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KRAS mutation status is associated with specific pattern of genes expression in pancreatic adenocarcinoma Alessandro Bittoni1, Francesco Piva2, Matteo Santoni1, Kalliopi Andrikou1, Alessandro Conti1, Cristian Loretelli1, Alessandra Mandolesi3, Andrea Lanese1, Chiara Pellei1, Marina Scarpelli3, Giovanni Principato2 & Stefano Cascinu*,1 Abstract Aims: To evaluate potential differences at a molecular level between KRAS mutant tumors (MT) and KRAS wild-type (WT) pancreatic tumors and the biological and prognostic significance of different KRAS mutations. Materials & methods: Expression of a panel of 29 genes was analyzed in KRAS WT and MT tumors. Effects of KRAS mutation and gene expression levels were assessed on patients’ survival. Results: MUC6 (p = 0.009), HGF (p = 0.011), VEGFR-2 (p = 0.020) and VEGFB (p = 0.026) were significantly more expressed and SMAD4 was less suppressed (p = 0.003) in WT KRAS. Contrariwise, SHH (p = 0.012) and IHH (p = 0.031) were more expressed in MT KRAS patients. No OS difference was found between WT and MT KRAS tumors. Conclusion: KRAS mutation status seems to identify two different subtypes of pancreatic ductal adenocarcinoma with similar outcome but distinct molecular features and probably different therapeutic targets. Pancreatic ductal adenocarcinoma (PDAC) represents the fourth leading cause of cancer-related deaths in the USA with a 5-year survival rate of less than 4% [1] . The EUROCARE-5 study confirmed that PDAC represents one of solid tumors with the worst prognosis in Europe with a 5-year survival rate of 6.9 months for patients diagnosed between 2000 and 2007 [2] . Strong evidence exists that the accumulation of both germline and somatic gene mutations, involving KRAS, p16, p53 and SMAD4 [3,4] , results in a sequential progression from normal pancreas to various stages of premalignant lesions and finally to PDAC [3,5–7] . KRAS mutations, predominantly in codon 12 or 13, are reported, although not univocally, in 30% of premalignant lesion and in about 90% of pancreatic cancer tumor specimens [8–11] . Mutated KRAS (MT KRAS) results in constant activation of the RAS pathway by locking RAS into the active GTP-binding state [12] . Nevertheless, the efforts to develop drugs directly targeting mutant KRAS failed in the past and remains an ambitious challenge for researchers. This failure may be partially explained by the heterogeneity of accumulated mutations in PDAC that makes this disease a nonhomogenous entity. Moreover, cross-talks between KRAS and other cancer-relevant signaling pathways, such as TGF-β, Wnt, Notch and Hedgehog [13–16] , may contribute to explain this heterogeneity but also may provide molecular targets for novel therapeutic agents. Furthermore, several studies have reported variable rates of KRAS mutation in PDAC, questioning the assumption that KRAS mutations are ubiquitous events in pancreatic cancer progression [11] . Considering the scarcity of data available on KRAS wild-type (WT) pancreatic cancerogenesis and in order to evaluate potential differences at a molecular level between KRAS mutant tumors (MT) and KRAS WT PDAC, we compared the expression of a panel of 29 genes in KRAS WT and

Keywords

• KRAS • nucleotide variations • overall survival • pancreatic ductal adenocarcinoma • splicing

Department of Medical Oncology, AOU Ospedali Riuniti, Università Politecnica delle Marche, via Conca 71, 60126 Ancona, Italy Department of Specialistic Clinical & Odontostomatological Sciences, Polytechnic University of Marche, Ancona 60131, Italy 3 Department of Pathology, AOU Ospedali Riuniti, Università Politecnica delle Marche, via Conca 71, 60126 Ancona, Italy *Author for correspondence: Tel.: +39 071 596 4169; Fax: +39 071 596 4269; [email protected] 1 2

10.2217/FON.15.98 © 2015 Future Medicine Ltd

Future Oncol. (2015) 11(13), 1905–1917

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Research Article  Bittoni, Piva, Santoni et al. KRAS MT pancreatic tumors. In addition, we evaluated the biological and prognostic significance of different KRAS mutations by statistical and computational analyses. Material & methods ●●Patients characteristics

