EP4634666A1 - Method to isolate proteins from mucinous carcinoma cancer cells and uses thereof - Google Patents

Abstract

The present invention refers to a method to isolate proteins from mucinous carcinoma cancer cells. The present invention also refers to the use of the proteins thus obtained as biomarkers or therapeutic targets.

Description

METHOD TO ISOLATE PROTEINS FROM MUCINOUS CARCINOMA CANCER

CELLS AND USES THEREOF

FIELD OF THE INVENTION

The present invention refers to the medical field. Particularly, the present invention refers to a method to isolate proteins from mucinous carcinoma cancer cells. The present invention also refers to the use of the proteins thus obtained as biomarkers or therapeutic targets.

STATE OF THE ART

Pseudomyxoma Peritonei (PMP) is a rare malignant disease defined by the progressive, abundant, multifocal accumulation of mucinous tumour tissue in the peritoneal cavity, essentially without extraperitoneal growth and distant metastasis development. It has been historically considered as a terminal condition, in which debulking surgery or palliative treatments were its only options. In the last years, reference groups have published results showing survival benefits treating the PMP with cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC). However, the recurrence is common despite this therapeutic effort, with subsequent progression and death.

The PMP tumour’s clinical diagnosis can be considered a difficult challenge; the tumour can remain silent, simulating acute appendicitis or disguised by an abdominal disease. Besides, there are no specific tumour markers other than biopsy. In this context, the most successful protocol has been established by PSOGI (The Peritoneal Surface Oncology Group International), which includes two subtypes of PMP: i) low-grade mucinous carcinoma and ii) high-grade mucinous carcinoma and two extreme subtypes with the presence of acellular mucin or with signet ring cells, with different survival outcomes. Moreover, a new subtype inside the HG-PMP depending on Ki67 % proliferation index has been described.

The fast development of the personalized therapy for oncologic patients has highlighted the importance of describing the molecular possible subtypes within the same histologic tumour. In this way, the search for personalized therapeutic solutions has made the tumour molecular classification as a critical step in the clinical decisions. Although minimal, several molecular studies in PMP has been published. Some of the genes and proteins have been proposed as biomarkers; however, their usefulness is still limited, and none of them have been validated as a possible therapeutic target. Furthermore, it is noteworthy that there are no studies describing the protein profile of this kind of tumour, since the high concentration of glycoproteins together with the inter-mucin physical and chemical characteristics interfere with the current protein’s isolation protocols.

In general, mucins are a family of heavily glycosylated proteins, also known as glycoproteins, that are expressed in specialized epithelial cells of mucosal surfaces including the respiratory, gastrointestinal, and urogenital tracts. Mucins are classified into membrane-associated and secreted mucin subtypes. Membrane-associated mucins are sensors of the extracellular media, and they are able to mediate the intracellular signal transduction when the mucous layer is disrupted. Secreted mucins constitute a physical barrier for epithelial cells against microorganisms and insoluble materials and maintain the local molecular environment. Under normal conditions, there is a well-established metabolic turnover of mucin production and degradation. However, in PMP, mucin is secreted and gradually deposited in the peritoneal cavity where it is difficult to breakdown and drain away. Mucin accumulation over time is responsible for a large portion of the morbidity in PMP and is also able to surround and protect tumour cells, allowing them to disseminate through the peritoneal cavity. Importantly, several studies have reported the presence of non-mucin proteins such as digestive enzymes, dietary proteins, immunoglobulins, albumins, and keratin, among others, which directly interact with mucin proteins through strong non-covalent interactions, enhancing the viscosity of PMP secretions. Additionally, mucin from PMP patients has also been classified into soft, semi-hard and hard mucin samples based on physical and chemical properties as well as the visual appearance.

