Mitogen signal-associated pathways, energy metabolism regulation, and mediation of tumor immunogenicity play essential roles in the cellular response of malignant pleural mesotheliomas to platinum-based treatment: a retrospective study.

BACKGROUND: Malignant pleural mesothelioma (MPM) is a rare malignant tumor associated with asbestos exposure, with infaust prognosis and overall survival below 20 months in treated patients. Platinum is still the backbone of the chemotherapy protocols, and the reasons for the rather poor efficacy of platinum compounds in MPM remain largely unknown. Therefore, we aimed to analyze differences in key signaling pathways and biological mechanisms in therapy-naïve samples and samples after chemotherapy in order to evaluate the effect of platinum-based chemotherapy.
METHODS: The study cohort comprised 24 MPM tumor specimens, 12 from therapy-naïve and 12 from patients after platinum-based therapy. Tumor samples were screened using the NanoString nCounter platform for digital gene expression analysis with an appurtenant custom-designed panel comprising a total of 366 mRNAs covering the most important tumor signaling pathways. Significant pathway associations were identified by gene set enrichment analysis using the WEB-based GEne SeT AnaLysis Toolkit (WebGestalt).
RESULTS: We have found reduced activity of TNF (normalized enrichment score: 2.03), IL-17 (normalized enrichment score: 1.93), MAPK (normalized enrichment score: 1.51), and relaxin signaling pathways (normalized enrichment score: 1.42) in the samples obtained after platinum-based therapy. In contrast, AMPK (normalized enrichment score: -1.58), mTOR (normalized enrichment score: -1.50), Wnt (normalized enrichment score: -1.38), and longevity regulating pathway (normalized enrichment score: -1.31) showed significantly elevated expression in the same samples.
CONCLUSIONS: We could identify deregulated signaling pathways due to a directed cellular response to platinum-induced cell stress. Our results are paving the ground for a better understanding of cellular responses and escape mechanisms, carrying a high potential for improved clinical management of patients with MPM. 2021 Translational Lung Cancer Research. All rights reserved.

Introduction

Malignant pleural mesothelioma (MPM) is a malignancy originating from pleural mesothelium, a serosal membrane covering the thoracic cavity. This rare type of cancer is associated with asbestos exposure and has a very poor prognosis: less than 20 months if treated and less than nine months if untreated (1-3). Only a small fraction of patients is suitable for potentially curative surgery through radical pleurectomy (4). Despite encouraging results of the recent trials with immunotherapy in patients with MPM (5), most patients receive the standard chemotherapy, approved by both the U.S. Food and Drug Administration (FDA) as well as the European Medicines Agency (EMA). This treatment, consisting of cisplatin and pemetrexed, prolongs overall survival by several months, with especially epithelioid mesothelioma benefiting from this therapy (6). Response rates for monotherapies either with cisplatin or carboplatin are low, ranging from 6% to 16% (7,8).

Platinum cytotoxicity is based on the formation of bulky DNA adducts induced by covalent binding of platinum and alteration of DNA bases (9), leading to both DNA inter- and (1,2 or 1,3) intra-strand cross-linking (10-17). Platinum compounds prevent normal cell replication and trigger apoptosis (12,16,18) unless adducts from genomic DNA are repaired (15). Even a single DNA cross-link, if not repaired, can be lethal (19). Platinum-induced DNA damage leads to TP53 induced cell cycle arrest and apoptosis (20,21). However, it is uncertain whether DNA repair mechanisms are the key factors associated with an impaired therapy response since other cellular mechanisms, such as cell cycle control and intracellular signaling pathways, are thought to contribute to resistance to platinum. Since the identification of MPM molecular properties may help to identify the reason(s) of the inadequate treatment response, several studies addressed this question (7,8,19,22-25). Unfortunately, the reasons for the poor efficacy of platinum-based therapy in MPM patients are still largely unknown. Given these therapeutic limitations, further basic research is needed to provide a more detailed insight into the pathogenesis and biology of MPM and enable opportunities for innovative and novel treatment strategies (1).