A total of 84 PDAC patients were retrospectively retrieved from our series. In particular, 42 KRAS WT samples were found and compared with other 42 KRAS MT samples. All the pancreatic cancer patients underwent surgical resection between 2006 and 2012 at AOU Ospedali Riuniti. All patients included in the study had resectable pancreatic cancer, without distant metastases. All patients provided written consent to have their samples and linked clinical data banked for clinical and basic research purposes. Our institutional ethical committee approved the consent process. For this study, all samples and data were de-identified and, therefore, did not require any additional Institutional Review Board approval. ●●Samples processing & quantitative PCR

analysis

Tumor tissues were collected from pancreatic cancer patients during surgery. Gene expression profile analysis was performed by laboratory personnel blinded to patients’ status. Multiple sections of formalin-fixed, paraffin-embedded tissue blocks (25–30 mg of primary tumor, manual microdissected tissue) were collected; paraffin wax was removed and total RNA was extracted by the RT2 FFPE RNA Extraction Kit (SABiosciences Corporation, MD, USA), following the manufacturer’s instructions. RNA samples were quantified and quality tested for the presence of protein and/or organic solvent contaminants by a spectrophotometric assay. In total, 500 ng from each sample were reverse transcribed to cDNA and preamplified using the RT2 FFPE PreAMP cDNA Synthesis Kit and the primer mix specific for the customized Stem Cell RT2Profiler PCR Array (SABiosciences Corporation). Quantitative real-time PCR analysis was performed on a 7300 Real-Time PCR System (Applied Biosystems, Inc., CA, USA) by a SYBRH Green method. A complete list of the genes tested can be found in Table 1. Information detailing the expression profiling primer sets is reported in Table 2. The panel of 29 genes analyzed was selected on the basis of evidences from literature supporting the role

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of SMAD4, Hedgehog pathway, Wnt pathway, Notch pathway, BRCA, VEGF, MET and SPARC in pancreatic cancer development and progression [17] . Other three genes, namely B2M, GUSB and HPRT1, three housekeeping genes, were analyzed and used as endogenous controls in the qPCR analysis. ●●DNA sequencing for KRAS

Formalin-fixed and paraffin-included tumor samples were analyzed for KRAS exon 2 mutations, located within the codon 12 and 13. Only mutations in codon 12 and 13 were analyzed, considering that more than 98% of KRAS mutations in PDAC are located in these codons [18] . DNA was extracted from five paraffin sections of 10-μm thickness containing at least 50% tumor cell, using the QIAamp DNA Mini Kit (Qiagen). After the purification using QIAquick PCR Purification kit, the PCR products (20 ng) were added to the solution sequencing reaction composed of 2 μl BigDye® Terminator V1.1 Ready Reaction Mix (Applied Biosystems, Inc.), 2 μl Sequencing Buffer, 5 μM primer forward reverse, 3 μl distilled water). The sequencing reaction solution was sequenced on ABI Prism 3100 DNA sequencer (Applied Biosystems, Inc.). Primers used for KRAS were as follows: codons 12 and 13, forward: 5′-AAG GCC TGC TGA AAA TGA CTG-3′ and reverse: 5′-CAA AGAATG GTC CTG CAC CAG-3′. The analysis of the produced sequences by each sample was obtained by sequencing Analisis Software 3.7. ●●Data processing & statistical analysis

Relative gene expression was quantified using the comparative ΔCt method. We used the tool ‘PCR Array Data Analysis Web Portal’ [19] on the manufacturer’s website to perform data quality tests, calculations on the qPCR data and data normalization. In particular, all threshold cycles values greater than 35 or not detected, were considered as negative calls. We retained samples with negative genomic DNA control, definite reverse transcription control and positive PCR control values, according to manufacturer’s indications. The clinical and histopathological characteristics of enrolled patients were analyzed in the present study. Data were retrospectively collected from medical chart reviews and electronic records. Overall survival (OS) was defined as the time from the diagnosis to death. Survival analysis was conducted via Kaplan–Meier productlimit method and the Mantel–Haenszel log-rank

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KRAS mutation status & patterns of genes expression in pancreatic adenocarcinoma 

Research Article

Table 1. Genes analyzed and main gene function. Gene ID

Gene name

Biological process

ALCAM B2M BMP4 BRCA1 BRCA2 CD24 CD44 CDKN2A DHH FLT1 GUSB HGF HPRT1 IHH LGR5 MET MUC6 NOTCH1 OCT3/4 PDGFRB PGF PROM1 PTCH1 PTCH2 SHH SMAD4 SMO SPARC VEGFA VEGFB VEGFR-2 WNT1