So, in summary, there is an unmet medical need of finding effective methods to isolate proteins from mucinous carcinoma cancer cells and also of developing efficient diagnosis, prognosis or therapeutical strategies. Indeed, the reference [Alvaro Arjona-Sanchez, et al, 2021. A Proposal for Modification of the PSOGI Classification According to the Ki-67 Proliferation Index in Pseudomyxoma. Ann Surg Oncol, https://doi.org/10.1245/sl0434-021- 10372-9 indicates that molecular profiling of PMP cases might help to better categorize patients and predict treatment responses. The present invention is focused on solving these problems and a protocol to isolate massive proteins from soft and hard mucin samples is herein described. Moreover, the present invention also shows biomarkers and therapeutic targets which could be used in the context of mucinous carcinomas diagnosis or treatment. Description of the invention

Brief description of the invention

The present invention refers to a method to isolate proteins from mucinous carcinoma cancer cells. The present invention also refers to the use of the proteins thus obtained as biomarkers or therapeutic targets.

Interestingly, the inventors of the present invention have designed a method to break the mucin barrier and isolate proteins from soft and hard mucins. This approach is based on glycoproteins, immunoglobins and albumins depletion by affinity liquid chromatography, and it makes the mucin able for free-label mass spectrometry. The method comprises the following steps:

1. Mucin samples are dissected in small pieces and homogenize using ultrasounds: Soft and hard mucin (low and high grade) samples, as well as control samples, were dissected into small fragments and homogenized using ultrasounds.

2. The homogenate is centrifugated and the supernatant is collected and filtered: The samples were centrifuged to remove any leftover and solid fragments were filtrated to remove any cellular components or microorganism, as these would interfere with the subsequent chromatography procedures.

3. The homogenate is used to carry out a liquid chromatography (LC) using two different columns in order to eliminate glycoproteins, IgG and albumin from the sample: Filtrated samples were processed by two consecutive affinity chromatography steps. First, samples were run through the chromatographer using a column to specifically capture glycoproteins, polysaccharides, and glycolipids. The flow-through, corresponding to the maximum absorbance peaks in the two- dimensional plot chromatogram, were collected and precipitated to be used in the next step. Those collected fractions were then centrifuged and filtrated again, and after that, they were run a second time through the chromatographer using columns to specifically capture albumins and IgGs. As before, the flow-through fractions, corresponding to the maximum absorbance peaks in the chromatogram, were collected and precipitated in acetone to be used for the mass spectrometry (MS) approach. Final protein extract is obtained without IgG and albumin.

4. A small volume of the purified protein extract obtained from LC was used to validate the quality of the proteins using SDS-PAGE: Some of the collected samples were used to verify the quality of the protein fractions before their use for the mass spectrometry analysis. Protein fractions were separated by SDS-PAGE, showing in this case a clear band pattern for all samples analysed.

5. The rest of the protein extract was used to do mass spectrometry using an unbiased proteomic targeted approach with Sequential Window Acquisition of all Theoretical Mass Spectra (SWATH): All protein samples were precipitated, quantified, and properly prepared to perform nano-LC/MS-MS using a Sequential Window Acquisition of all Theoretical Mass Spectra SWATH-MS data- independent acquisition (DIA) approach for massive protein quantitation.

This protocol has allowed the inventors of the present invention to extract and isolate massive proteins with high quality and purity to be used for proteomic analyses in mucinous tumours, revealing, for instance, MUC13 and TFF1 as potential biomarkers or therapeutic targets.

So, the first embodiment of the present invention refers to a method to isolate proteins from mucinous carcinoma cancer cells which comprises: a) Dissecting and homogenizing the mucin samples to obtain a homogenate, b) centrifugating the homogenate to remove any leftover solid fragment and collecting and filtering the supernatant to remove any cellular component or microorganism, and c) processing the filtrated samples to obtain a purified protein extract by performing two consecutive steps: i) capturing and eliminating glycoproteins, polysaccharides, and glycolipids and ii) capturing and eliminating IgG and albumin.

In a preferred embodiment, the method further comprises: d) Validating the quality of the proteins, and e) quantitative profiling of the proteins.

In a preferred embodiment, the step a) is carried out by using ultrasounds.

In a preferred embodiment, the step c) is carried out by liquid chromatography using two different affinity columns to i) eliminate glycoproteins, polysaccharides and glycolipids, and ii) eliminate IgG and albumin from the sample.

In a preferred embodiment, the step d) is carried out by SDS-PAGE.

In a preferred embodiment, the step e) is carried out by mass spectrometry.