Therefore, we aimed to analyze differences in key signaling pathways and biological mechanisms in therapy-naïve samples compared to samples after chemotherapy to evaluate the effect of platinum-based chemotherapy.

We present the following article in accordance with the STROBE reporting checklist (available at https://dx.doi.org/10.21037/tlcr-21-201).

Methods

Study cohort and experimental design

For this exploratory mRNA study, twenty-four formalin-fixed paraffin-embedded (FFPE) tumor specimens of MPM patients of the epithelioid histological subtype treated at the West German Cancer Centre or the West German Lung Centre between 2005 and 2009 were screened. Diagnoses of MPM, epithelioid subtype, were confirmed by two experienced pathologists (TMA, JWO), based on the latest WHO classification (26), and patients were re-staged using the UICC/AJCC staging system of 2017 (27). Half of the collected samples were taken prior to any therapy, and the other half after chemotherapy. All patients received platinum-based chemotherapy. The radiologic response rate was assessed using the modified Response Evaluation Criteria in Solid Tumors (modRECIST), which have been validated in MPM (28,29). Surveillance for this study cohort stopped on August 31, 2014. Complete follow-up was available for all patients with reported deaths in more than 96% (23/24). Disease progression under therapy was observed in approximately 80% (19/24) of patients. Clinico-pathological data are summarized in .

Table 1

Clinico-pathological data of the study cohort

Characteristics	Data
Number of patients	24
Therapy naïve
Yes	12
No	12
Gender
Male	22
Female	2
Histological subtype
Epithelioid	24
UICC/AJCC 2017
1A	1
1B	10
3A	1
3B	7
4	4
Undetermined	1
Age
Mean/median age at diagnosis (years)	65/63.5
Range (years)	51–80
OS
Deceased	23
Alive	1
Mean/median OS (months)	19.4/16.3
PFS
Remission (initial)	10
Progression (total)	20
Mean/median PFS (months)	9.7/6.3

This retrospective study conformed to the principles outlined in the Declaration of Helsinki (as revised in 2013). It was approved by the Ethics Committee of the Medical Faculty of the University Duisburg-Essen (identifier: 14-5775-BO), and individual consent for this retrospective analysis was waived.

RNA extraction and RNA integrity assessment

Three to five 4 µm thick paraffin sections of whole tumor per sample were used for total RNA isolation. RNA was isolated by a semi-automatic Maxwell purification system (Maxwell RSC RNA FFPE Kit, AS1440, Promega). Purification was performed according to the manufacturer’s instructions. RNA was eluted in 50 µL RNase-free water and stored at −80 °C. RNA concentration was measured using a Qubit 2.0 fluorimeter (Life Technologies) appertaining to the RNA broad-range assay. Total RNA integrity and quality were assessed using a Fragment Analyzer (Advanced Analytical Technologies, Ames, IA, USA) and appertaining DNF-489 standard sensitivity RNA analysis kit.

Digital gene expression analysis

Gene expression patterns were screened for prognostic and predictive biomarkers using the NanoString nCounter platform for digital gene expression analysis with an appurtenant custom-designed panel comprising a total of 366 mRNAs (9 potential reference genes (ACTB, GAPDH, HPRT1, CLTC, GUSB, NEDD8, PGK1, TTC1 and TUBB as well as 357 target genes) covering the most important tumor signaling pathways. For each sample, 200 ng of total RNA was processed. The sample preparation in the nCounter Prep Station (NanoString) was carried out by using the high-sensitivity protocol (3 h preparation). The cartridges were measured at 555 fields of view in the nCounter Digital Analyzer (NanoString).

NanoString data processing

NanoString data processing was performed with the R statistical programming environment (v3.4.2) using the NanoStringNorm (30) and the NAPPA package, respectively.

Considering the counts obtained for positive control probe sets raw NanoString counts for each gene were subjected to a technical factorial normalization, carried out by subtracting the mean counts plus two-times standard deviation from the CodeSet inherent negative controls. Afterward, a biological normalization using the geometric mean of the inherent reference genes was performed. Additionally, all counts with P>0.05 after one-sided t-test versus negative controls plus 2× standard deviations were interpreted as not expressed to overcome basal noise.