CD166 antigen Beta-2-microglobulin Bone morphogenetic protein 4 Breast cancer type 1 susceptibility protein Breast cancer type 2 susceptibility protein Signal transducer CD24 CD44 antigen Cyclin-dependent kinase inhibitor 2A, isoform 4 Desert hedgehog protein Vascular endothelial growth factor receptor 1 Beta-glucuronidase Hepatocyte growth factor Hypoxanthine-guanine phosphoribosyltransferase Indian hedgehog protein Leucine-rich repeat-containing G-protein coupled receptor 5 Hepatocyte growth factor receptor Mucin-6 Neurogenic locus notch homolog protein 1 POU domain, class 5, transcription factor 1 Platelet-derived growth factor receptor beta Placenta growth factor Prominin-1 Protein patched homolog 1 Protein patched homolog 2 Sonic hedgehog protein Mothers against decapentaplegic homolog 4 Smoothened homolog Secreted protein acidic and rich in cysteine Vascular endothelial growth factor A Vascular endothelial growth factor B Vascular endothelial growth factor receptor 2 Proto-oncogene Wnt-1

Cell adhesion House-keeping gene for normalization Cell differentiation Cell cycle Cell cycle Cell proliferation Cell adhesion Cell cycle regulation and apoptosis Cell differentiation Cell differentiation and angiogenesis House-keeping gene for normalization Cell proliferation, chemotaxis and apoptosis House-keeping gene for normalization Cell differentiation Cell development, stem cell marker Cell proliferation, scattering, morphogenesis and survival Maintenance of epithelium Cell differentiation and angiogenesis Cell differentiation Cell proliferation, chemotaxis and migration Cell differentiation and angiogenesis Cell differentiation Cell differentiation Cell differentiation Cell differentiation Cell differentiation and signal transduction Cell differentiation Cell growth Cell differentiation and angiogenesis Cell differentiation and angiogenesis Cell differentiation and angiogenesis Cell differentiation

B2M, GUSB and HPRT1 are housekeeping genes used as endogenous controls in the qPCR analysis.

test was employed to compare survival among groups. The assumption of proportionality of hazards was checked with graphic analysis of scaled Schoenfeld residuals. Variables not fitting at univariate analysis were excluded from the multivariate model. No multicollinearity of the grouped co-variates was checked. Significance level in the univariate model for inclusion in the multivariate final model was more liberally set at a 0.2 level, according to Hosmer et al. [20,21] . The likelihood ratio test was conducted to evaluate the improvement in prediction performance gained by backward elimination of variables from the prognostic model [22] . All other significance levels were set at a 0.05 value and all p-values were two-sided. Statistical

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analysis was conducted with the ‘R’ statistical software version 2.15.2. ●●Bioinformatic analyses

The search for alternative transcripts of WT or MT KRAS was performed by analyzing the mRNA and EST sequences collected in UCSC Genome Browser [23] and in Ensembl [24] . The splice sites predictions have been carried out by BDGP [25] that adopts an artificial neural network algorithm and it returns a score ranging from 0 to 1, higher the score stronger is the splice site. The pattern of splicing proteins bound to KRAS pre-mRNA WT and MT transcripts has been executed by SpliceAid2 tool [26] . This resource uses only experimental binding sites information to minimize the false positive and negative

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Research Article  Bittoni, Piva, Santoni et al. Table 2. Assay ID/sequences for gene-expression analysis. Gene ID

Gene details 

Assay ID/sequence

GUSB HPRT1 B2M BRCA1 BRCA2 NOTCH1 SPARC SHH DHH IHH SMO PTCH1 PTCH2 ALCAM PROM1 OCT3/4 CD44 LGR5 BMP4 WNT1 KDR FLT1 PDGFRB VEGFA VEGFB PGF CDKN2A SMAD4 MET HGF MUC6 CD24

House-keeping gene for normalization House-keeping gene for normalization House-keeping gene for normalization                                                        

Hs99999908_m1 Hs01003267_m1 Hs00984230_m1 Hs01556191_m1 Hs01037414_m1 Hs01062014_m1 Hs00234160_m1 Hs00179843_m1 Hs00368306_m1 Hs01081800_m1 Hs01090242_m1 Hs00970979_m1 Hs00184804_m1 Hs00233455_m1 Hs01009250_m1 Hs00999632_g1 Hs01081474_m1 Hs00969422_m1 Hs00370078_m1 Hs00180529_m1 Hs00911700_m1 Hs00176573_m1 Hs01019589_m1 Hs00900055_m1 Hs00173634_m1 Hs01119262_m1 Hs99999189_m1 Hs00929647_m1 Hs01565572_m1 Hs00900062_m1 Hs00401231_m1 Forward primer sequence: GCTCCTACCCACGCAGATTTAT Reverse primer sequence: ACTCTGGGAGGAGTTACTTGAAGTTC Probe sequence: CCAGTGAAACAACAAC