In a preferred embodiment, the proteins obtained, validated and quantified are MUC13 and/or TFF1. The second embodiment of the present invention refers to an in vitro method for identifying or confirming the presence of mucinous carcinoma cancer cells, which comprises assessing the expression level of a protein selected for the group consisting of: MUC13 and/or TFF1 in a biological sample obtained from the subject, wherein the identification of a higher expression level as compared with a pre-established threshold level is an indication of the presence of mucinous carcinoma cancer cells.

The third embodiment of the present invention refers to the in vitro use of a protein selected for the group consisting of: MUC13 and/or TFF1 for identifying or confirming the presence of mucinous carcinoma cancer cells.

The fourth embodiment of the present invention refers to MUC13 and/or TFF1 affinity reagent, inhibitors or blocking agents for use in a method for treating a mucinous carcinoma, preferably Pseudomyxoma peritonei. Alternatively, this embodiment also refers to a method for treating a mucinous carcinoma which comprises administering to the patient a therapeutically effective amount or dose of MUC13 and/or TFF1 affinity reagents, inhibitors or blocking agents, or a composition comprising them along with pharmaceutically acceptable excipients or carriers.

In a preferred embodiment the therapeutic strategy comprises the use of MUC13 affinity reagent or inhibitors, preferably antibodies binding MUC13, most preferably monoclonal antibodies. Consequently, since MUC13 is a membrane protein, it would be used as a target for immunotherapy techniques. Thus, after cytoreductive surgery (the only existing treatment), only the tumour cells that could not be eliminated by surgery and which are the most common cause of recurrence would be selectively destroyed (using immunotherapy). On the other hand, the affinity reagents, for instance antibodies, may be used in the present invention for targeting purposes. According to this scenario, the antibodies used in the present invention may be conjugated with cytotoxic drugs giving rise to a antibody-drug conjugate complex composed by a cytotoxic drug hanging on an antibody scaffold. Alternatively, the antibody may be conjugated with a specific tag, such as a labelling agent, to guide the cytoreductive surgery.

In a preferred embodiment the therapeutic strategy comprises the use of TFF1 blocking agents, preferably nanoliposomes. Since TFF1 is a secreted protein that gives consistency to mucin, the therapeutic strategy would be to block it. A possible solution would be to use nanoliposomes to block its action. In a preferred embodiment, the mucinous carcinoma is PMP.

In a preferred embodiment, the present invention is a computer-implemented invention, wherein a processing unit (hardware) and a software are configured to: a) Receive the expression level values of any of the above cited biomarkers or signatures, b) process the expression level values received for finding substantial variations or deviations, and c) provide an output through a terminal display of the variation or deviation of expression level values, wherein the variation or deviation of the expression level values indicates the presence of mucinous carcinoma cancer cells.

For the purpose of the present invention the following terms are defined:

• According to the present invention, a reference value can be a “pre-established threshold value” or a “cut-off’ value. Typically, a "threshold value" or "cut-off value" can be determined experimentally, empirically, or theoretically. According to the present invention, the “pre-established threshold” value refers to a value previously determined in subjects who are nonsuffering from a mucinous carcinoma. Thus, for instance, the subject is likely to have mucinous carcinoma cancer cells when the level of expression of the proteins MUC13 and/or TFF1 is higher as compared with a pre- established “threshold value”. A “threshold value” can also be arbitrarily selected based upon the existing experimental and/or clinical conditions, as would be recognized by a person of ordinary skilled in the art. The “threshold value” has to be determined in order to obtain the optimal sensitivity and specificity according to the function of the test and the benefit/risk balance (clinical consequences of false positive and false negative). Typically, the optimal sensitivity and specificity (and so the “threshold value”) can be determined using a Receiver Operating Characteristic (ROC) curve based on experimental data.

• The term "comprising" means including, but not limited to, whatever follows the word "comprising". Thus, use of the term "comprising" indicates that the listed elements are required or mandatory, but that other elements are optional and may or may not be present.