Statistical analysis

Statistical and graphical analyses were performed with the R statistical programming environment (v4.0.2) (31). Before explorative data analysis, the Shapiro-Wilks-test was applied to test for normal distribution of each data set. For dichotomous variables, the Wilcoxon Mann-Whitney rank sum test (non-parametric) or two-sided student’s t-test (parametric) was used. For ordinal variables with more than two groups, the Kruskal-Wallis test (non-parametric) or ANOVA (parametric) was used to detect group differences. Double dichotomous contingency tables were analyzed using Fisher’s exact test. To test dependence to ranked parameters with more than two groups, Pearson’s Chi-squared test was used. Correlations between metrics were analyzed using Spearman’s rank correlation test.

Adaption of profiles for diagnostic purposes was explored with the supervised machine learning tools “Classification and Regression Tree Algorithm” (CART) as implemented in the “rpart” library of R (according to Therneau and Atkinson) using a leave-one-out cross-validation. Additionally, conditional interference trees (CTree), as implemented in the “party” library of R was modeled, leading to a non-parametric class of tree-structured regression models.

Pathway analysis is based on the KEGG database and was performed using the “pathview” package of R. Differences were specified by –log2 fold change between means (if parametric) or medians (if non-parametric) of compared groups. Significant pathway associations were identified by gene set enrichment analysis using the WEB-based GEne SeT AnaLysis Toolkit (WebGestalt) (31-33). Each run was executed with 1,000 permutations. Finally, all associations were ranked according to the false discovery rate (FDR) (P<0.05).

Due to the multiple statistical tests, the P values were adjusted by using the FDR. The level of statistical significance was defined as P≤0.05 after adjustment.

Results

Gene expression analysis

Tumor specimens collected after therapy show clearly elevated expression levels of H2AFX (P=0.005), PCNA (P=0.024), XRCC1 (P=0.029), DUOX2 (P=0.034) and NELL2 (P=0.034) in comparison to treatment-naïve samples. In contrast, gene expression of DUSP6 (P=0.010), MYC (P=0.020), IL8 (P=0.045) and ANGPT2 (P=0.050) was found to be significantly decreased after treatment ().

Figure 1

Presentation of different expression of analysed genes in samples with and without therapy. One can see elevated expression of H2AFX, PCNA, XRCC1, DUOX2 and NELL2) in samples after therapy, and decreased expression of DUSP6, MYC and IL8.

Additionally, unsupervised conditional inference tree (CTree) analysis identifies H2AFX and PMS1 as a pattern for biunique classification between neo- and adjuvant treatment. In particular, H2AFX expression below 1,088 counts leads to a subgroup highly enriched for pretreatment samples (P=0.006). Subsequently, a group with more than 1,088 H2AFX counts and high expression of PMS1 (P=0.027, cut-off: 222 counts) contains only specimens after platinum-based therapy ().

Figure 2

Presentation of unsupervised conditional inference tree (CTree) analysis. H2AFX and PMS1 have been identified as important separators of samples before and after therapy, based on their counts. H2AFX expression below 1,088 counts revealed a subgroup highly enriched for pretreatment samples. A group with more than 1,088 H2AFX counts, and more than 222 PMS1 counts includes only specimens after platinum-based therapy.

Looking into the factors involved in cell cycle control and progression in the pretreatment samples revealed high levels of TGF-β, p14, p16 (both encoded by CDKN2A), and p15 (encoded by CDKN2B), indicating a robust regulatory mechanism for G1-to-S-phase transition. In samples collected after platinum-based therapy, an upregulation of the following genes was observed: SMAD2 and SMAD3, markers for apoptosis/cell cycle arrest induction, ATM/ATR and their downstream cascade including p53 and PCNA, and also various CDKs such as CDK1, CDK4, and CDK6 as well as their respective cyclins B, D, and H ().