   

predictions [27] . The search for RNA motifs regulating nuclear export of KRAS transcript has been ­performed by ExportAid tool [28] . We used more reliable tools, in terms of false-negative and false-positive error rate, to predict the severity of amino acidic substitutions [29] . In particular, we have used SIFT [30] , PolyPhen-2 [31] and SNPs3D [32] . Results ●●Patient characteristics

A total of 42 resected WT KRAS PDAC patients were identified for inclusion in the study. Median age was 67 years (range: 47–81 years). In total,

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27 patients were male (64%). Most patients presented with T3 stage disease (34 patients, 81%) at pathological examination, while four patients had T2 stage (9%), two patients had T1 (5%) and the remaining two patients were classified as T4 (5%). A total of 32 patients had node positive disease (76%). Nine patients had poorly differentiated disease (21%). In total, 42 resected MT KRAS PDAC patients with comparable clinico-pathological characteristic were used as control group (Table 3) . In this group, the median age was 68 years (range: 53–83 years), 35 patients presented with T3 stage disease (81%) at pathological examination

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KRAS mutation status & patterns of genes expression in pancreatic adenocarcinoma  and most patients had node-positive disease (31 patients; 74%). The 29 genes panel analyzed in both groups are summarized in Table 1.

Research Article

SMAD4 and IHH. Data on gene correlation are shown in Figure 2. ●●Association between OS & different KRAS

●●Analysis of differential gene expression in

mutations

MT & WT PDACs

Clinical data were collected for a total of 77 patients, who were suitable for survival analysis. Median survival time was 11.1 months (95% CI: 10.1–17.3 months) in the overall population. No significant differences were found at Kaplan–Meier analysis stratifying patients for age at diagnosis (log-rank p = 0.1), gender (log-rank p = 0.44). Data on KRAS Mutations are reported in Table 4. No significant differences were found between MT and WT patients (log-rank p = 0.94). Since we have not found OS differences between the two groups, we have analyzed if the nucleotide variations are not able to alter any molecular process involving KRAS transcript and protein. As our mutations lie in a coding exon they should not able to affect transcription instead they could change transcript maturation and protein folding.

The analysis of gene expression data showed that MUC6 (p = 0.009), VEGFR-2 (p = 0.020), VEGFB (p = 0.026) and HGF (p = 0.011) were significantly more expressed in WT KRAS PDACs, while SHH (p = 0.012) and IHH (p = 0.031) were more expressed into MT KRAS tumors. Furthermore, SMAD4 was found to be less suppressed in WT KRAS PDACs (p = 0.003). All the others clinical characteristics analyzed resulted well balanced between the two groups. A comparison of the differential gene expressions in WT and MT KRAS PDACs is shown in Figure 1. Furthermore, we investigated the correlation between the expression of each of the seven genes that were differently expressed in MT and WT tumors and the expression of the other genes analyzed in this study. The expression of VEGFR-2 was significantly correlated with the expression of SPARC (rho = 0.613; p < 0.001). As regard to VEGFB, its expression was significantly associated with FLT1 (rho = 0.744; p < 0.001) and PDGFRB (rho = 0.680; p < 0.001). In addition, a significant correlation was found between SHH and NOTCH1 (rho = 0.613; p < 0.001). No significant correlation was found for MUC6,

●●Alternative splicing predictions

It is known that many nucleotide variations, even synonymous, can alter the splicing pattern of a pre-mRNA transcript giving rise to complete or partial exon skipping or intron inclusion [33] . In particular, these mutations act by: creating or destroying functional splice sites; activating

Table 3. Patient characteristics. Clinicopathological features 

KRAS wild-type, n (%)

KRAS mutated, n (%)

Patients Gender: – Male – Female Age: – Median (range), years TNM status: – T1 – T2 – T3 – T4 – N0 – N1 Grading: – G1 – G2 – G3 – Gx

42 (100)   27 (64) 15 (36)   67 (47–81)   2 (5) 4 (9) 34 (81) 2 (5) 10 (24) 32 (76)   3 (8) 18 (43) 9 (21) 12 (29)

42 (100)   22 (52) 20 (48)   68 (53–83)   0 (0) 3 (8) 35 (83) 4 (9) 11 (26) 31 (74)   5 (12) 16 (38) 10 (24) 11 (26)