• The term "consisting of’ means including, and limited to, whatever follows the phrase “consisting of’. Thus, the phrase "consisting of’ indicates that the listed elements are required or mandatory, and that no other elements may be present. • By “therapeutically effective dose or amount” is intended an amount that, when administered as described herein, brings about a positive therapeutic response in a subject having mucinous carcinoma. The exact amount required will vary from subject to subject, depending on the species, age, and general condition of the subject, the severity of the condition being treated, mode of administration, and the like. An appropriate “effective” amount in any individual case may be determined by one of ordinary skill in the art using routine experimentation, based upon the information provided herein.

• “Pharmaceutically acceptable excipient or carrier” refers to an excipient that may optionally be included in the compositions of the invention and that causes no significant adverse toxicological effects to the patient.

List of abbreviations:

• PMP: Pseudomyxoma Peritonei

• CRS: Cytoreductive surgery

• HIPEC: Hyperthermic intraperitoneal chemotherapy

• PSOGI: The peritoneal surface oncology group international

• LG-PMP: Low grade-PMP

• HG-PMP: High grade-PMP

• AMIPROM: Adapted method to isolate proteins from mucin

• IGs: Immunoglobulins

• MS: Mass spectrometry

• DIA: Data-independent acquisition

• SWATH-MS: Sequential window acquisition of all theoretical mass spectra

• PLS-DA: Partial least squares-di scriminant analysis

• SM: Soft mucin

• HM: Hard mucin

• PPI: Protein-Protein Interaction

• GO: Gene ontology

• GAPDH: Glyceraldehyde 3 -phosphate dehydrogenase

• CRC: Colorectal cancer Description of the figures

Figure 1. Representation of the adapted method to isolate proteins from mucin. (A) Schematic representation of a patient with an appendiceal origin-PMP. Patient goes under cytoreductive surgery (CRS) in combination with hyperthermic intraperitoneal chemotherapy (HIPEC) and mucin samples are obtained. Low and high-grade, soft and hard mucin were used to validate the method here described. Samples were initially processed using the classical method for protein isolation showing a smeared pattern by SDS-PAGE. (B) Adapted method to isolate proteins from mucin (AMIPROM): (1) Mucin samples are dissected in small pieces and homogenize using ultrasounds. (2) The homogenate is centrifugated and the supernatant is collected and filtered. (3) Then, the homogenate is used to do liquid chromatography (LC) using two different columns in order to eliminate glycoproteins, IgG and albumin from the sample. (4) A small volume of the purified protein extract obtained from LC was used to validate the quality of the proteins using SDS-PAGE, showing in this case a clear pattern. (5) The rest of the protein extract was used to do mass spectrometry using an unbiased proteomic targeted approach with Sequential Window Acquisition of all Theoretical Mass Spectra (SWATH).

Figure 2. Proteome analysis of soft (n=14) and hard (n=15) mucin samples from Pseudomyxoma peritonei (PMP) compared to control samples (n=10). (AB) Partial least squares-di scriminant analysis (PLS-DA) of the proteome profile between soft and hard mucin samples and control tissues. (CD) Volcano plots showing Log2 Fold Change expression vs - loglO (p-value) of differentially expressed proteins with a p-value < 0.05 and an absolute Log2 Fold Change > 1 in the same set of samples. The green color indicates upregulated proteins and red color indicates downregulated proteins. (EF) Protein expression levels of mucin isoforms identified in soft (green bars) and hard (blue bars) mucin samples of PMP compared to control tissue (set at 100%; dashed line).

Figure 3. Validation of new putative target therapies for PMP by Western blot and ELISA. (AB) Protein expression levels of MUC13 and TFF1 in soft and hard mucin, [low (LG-PMP; n=4) and high-grade (HG-PMP; n=4)] compared to control tissues (n=4; no tumoral appendix) evaluated by Western Blot. GAPDH was used as a metabolic activity marker. The arbitrary densitometric unit (ADU) for each protein was normalized by the Total Protein Normalization (TPN) value. (CD) Full cohort validation of MUC13 and TFF1 by ELISA quantitation in soft and hard mucin samples [low (LG-PMP) and high-grade (HG- PMP)] compared to control tissues (number of samples analysed is indicated in the bars of the graph). One way ANOVA analysis was carried out with multiple comparisons (LG and HG-PMP vs Control). * p<0.05, **p<0.001 and p<0.0001.

Detailed description of the invention

The present invention is illustrated by means of the Examples set below without the intention of limiting its scope of protection.