Figure 3

Expression of different pathways involved in cell cycle control. Pathways marked in red were overexpressed before, green pathways were overexpressed after treatment. High levels of TGF-β, p14, p16, and p15 were detected in pretreatment samples. In samples collected after platinum-based therapy, SMAD2 and SMAD3 were upregulated of the following genes was observed: markers for apoptosis/cell cycle arrest induction, ATM/ATR and their downstream cascade including p53 and PCNA, and also various CDKs such as CDK1, CDK4, and CDK6 as well as their respective cyclins B, D, and H.

Gene set enrichment analysis (GSEA)

GSEA was performed (Table S1) to identify affected biological processes. GSEA utilizes molecular interaction networks outlined by the Kyoto Encyclopedia of Gene and Genomes (KEGG) to map out increased gene expression in a specific molecular pathway, depending on a response variable (treatment) (Figures S1,S2).

AMPK signaling pathway (normalized enrichment score: −1.58), mTOR signaling pathway (normalized enrichment score: −1.50), hepatocellular carcinoma (normalized enrichment score: −1.48), endocytosis (normalized enrichment score: −1.46), Wnt signaling pathway (normalized enrichment score: −1.38), breast cancer (normalized enrichment score: −1.37), longevity regulating pathway (normalized enrichment score: −1.31), glioma (normalized enrichment score: −1.25), endometrial cancer (normalized enrichment score: −0.59) and ErbB signaling pathway (normalized enrichment score: −0.46) are enriched in samples after platinum therapy. On the other hand, activity of TNF signaling pathway (normalized enrichment score: 2.03), rheumatoid arthritis (normalized enrichment score: 1.99), IL-17 signaling pathway (normalized enrichment score: 1.93), osteoclast differentiation (normalized enrichment score: 1.84), Leishmaniasis (normalized enrichment score: 1.82), parathyroid hormone synthesis, secretion and action (normalized enrichment score: 1.68), MAPK signaling pathway (normalized enrichment score: 1.51), NOD-like receptor signaling pathway (normalized enrichment score: 1.45), relaxin signaling pathway (normalized enrichment score: 1.42) and Th1 and Th2 cell differentiation (normalized enrichment score: 1.42) are associated with specimens collected from therapy-naïve patients ().

Figure 4

Results of the GSEA analysis between specimens collected prior and after chemotherapy. AMPK signaling pathway, mTOR signaling pathway, hepatocellular carcinoma, endocytosis, Wnt signaling pathway, breast cancer, longevity regulating pathway, glioma, endometrial cancer and ErbB signaling pathway are enriched in samples after platinum therapy, indicated by yellow bars. Activity of TNF signaling pathway, rheumatoid arthritis, IL-17 signaling pathway, osteoclast differentiation, Leishmaniasis, parathyroid hormone synthesis, secretion and action, MAPK signaling pathway, NOD-like receptor signaling pathway, relaxin signaling pathway and Th1 and Th2 cell differentiation are enriched in specimens collected from therapy-naïve patients, indicated by blue bars. Highly significant associations showing P<0.05 after false discovery rate (FDR) are highlighted in dark-shaded colors.

Gene expression and clinical stage

Eight genes were identified to be differentially expressed depending on MPMs’ UICC stage. MAPK6 (P=0.007, rho=0.75), ICAM5 (P=0.041, rho=0.62) and ICAM4 (P=0.050, rho=0.60) show low expression levels in UICC 1A and B cases, increasing and reaching its plateau in higher UICC stages 3A, 3B and 4, where no differences in expression can be shown. Similarities can be observed for PARP1 (P=0.007, rho=0.74), DUOX2 (P=0.023, rho=0.67), SLCO3A1 (P=0.023, rho=0.67) and BRCA1 (P=0.041, rho=0.63) expression levels, showing a continuous rise from low expression in UICC 1A/B, rising from UICC 3A to 3B with higher gene expression to outstanding expression in UICC stage 4. For HGF (P=0.050, rho=0.60), the increased expression could only be detected in UICC stage 4.