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Research Article  Bittoni, Piva, Santoni et al. More expressed in MT KRAS PDACs 0.100

More expressed in WT KRAS PDACs

1.5

50

0.3

40

0.050

p = 0.009

20

0.5

0.025

0.2

30

p = 0.003

MUC6

p = 0.012

SMAD4

SHH

1.0

p = 0.011

HGF

0.075

0.1

10

0.000 KRAS

WT

0.0

0

0.0 MT

MT

KRAS

WT

MT

KRAS

0.6

0.4

WT

MT

KRAS

WT

2.5

0.4 p = 0.020 0.2

0.1

VEGFB-2

IHH

p = 0.031

0.2

VEGFR-2

2.0 0.3

1.5

p = 0.026

1.0 0.5

0.0

0.0 MT

KRAS

WT

0.0 MT

KRAS

WT

MT

KRAS

WT

Figure 1. Comparison between differential gene expressions in patients with wild-type and mutated KRAS pancreatic tumors. MT: Mutated; PDAC: Pancreatic ductal adenocarcinoma; WT: Wild-type.

ectopic splice sites; and creating or destroying RNA motifs bound from splicing proteins that process the transcript [29] . We have analyzed, by bioinformatics tools, if our KRAS mutations could modify the exon 2 splicing. According to public sequence databases exon 2 is constitutively present in all four alternative splicing variants both from normal and tumor samples. The 3′ splice site of exon 2 is rather weak since its predicted score is 0.69 and therefore we would expect that the exon 2 was an alternative exon. The predictions of SpliceAid2 tool show that a lot of binding sites for the splicing regulatory protein HuR, HuB, hnRNP C1, hnRNP C2 and KSRP lie in the last 30 bases of the intron upstream exon 2 (Figure 3) . These factors belong to the class of the exonic splicing silencers and act to mark an intron or reinforce a weak splice site. Probably this is the mechanism that compensates for the weak 3′ splice site and makes the exon 2 present in all transcript isoforms. Notably, we analyzed only the 3′ splice site because it is the nearest to our mutations. We also did not analyze the 5′ splice site because our KRAS mutations are far away from it so they should not be able to affect it.

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We tried to explain why nucleotide mutations of the codon 12 did not cause partial or complete exon 2 skipping as showed by UCSC. The analysis by SpliceAid2 tool predicts that all our nucleotide variations destroy the RNA motif recognized from hnRNP P splicing protein. Since it is an exonic silencer, its removal from an alternative exon increases the exon inclusion and has no effect on constitutive exons. ●●Nuclear export predictions

Furthermore, we assessed if the nucleotide variations could affect the nuclear export of the mature RNA transcript. In fact, not only the deposition of the Exon Junction Complex by splicing machinery regulates the nucleo-cytoplasmic export but also other RNA elements as, for example, the constitutive transport element, the cytoplasmic accumulation region, the eIF4E-sensitive element, the post-transcriptional regulatory element, the signal sequence coding region and others. However, this analysis performed by ExportAid [28] has shown that mutations do not involve any known nuclear export signal.

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KRAS mutation status & patterns of genes expression in pancreatic adenocarcinoma  ●●Severity of the amino acidic substitutions

Our mutations cause the substitution of glycine at codon 12 but different substitutions could give a different protein folding changes and so different degree of functional alterations. Notoriously,

Research Article

glycine is an aminoacid difficult to substitute due to its conformational flexibility and its ability to bind phosphates. However, we investigated if the substitution with other small aminoacids could result in less severity. To predict the severity of

A

B 0.5

1.2

MT KRAS

WT KRAS 0.4

0.8

FLT1

NOTCH1

0.3

0.2

0.4 0.1

0.0

0.0 0.00

0.02

0.04

0.06

0.25

0.50

SHH

0.75 VEGFB

1.00

1.25

D

C

0.25

WT KRAS

7.5

WT KRAS

0.20

VEGFR-2

PDGFRB

0.15 5.0

2.5

0.10

0.05

0.00 0.25

0.50

0.75 VEGFB

1.00

1.25

10

20 SPARC

30

Figure 2. Scatterplot of relationships between different gene expressions in wild-type and mutated KRAS pancreatic tumors. MT: Mutated; WT: Wild-type.