Example 1. Material and methods

Example 1.1. Sample collection

Mucin samples were obtained by CRS + HIPEC surgery from patients diagnosed with appendiceal origin-PMP. In total, 24 were soft mucin samples (15 classified as low grade and 9 as high grade) and 22 were hard mucin samples (14 classified as low grade and 8 as high grade). Additionally, 18 control tissue samples were obtained, 9 appendix samples from prophylactic appendectomy due to another medical condition and 9 normal colon samples collected from PMP patients during the CRS + HIPEC surgery. All samples were histologically studied by experienced anatomopathologists to confirm the diagnosis. Demographic and histology results are detailed in Table 1.

Table 1

All patients signed the informed consent, and the study was approved by our local Ethics Committee.

Example 1.2. Liquid Chromatography

Mucin samples (~1 g) were dissected in small fragments (1-3 mm) and homogenized in 3.5- 4.5ml of binding buffer (20mM Tris, 500 mM NaCl, ImM MnC12 and ImM CaC12) using ultrasonic pulses. After centrifugation and filtration with a 0.22pm filter, 3-4ml of each sample were loaded in the Akta Purifier (Cytiva, MA, USA), which was previously prepared with the Hitrap Con A 4B column (Cytiva, MA, USA). Glycoproteins, polysaccharides, and glycolipids were captured in the column and the rest of the sample was collected in 0.4 ml fractions corresponding to the maximum absorbance peaks. All collected fractions were precipitated with 4 volumes of cold acetone, centrifuged and pellets were resuspended in 1.5ml of binding buffer (20mM NaH2PO4 and 150mM NaCl). All fractions were mixed in a single tube, centrifuged, and filtrated. Then, 0.5ml of this filtered extract was loaded again in the Akta Purifier, which was previously prepared with the HiTrap Albumin and IgG depletion column (Cytiva, MA, USA). In the same way, IgGs and albumins were captured in the column and the rest of the sample was collected in 0.2ml fractions corresponding to the maximum absorbance peaks. Then, these fractions were precipitated with 4 volumes of cold acetone and some of them were used to do mass spectrometry. The rest of the fractions were centrifuged and pellets resuspended in lOOpl lysis buffer DIGE (O. lmM urea, lOOOmM thiourea, 30mM Tris and 4% (w/v) 3-[(3-cholamidopropyl)dimethylammonio]-l- propanesulfonate) for Western Blot and ELISA measurements.

Example 1.3. SWATH-MS protein quantification

Purified protein extracts obtained from the liquid chromatography were precipitated and dissolved in 100 pl of Lysis Buffer (EasyPep™ Mini MS Sample Prep Kit; Thermo Fisher Scientific). Samples were quantified using QubitO Protein Assay Kit (Thermo Fisher Scientific) and 20 pg of each protein extract was digested and cleaned using EasyPep™ Mini MS Sample Prep Kit (Thermo Fisher Scientific) following the manufacturer's instructions. Then, samples were dried and resuspended with 2% acetonitrile (ACN) and 0.1% trifluoroacetic acid (TFA) to a final concentration of 1 pg/pl. Samples were analysed using a SWATH data-independent acquisition (DIA) approach for massive protein quantitation. Equal amount of all samples from each group were pooled and subjected to shotgun analysis to build a peptide spectral library. Each pooled sample was analysed twice by data-dependent acquisition (DDA) nanoscale liquid chromatography, followed by tandem mass spectrometry (nano LC-MS/MS) runs, using a LC system Ekspert™ nanoLC425 (Eksigent, Dublin, CA, USA) coupled to a Triple TOF® 6600+ (Sciex, Redwood City, CA, USA) mass spectrometer system. 3 pl of each peptide mixture sample was loaded by the nanoLC425 onto a trap column (3p C18-CL, 350 pm x 0.5mm; Eksigent) and desalted with 0.1% TFA at 5 pl/min during 5 min. Then, the peptides were eluted onto an analytical column (3p C18-CL 120 A, 0.075 x 150 mm; Eksigent) equilibrated in 5% ACN and 0.1% formic acid (FA). Peptide elution was carried out using a 60 min linear gradient of 7 to 40% buffer B (buffer A: 0.1% FA in water; buffer B: 0.1% FA in ACN) at a flow rate of 300nl/min. The Triple TOF® was operated in SWATH mode, in which a 0.050-s TOF MS scan from 350-1250 m/z was performed. Then, 0.080-s product ion scans in 100 variable windows from 400 to 1250 m/z were acquired throughout the experiment. Peptide and protein identifications were performed using Protein Pilot software v5.0 (Sciex). The Paragon algorithm was used to search the Swissprot_200601.fasta database with the following parameters: trypsin specificity, IAM cys-alkylation and taxonomy restricted to Homo sapiens. The MS/MS spectra of the identified peptides were used to generate a spectral library for SWATH peak extraction using the add-in for PeakView Software v2.1 (Sciex) MS/MSALL with SWATH Acquisition MicroApp v2.0 (Sciex). Peptides with a confidence score >95% as reported from Protein Pilot database search were included in the spectral library.