Gene expression and stratification for overall survival (OS) and progression-free survival (PFS)

Gene expression levels of 22 targets have been identified to impact OS. Longer OS is associated with lower expression of 19 genes: ICAM5 (P=0.002), CD82 (P=0.004), FYN (P=0.010), WNT1 (P=0.011), FGF1 (P=0.013), CDK5R2 (P=0.015), MMP8 (P=0.019), IGSF5 (P=0.033), SLC7A6 (P=0.033), SLIT1 (P=0.036), SLCO1B3 (P=0.037), WISP3 (P=0.039), MLH1 (P=0.040), GLUT1 (P=0.043), IGSF5 (P=0.043), TCEB2 (P=0.048) and claudins, including CLDN14 (P=0.004), CLDN3 (P=0.010) and CLDN11 (P=0.011). In addition, CLDN2 (P=0.055) and CLDN5 (P=0.057) closely failed significance but show a clear trend for prolonged survival at low gene expression levels. On the other hand, expression of CDKN2A (P=0.022) and high expression of TIAM1 (P=0.024) and JAM2 (P=0.039) are detected in patients with longer OS ().

Figure 5

Relation of gene expression with overall survival.

Applying unsupervised, decision-tree-based machine learning (COX-CIT), the expression of MMP9 can stratify patients into three different risk groups, with different OS. Using two cut-offs (609 counts, P=0.004, and 1,492 counts, P=0.001) it can identify the patients with the worst prognosis (Node 4) ().

Figure 6

Results of the unsupervised, decision-tree-based machine learning for OS and PFS. (A) Unsupervised, decision-tree-based machine learning (COX-CIT) and the expression of MMP9 using two cut-offs (609 and 1,492) for counts stratify patients into three different risk groups, with different OS. (B) Unsupervised, decision-tree-based machine learning and PFS identified patients with early or late progression within the first 10 months, based on the expression of PARP1 and TIAM1.

Applying the same COX-CIT algorithm for PFS, we were able to identify patients showing progression within the first 10 months (node 4), presenting low TIAM1 (P=0.004, cut-off 652 counts) and high PARP1 (P=0.017, cut-off: 1,819 counts) expression. Furthermore, the low TIAM1 and PARP1 expression group comprised another third of patients showing progression within the first year in up to 90% of cases. The remaining samples with high TIAM expression show better PFS times and rates, with a median survival of about one year, while one-fifth of patients show progression foremost after two years ().

Discussion

Chemoresistance, either intrinsic or acquired, substantially impairs the efficacy of chemotherapy, escalating mortality rates in cancer patients. Acquirement of resistance towards chemotherapeutics is a dynamic and multifactorial process influenced by cancer stem cell (CSC) enrichment (34). Several mechanisms leading to platinum resistance have been identified: reduced intracellular cisplatin accumulation due to alterations in transmembrane transport, activation of cell growth-promoting and DNA damage repair pathways, aberrant DNA methylation, enhanced epithelial-to-mesenchymal transition, and reduced endocytosis of cisplatin (35-37). However, as there are no established tools for predicting response to platinum therapy, there is an unmet need for predictive factors helping to identify patients at high risk for platinum resistance to select the adequate treatment for individual patients (37). Understanding the underlying mechanisms of cellular response to platinum may help to overcome this problem.

In the present study, we identified downregulation of MAPK-, IL-17-, relaxin- and TNF signaling pathways, and upregulation of AMPK-, mTOR-, Wnt- signaling and longevity regulation pathways in tumor samples of patients with MPM after platinum-based therapy.

Mitogen-activated protein kinase (MAPK)

