Dis. 2022;13(9):817.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Vasseur S, Guillaumond F. Lipids in cancer: a global view of the contribution of lipid pathways to metastatic formation and treatment resistance. Oncogenesis. 2022;11(1):46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Hanahan D. Hallmarks of cancer: new dimensions. Cancer Discov. 2022;12(1):31–46.

    Article  CAS  PubMed  Google Scholar 

  46. Tennant DA, Duran RV, Gottlieb E. Targeting metabolic transformation for cancer therapy. Nat Rev Cancer. 2010;10(4):267–77.

    Article  CAS  PubMed  Google Scholar 

  47. Yang K, Lu HH, Zhao W, Zhao Q. Efficacy and safety of metformin in combination with chemotherapy in cancer patients without diabetes: systematic review and meta-analysis. Front Oncol. 2023;13: 1176885.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Floresta G, Patamia V, Zagni C, Rescifina A. Adipocyte fatty acid binding protein 4 (FABP4) inhibitors. An update from 2017 to early 2022. Eur J Med Chem. 2022;240:114604.

    Article  CAS  PubMed  Google Scholar 

  49. Ho WJ, Jaffee EM, Zheng L. The tumour microenvironment in pancreatic cancer - clinical challenges and opportunities. Nat Rev Clin Oncol. 2020;17(9):527–40.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Ye H, Zhou Q, Zheng S, Li G, Lin Q, Wei L, Fu Z, Zhang B, Liu Y, Li Z, et al. Tumor-associated macrophages promote progression and the Warburg effect via CCL18/NF-kB/VCAM-1 pathway in pancreatic ductal adenocarcinoma. Cell Death Dis. 2018;9(5):453.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Di Caro G, Cortese N, Castino GF, Grizzi F, Gavazzi F, Ridolfi C, Capretti G, Mineri R, Todoric J, Zerbi A, et al. Dual prognostic significance of tumour-associated macrophages in human pancreatic adenocarcinoma treated or untreated with chemotherapy. Gut. 2016;65(10):1710–20.

    Article  PubMed  Google Scholar 

  52. Lohneis P, Sinn M, Klein F, Bischoff S, Striefler JK, Wislocka L, Sinn BV, Pelzer U, Oettle H, Riess H, et al. Tumour buds determine prognosis in resected pancreatic ductal adenocarcinoma. Br J Cancer. 2018;118(11):1485–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Mailankody S, Prasad V. Overall survival in cancer drug trials as a new surrogate end point for overall survival in the real world. JAMA Oncol. 2017;3(7):889–90.

    Article  PubMed  Google Scholar 

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Acknowledgements

We are grateful to Eva Sipos, Alexandra Martin and Christian Beul for their excellent technical assistance.

Funding

Open Access funding enabled and organized by Projekt DEAL. No funds, grants, or other support was received. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

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B.M., P.G., N.G.R. and B.G. contributed to the study’s conception and design; P.G., N.G.R., B.G., F.S., A.P., J.W., G.S. and B.M. contributed to the data acquisition process; P.G. and N.G.R. contributed to the analysis and interpretation of the data. N.G.R. and B.M. contributed equally and share last authorship. All authors revised the article critically, contributed to it with reflective improvements and approved the final version. B.M. is the guarantor of this work and, as such, had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

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Correspondence to Przemyslaw Grochowski.

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Grochowski, P., Grosser, B., Sommer, F. et al. The Concept of Stroma AReactive Invasion Front Areas (SARIFA) as a new prognostic biomarker for lipid-driven cancers holds true in pancreatic ductal adenocarcinoma. BMC Cancer 24, 768 (2024). https://doi.org/10.1186/s12885-024-12519-9

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