Example 1.4. Prediction of protein interactions and pathway enrichment analysis

The differentially expressed proteins listed from the SWATH quantitative analysis were imported into STRING open database, which has been used to assemble and evaluate proteinprotein association information. For this analysis, medium confidence (0.400), as the minimum required interaction score, was selected. Additionally, ClueGO from the most update version of Cytoscape was used for the enrichment pathway analysis, where Gene Ontology (biological processes, molecular functions, and cellular components), KEGG and Reactome (pathways and reactions) were used.

Example 1.5. Western Blot

The protein concentration of each sample obtained from liquid chromatography was determined using RC DCO Protein Assay (Bio-Rad). The absorbance was read at a wavelength of 750 nm in a standard spectrophotometer (Beckman DU530). Briefly, 5 pg of total protein was subjected to SDS-PAGE on 9% polyacrylamide gels, electro-transferred on polyvinylidene difluoride membranes (Millipore) and probed overnight at 4°C in the presence of the corresponding primary antibody [anti-MUC13 (1/500; Abeam) and anti-TFFl (1/500; Cell Signalling)]. For protein detection, horseradish peroxidase-conjugated secondary antibodies [anti-rabbit (1/5000; Abeam)] were used and chemiluminescence ECL Western Blotting Substrate (Thermo Scientific) was applied. Protein levels were normalized using TLN (Total Line Normalization) method, where the intensity value of total protein from each sample is calculated. Densitometric analysis of protein bands was conducted using Imaged. Example 1.6. ELISA measurements

Protein extracts from liquid chromatography were also used to analyse MUC13 and TFF1 using human commercial ELISA kits [MUC13 (MBS507589; MyBiosource), TFF1 (ab213833; Abeam)] according to the manufacturer's instructions.

Example 1.7. Statistical analyses

Statistical analyses were performed using Prism software v.8.0 (GraphPad Software, La Jolla, CA, USA), except the clustering analyses which were performed with MetaboAnalyst Software v.5.0 (McGill University, Quebec, Canada). Volcano plots were implemented using R language v.4.1.2 36. For proteomic results, Welch T tests were applied for assessment of statistical differences between groups. All data are presented as mean ± standard error of the mean (SEM). Group sizes are denoted in the “Sample collection” section of the Methods and in the figure legends for each experiment. Unless otherwise stated, one-way ANOVA followed by post hoc Bonferroni tests were applied. P-values less than 0.05 were considered significant. Asterisks (* P < 0.05, ** P < 0.01, *** P < 0.00) indicate statistically significant differences.

Example 2. Results

Example 2.1. Adapted method to isolate proteins from mucin (AMIPROM)