MAPK signaling is important for cancer resistance to chemotherapy (38). Especially the p38-MAPK pathway seems to be involved in cisplatin-based chemotherapy response. In head and neck carcinoma, its low activity was in correlation to platinum resistance (39). In contrast, constitutive activation of the C-KIT/MAPK/MEK axis has been shown to result in a resistant phenotype of ovarian cancer cells treated with platin compounds (40).Furthermore, a broad miRNA screening study in vitro identified the miRNAs miR-20b*, let-7a, miR-524-3p, miRPlus-F1147, miR-300, miR-1299, miR-193b, miRPlus-F1064 as well as miR-642 as differentially expressed between sensitive and resistant ovarian cancer cell lines (41). Those miRNAs manipulate, among others, MAPK-, Wnt-, mTOR- as well as TGF-β-signaling (41). Of interest, also an RAF independent activation of MAPK cascade was proven to contribute to platin resistance in human cancer cells in vitro and in vivo (42). Taking all this into account, activation of the MAPK pathway is a prominent mechanism for platinum resistance in MPM; therefore, its downregulation in our samples after platinum-based chemotherapy corresponds most likely to the direct effect of the therapy.

Interleukin-17 (IL-17)

Since inflammation is present in most cancers, it is evident that different cytokines, like TNF-α, TGF-β, macrophage migratory inhibitory factor, and interleukins, are involved in promoting or inhibiting malignant growth, both in vitro and in vivo (43). Among those, the IL-17 family has a significant role in cancer immunity. Th17 (CD4+ T cells), innate tissue-resident cells (ITRC), and CD8+ cytotoxic T lymphocytes (CTLs) have been reported to participate in IL-17 production (44,45). IL-17 signaling in colorectal carcinoma promotes tumorigenesis by preventing the production of T cell-attracting CXCL9 and CXCL10 in tumor cells (46). Interestingly, asbestos exposure increases IL-17 production, especially in CD4+ surface CXCR3+ cells, indicating that the relation between Treg and Th17 fractions is important for asbestos immunogenicity and decrease in anti-tumor immunity (47). This is in accordance with the findings of Zebedeo et al., who found increased production of IL-17, IL-6, TGF-β, and TNF-α in mice following exposure to erionite and amphibole asbestos (48). Furthermore, IL17A mRNA expression and IL17A+ intratumoral cells in gastric cancer were predictive for better chemotherapy response (49). Moreover, in platinum-treated ovarian cancer, GSEA revealed enrichment in MAPK, ERBB, TNF, and IL‐17 signaling pathways in chemotherapy-sensitive patients (50). Besides the known pro-tumorigenic functions of IL-17, its increased presence is also a potential marker for chemotherapy sensitivity. Our finding of IL-17 pathway downregulation in MPM patients after platinum-based therapy is best explained as a platinum anti-tumorigenic effect.

Relaxin

Another pathway found to be downregulated in MPM samples after therapy is the relaxin-pathway. Relaxin is an anti-fibrotic agent. It hinders cytokine and growth-factor induced proliferation of fibroblasts and matrix production but also induces matrix degradation through increased activity of matrix‐degrading matrix metalloproteinases (MMP) (51,52). Its involvement in ECM remodeling, but also in angiogenesis, blood flow, anti-apoptosis, cell migration, and anti‐inflammation, helps in the proliferation, invasiveness, and metastasis of tumor cells and is highly expressed on them (51,53,54). Our finding of relaxin-pathway downregulation demonstrates the effect of platinum-based chemotherapy on this pro-tumorigenic factor.

TNF and NF-κB

Asbestos induces expression of TNF-α receptor I and secretion of TNF-α secretion in mesothelial cells (55). Furthermore, in mesothelial cells, TNF-α prevents apoptosis and cell death through NF-kappaB activation, reducing asbestos toxicity, allowing them to survive and go through malignant transformation (55,56). Activated NF-κB leading to upregulated PIK3CA expression via TNF-α has been identified as one of the key molecular features behind the action of cisplatin (34). Recent data indicates the PI3K/AKT pathway as decisive of cisplatin action in resistant cells (34). Platinum compounds have been shown to inhibit TNF-α stimulated NF-κB activation (57). This explains the downregulation of the TNF-pathway associated with platin-based treatment as a rather direct effect of therapeutic intervention.