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Research Article  Bittoni, Piva, Santoni et al. Table 4. KRAS mutations type. KRAS mutations

Patients, n (%)

GGT→GAT GGT→GTT GGT→CGT GGT→AGT GGT→TGT GGT→GAC

15 (36) 17 (40) 6 (14) 1 (2.5) 1 (2.5) 2 (5)

aminoacidic substitutions we used some tools assessed to be more reliable in terms of false-negative and false-positive error rate [34] . In particular, we have used SIFT [30] , PolyPhen-2 [31] and SNPs3D  [32] . The detailed results are shown in Table 5 and we observed an agreement of high severity only for G→V and a disagreement for the others, in particular G→D. We could assume that G→R and G→S could be a bit less severe than the others. In addition, we have tested if our severity prediction correlated with patient’s OS. No significant difference in terms of OS was found when comparing the different types of KRAS mutations to each other (Arg vs Val + Asp + Cys logrank p = 0.37, Asp vs Val + Arg + Cys p = 0.33, Val vs Asp + Arg + Cys log-rank p = 0.53, Asp vs Val log-rank p = 0.39, Arg vs Asp log-rank p = 0.22, Arg vs Val log-rank p = 0.66, Asp vs Val log-rank p = 0.67) or versus WT (Arg vs WT log-rank p = 0.44, Asp vs WT log-rank p = 0.36, Val vs WT log-rank p = 0.70). Discussion KR AS activating mutations are considered among the most common genetic abnormalities

in pancreatic cancer and also one of the earliest events observed in tumor progression, although their influence on the outcome of patients still remains unclear. A recent review has reported variable rates of KRAS mutation, ranging from 33 to 88%, questioning the assumption that KRAS mutations are ubiquitous events in pancreatic cancer progression  [35] . This seems to be confirmed by a retrospective analysis on 136 advanced pancreatic cancer patients receiving a gemcitabine-based chemotherapy, where KRAS mutations at codons 12, 13 and 61, were detected in 71 pancreatic tumors only, with a mutation rate (52.2%) [11] . Considering the scarcity of data available on KRAS wild-type pancreatic carcinogenesis, we investigated differences at a molecular level according to KRAS mutation status. Interestingly, we showed significant differences in gene expression between KRAS WT and KRAS MT. SHH and IHH, two elements of the Hedgehog (HH) pathway, were significantly more expressed in KRAS MT compared with KRAS WT. These results are in accordance with preclinical data showing how aberrantly activated HH signaling cooperates with KRAS mutations to promote formation of PanIN lesions [36] . Our findings confirm that the HH pathway may represent a driver of progression in the subgroup of patients with pancreatic cancers harboring a KRAS mutation. This role in pancreatic cancer development makes HH pathway a potential target for treatment. In a recent Phase Ib/II trial [37] addition of vismodegib, an HH pathway inhibitor, to gemcitabine demonstrated no benefit in terms of response rate, PFS or OS in an unselected

SpliceAid 2 predictions ESE

ESS

TIAL1 TIA-1

Nova-1 SRp30c

TIAL1

hnRNP C1 HuR KSRP hnRNP C1 hnRNP C1 hnRNP C2 KSRP HuR HuB Sam68

HuB

Intron C A C A U U U U CA

10bp

MBNL1

hnRNP P (TLS) hnRNP A1

Fox-1 Fox-2

Codon 12 5’UTR

U U A U UU U U A U U A U A A GG C CU

20bp

30bp

Coding exon

GCUG A A A AU G A CUG A A U AUA

40bp

50bp

A A C U UG U GG U A GU U GG A G CU

60bp

70bp

GG U G G C G U A G G CA A G

80bp

Figure 3. Pattern of splicing proteins bound to the beginning of KRAS exon 2 according to SpliceAid 2 tool.

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KRAS mutation status & patterns of genes expression in pancreatic adenocarcinoma 

Research Article

Table 5. Predicted severity of amino acidic substitutions. Severity of the amino acidic substitutions

SIFT

PolyPhen-2

SNPs3D

G→C G→D G→R G→S G→V

Severe Severe Severe Severe Severe

Probably damaging (score: 1) Possible damaging (score: 0.5) Possible damaging (score: 0.7) Possible damaging (score: 0.7) Probably damaging (score: 1)

Score: -0.75 Score: -2.12 Score: -0.06 Score: 0.28 Score: -2.47

Higher is PolyPhen-2 score higher is substitution severity. Lower is SNPs3D score higher is substitution severity.