AMPK and mTOR

AMP-activated protein kinase (AMPK) is a stress-response molecule involved in maintaining energy homeostasis in eukaryotic cells (58,59). Together with the mTOR downstream pathway, it regulates cellular metabolism, energy homeostasis, and cell growth (58). AMPK with LKB1 and P53 modulate mTOR and Akt signaling, resulting in cell growth inhibition and cell cycle arrest (60). It is known that the mTOR pathway is hyperactivated in many cancers (61-63). The PI3K/AKT/mTOR pathway has an important role in MPM tumorigenesis, and its blockade is therapeutically relevant, although it might induce rebound AKT activation leading to resistance (64). Interestingly, platinum-based chemotherapy did not prove effective against tumor cells with a hyperactivated mTOR pathway (65,66). Therefore dual inhibition of PI3K (for example, with cisplatinum), and mTOR might be a better option (67). Several studies indicate that AMPK activators inhibit the functions of myeloid-derived suppressor cells (MDSCs) and induce anti-tumor activities in many cancers (68-71). This is based on the fact that AMPK signaling inhibits immune signaling pathways, like JAK-STAT, NF-κB, C/EBPβ, CHOP, and HIF-1α, which are activating immunosuppressive MDSCs, therefore enabling adequate immune surveillance of tumor cells (68). In contrast, AMPK also has a pivotal role in chemoresistance (60).Evidence suggests that beyond classical resistance mechanisms, microenvironment stress also results in antagonism-independent alterations of the drug target, overactive DNA repair and survival pathways, enhanced expression of detoxification proteins, and drug efflux (72-74). It was shown that esophageal squamous carcinoma cells under nutrient stress, with activated AMPK pathway, kept proliferating and demonstrated chemoresistance to cisplatin (72). Both AMPK and mTOR pathways were upregulated in our samples after chemotherapy, most probably leading to the development of resistance to therapy.

Wnt

The interaction between the immune system and cancer is not fully understood but appears to be relevant for therapeutic decisions and response to platinum-based chemotherapy. While immune cells like CTLs and natural killer cells are well known for influencing outcome after chemotherapy in various cancers, also “non-immune” cells such as cancer cells or associated fibroblasts have an important role (75,76). They release molecules supporting angiogenesis, interact with the immune system, support tumor progression, induce degradation of the extracellular matrix, thereby enabling tissue invasion and metastasis (77-80). One potential inducer of TGF-β and VEGF are Wnt ligands, which can be produced, among others, by pulmonary fibroblasts. In non-small cell lung carcinoma, increased Wnt activity is associated with a higher risk for relapse (81-83). It has been demonstrated that Wnt ligands are potent modulators of the immune response and can suppress T cell activity (84-87) with some tumors producing them as an evasion strategy (88). Inhibition of Wnt signaling with cationic cyclometalated platinum(ii) complexes demonstrated efficient prevention of cancer cell proliferation and migration (89). Like the AMPK- and mTOR-, Wnt-pathway is also upregulated in our treated patient cohort, resulting in the development of chemotherapy-resistant tumor cells.

The interpretation of the results obtained in this study concerning different signaling pathways is challenging. Due to their multiple functions, it does not appear to be straightforward at first sight. Additionally, some effects can be assigned to cellular protection against the incoming oxidative cell stress, whereas some are related to mechanisms involved in overcoming cell longevity and progression. However, we have found downregulation in pathways important for carcinogenesis, which might be interpreted as the direct effect of the platinum-based therapy. Simultaneously, there is an upregulation in pathways involved in the resistance to this therapy, which eventually leads to disease progression.

There are several limitations to this study: it is a retrospective study, with a rather small cohort. Furthermore, we did not analyze paired samples from patients before and after therapy. However, our results are to be interpreted as a pilot study identifying pathways, which are of great importance for providing further hypothesis and direct upcoming research.

Conclusions

We could identify deregulated signaling pathways as a consequence of a directed cellular response to platinum-induced (oxidative) cell stress. In this context, mitogen signal associated pathways, such as MAPK, relaxin, TNF, or Wnt signaling, and energy metabolism regulation via AMPK and mTOR, as well as the mediation of tumor immunogenicity via cytokine release, play important roles. Our results are paving the ground for a better understanding of cellular response and escape mechanisms, carrying a high potential for improved clinical management of patients with MPM.

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