pancreatic cancer patients population. Indeed, only patients with KRAS MT tumors should be candidate to receive HH inhibitors since this pathway seems to be active only in these tumors. In KRAS MT patients we also found a significant correlation between Notch1 and SHH expressions. Both the Notch and HH signaling pathways have been shown to promote proliferation of progenitor cells and are involved in mediating cross-talk between the malignant pancreatic epithelium and its associated stroma. However, the hierarchical relationship between these two pathways is still unclear. Indeed, upregulation of the Notch pathway might occur downstream from the HH pathway during PDAC progression, although it is not possible to exclude that Notch signaling might also activate the HH pathway, as reported in other contexts [38] . In KRAS WT tumors we found higher expression levels of VEGFR-2, VEGFB, HGF and MUC6. This observation gives some insights on the carcinogenesis of KRAS WT pancreatic cancer. VEGF has a strong mitogenic effect on pancreatic cancer cells and this effect is mainly mediated by VEGFR-2. In a study on 24 pancreatic cancer tumors [39] , VEGFR-2 mRNA was found to be overexpressed in 63% of samples and it was correlated with poor tumor differentiation. Patients with overexpression of VEGFR-2 had a significantly shorter overall survival. Our results confirm that the VEGF signaling pathway may represent a driver of tumor progression in the subgroup of KRAS WT pancreatic cancer. Interestingly, a monoclonal antibody that blocks VEGFR-2, ramucirumab, has been recently shown to be effective in the treatment of advanced gastric cancer [40] . Our data suggest that ramucirumab may represent a valuable option and should be evaluated in the treatment of KRAS WT pancreatic cancer. Another interesting finding of our study was the observation that SMAD4 gene was less suppressed in KRAS WT tumors. SMAD4 gene is a tumor suppressor gene frequently inactivated in

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pancreatic cancers [41] . SMAD4 protein is a critical mediator of the TGF-β canonical signaling pathway and loss of SMAD4 in pancreatic cancer cells allows an escape from TGF-β-induced growth inhibition. Our results suggest that loss of SMAD4 may represent a frequent event in KRAS MT pancreatic cancers but not in KRAS WT tumor. However, it is important to highlight that we did not perform SMAD4 mutation analysis but we only analyzed SMAD4 mRNA expression. Although KRAS mutational status is associated with different molecular pathways, it is not associated with a different survival in our analysis. This seems to be in accordance with data from literature. Indeed, the prognostic role of KRAS mutation in pancreatic cancer is still controversial. During the past 5 years several analyses on the role of KRAS mutation as a biomarker in this malignancy have been published with contradictory results. Some studies suggest a negative prognostic value in terms of survival for KRAS mutations [36,42–44] . In one of the largest published case-series on resectable pancreatic cancer patients, a statistically significant better survival was observed for KRAS WT patients both in univariate (26.4 vs 14.3 months; p = 0.001) and in multivariate analysis (odds ratio: 1.63; p = 0.011) [45] . However, other studies did not confirm it [46,47] even if, other authors observed that the presence of KRAS mutation alone did not correlate with survival, while survival significantly differenced according to the type of KRAS mutation [48] . In our study, the lack of clinical information regarding the adjuvant treatments received by patients may also have affected survival and influenced the comparison in OS between WT and MT patients. Notably, median overall survival reported in our study (about 11 months) is quite short if compared with OS data from clinical trials on resected PDAC patients. However, it should be pointed out that most of patients in our study presented with high stage disease, mainly T3 N1 while a few patients had also T4 disease. Moreover our series included also elderly patients (up to 83 years old) with higher risk of perioperative mortality.

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Research Article  Bittoni, Piva, Santoni et al. The bioinformatics analysis can contribute to explain the lack of difference in survival according to KRAS status observed in our study. By exploiting if the nucleotide variations could alter any molecular process involving KRAS transcript and protein and if the nucleotide variations could affect the nuclear export of the mature RNA transcripts, we did not find any significant difference among the aminoacidic substitutions due to different KRAS mutations. However, in our study we did not look to the expression of KRAS effectors to confirm the activation of the pathway in KR AS MT versus KRAS WT samples, mainly for limited resources. This may represent a limit of our study and matter for future research. Moreover, the analysis of miRNAs suggests their possible role in making not significant some of the differences between gene expression levels in WT and MT KRAS patients. Indeed, it is known that miRNA can silence mRNAs so reducing the related protein level. The lack of influence of SMAD4 mean expression levels on OS could be due to the presence of miR-224 that has been shown to be upregulated in pancreatic ductal adenocarcinomas [49] . SMAD4 is the target gene of miR-224 [50] and its suppression contributes to promote cell proliferation, migration and invasion equally in both WT and MT. However the expected absence of SMAD4 is only partly confirmed from Human Protein Atlas, indeed, it reports that SMAD4 protein is weakly or moderately present in half of its collected pancreatic cancer samples. MUC6 and SHH genes are validated targets of only hsa-miR-335-5p  [51] but miR-335 level is low in pancreatic carcinoma samples [52] so these genes should not be suppressed. Analogously, VEGFR-2 is a validated target of miR-19b-1-5p [53], miR-200b-3p [54] , miR-16-5p [55] , miR-335-5p [56] , miR-106b-5p [57] . However, in pancreatic cancer, miR-200b seems to be not expressed [58] , miR16 is significantly downregulated [59] , miR-335 level is low and there are no data for miR-19b and miR-106b. In addition, no miRNA is known to bind VEGFB and IHH genes. This allows us to believe that MUC6, SHH, VEGFR-2, VEGFB and IHH are not silenced, so the differences in terms of expression level that we found in the two groups could give the same ratio at protein level. An heterogeneous scenario emerges for SMAD4, MUC6 and SHH when comparing expected protein expression versus protein expression data found in Human Protein Atlas. In fact, in some samples they are not present, in others weakly

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and in other moderately present. Instead, IHH is moderately present in all samples and VEGFR-2 and VEGFB are always absent. To this end, it is important to notice that one of the limits of our study is that we analyzed only mRNA expression and not protein expression. Nevertheless, the lack of difference in terms of prognosis between KRAS MT and KRAS WT patients could also be related to downstream mediators of the KRAS pathway, such as MEK proteins, but also to the role of different pathways, such as JAK–STAT or IGF-1 pathway, which have been shown to be involved in PDAC progression. Conclusion Our findings give some insights about the molecular heterogeneity of pancreatic cancer with possible consequences on the design of future clinical trials. KRAS mutations are common but not ubiquitous events in pancreatic carcinogenesis. According to our analysis, KRAS mutational status may identify two different subtypes of pancreatic cancer with distinct molecular features and therefore with different therapeutic targets. The high mRNA expression of SHH and IHH demonstrated in KRAS MT pancreatic cancer suggests that they may represent a subset of tumors where the use of HH inhibitors could be effective. On the other hand, the high mRNA expression of genes related to angiogenesis, such as VEGFR-2 and VEGFB, in KRAS WT tumors, suggests the possible activity of antiangiogenic drugs such as ramucirumab in this subtype of pancreatic cancers. Notably, KRAS mutation status should not be considered as a predictive marker for the use of HH inhibitors or antiVEGFR-2 treatments in PDAC. Nevertheless, the significant differences in the expression of HH pathway-related genes and VEGF-related genes observed in our study, suggest that these pathways may play different roles in PDAC progression according to KRAS mutation status. Therefore we may speculate that inhibition of HH may be more effective in KRAS MT PDAC while inhibition of VEGFR in KRAS WT. These observations, if confirmed by further validation studies, should be taken into account in the design and patients selection of future clinical trials evaluating target therapies. Financial & competing interests disclosure The authors have no relevant affiliations or financial involvement with any organization or entity with a

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KRAS mutation status & patterns of genes expression in pancreatic adenocarcinoma  financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. No writing assistance was utilized in the production of this manuscript.

Research Article

Ethical conduct of research The authors state that they have obtained appropriate institutional review board approval or have followed the principles outlined in the Declaration of Helsinki for all human or animal experimental investigations. In addition, for investigations involving human subjects, informed consent has been obtained from the participants involved.

Executive summary ●●

KRAS-activating mutations play an important role in the progression of pancreatic ductal adenocarcinoma (PDAC), but recent studies reported variable rates of KRAS mutation, questioning the assumption that KRAS mutations are ubiquitous events in PDAC progression.

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In 42 KRAS WT PDAC patients who underwent surgical resection, we analyzed the expression of a panel of 29 genes and we compared them with those observed in 42 KRAS MT tumors.

●●

In WT KRAS tumors, MUC6 (p = 0.009), VEGFR-2 (p = 0.020) and VEGFB (p = 0.026) were significantly more expressed and

SMAD4 was less suppressed (p = 0.003). On the contrary, SHH (p = 0.012) and IHH (p = 0.031) were more expressed in MT KRAS patients. ●●

By investigating the severity of aminoacidic substitutions, G-R and G-S KRAS mutations appeared less severe than the others.

●●

No significant difference in terms of OS was found between WT and MT KRAS tumors and among patients with different KRAS mutations.

●●

Thus, KRAS mutational status may identify two different subtypes of pancreatic cancer with distinct molecular features and therefore with different therapeutic targets.

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