First, soft and hard mucin samples obtained from patients diagnosed with appendiceal-origin PMP were used to do a classic protein extraction method to check the quality of the proteins obtained using a electrophoretic protein separation by SDS-PAGE. Results showed a highly smeared bands pattern using both soft and hard mucin protein extracts (Figure 1A). These results were due to the high content of mucin and non-mucin proteins and their interactions, which makes obtaining a clear band pattern extremely challenging and, as a result, these samples unsuitable for high-throughput proteomic analyses. To solve this problem, we designed the first method adapted to mucin samples to extract and isolate massive proteins with high quality and purity to be used for proteomic analyses. In detail, one gram of soft and hard mucin (low and high grade) samples, as well as control samples, were dissected in small fragments and homogenized using ultrasounds (Figure IB). The samples were then centrifuged to remove any leftover solid fragments and filtrated to remove any cellular components or microorganism, as these would interfere with the subsequent chromatography procedures. Next, filtrated samples were processed by two consecutive affinity chromatography steps. First, samples were run through the chromatographer using a HiTrap Con A 4B column to specifically capture glycoproteins, polysaccharides, and glycolipids. The flow-through, corresponding to the maximum absorbance peaks in the two-dimensional plot chromatogram, were collected and precipitated to be used in the next step. Those collected fractions were then centrifuged and filtrated again, and after that, they were run a second time through the chromatographer using a HiTrap Albumin and IgG Depletion column to specifically capture albumins and IgGs. As before, the flow-through fractions, corresponding to the maximum absorbance peaks in the chromatogram, were collected and precipitated in acetone to be used for the mass spectrometry (MS) approach. At this point, some of the collected samples were used to verify the quality of the protein fractions before their use for the mass spectrometry analysis. In this sense, protein fractions were separated by SDS-PAGE, showing in this case a clear band pattern for all samples analysed (Figure IB). Finally, all protein samples were precipitated, quantified, and properly prepared (see Methods section) to perform nano-LC/MS-MS using a Sequential Window Acquisition of all Theoretical Mass Spectra SWATH-MS data-independent acquisition (DIA) approach for massive protein quantitation.

Example 2.2. The Proteome profile in Pseudomyxoma peritonei

Soft and hard (low and high-grade PMP) mucin samples were used to explore the proteome profile of PMP patients to identify, by applying quantitative proteomics, cellular pathways altered in PMP as well as potential therapeutic targets. Samples from prophylactic appendectomy due to another medical condition or normal colon tissue were used as control due to the absence of proper non-tumoral mucin samples (Table 1).

Nano-LC/MS-MS, equipped with SWATH acquisition for label-free quantitative proteomics, has enabled us to get the first library with more than 1000 proteins identified. Taking in account all proteins identified, Partial least squares-di scriminant analysis (PLS-DA) revealed a clear discrimination pattern in the proteome profile between soft or hard mucin and control tissues (Figure 2 AB). Furthermore, a non-supervised hierarchical analysis based on the expression levels of all proteins identified was able to appropriately segregate soft mucins, but not hard mucins, from control tissues. However, the same analysis based on the expression levels of all significantly altered proteins showed a clear segregation between soft or hard mucins and control tissues. Additionally, using a Log2 fold-change difference >1 and a p-value <0.05 to determine differentially expressed proteins, we observed 93 upregulated and 243 downregulated proteins in soft mucin vs control samples, and 86upregulated and 27 downregulated proteins in hard mucin vs control samples (Figure 2 CD). We next identified the different mucin isoforms detected in the PMP subtypes analysed, since mucins are the main proteins that characterize this pathology. In this regard, we found a differential pattern of mucin isoforms between soft and hard mucin samples (Figure 2 EF). Specifically, we detected MUC2, MUC5AC, MUC5B, MUC6 and MUC13 in soft mucin samples compared to control tissues, with MUC2, MUC5AC, MUC5B, and MUC6 significantly upregulated (Figure 2E). In contrast, MUC1, MUC2, MUC4, MUC5AC, MUC5B, and MUC13 were identified in hard mucin samples compared to control tissues, with MUC2, MUC5AC, and MUC13 significantly upregulated (Figure 2F). In all cases, MUC2 was the most expressed mucin isoform. Then, we compared low vs high grade in soft mucin PMP samples, showing that PLS-DA was not able to perfectly segregate between both groups, although was able to discriminate them from control samples. In terms of differentially expressed proteins, volcano plots (using a Log2 fold-change difference >1 and a p-value <0.05) showed 82 upregulated and 228 downregulated in low grade soft mucin (SM) samples compared to control tissues, and 40 upregulated and 383 downregulated in high grade SM samples compared to control tissues. In line with PLS-DA, no significant differences were found in the mucin isoforms identified between low and high-grade in SM samples. In contrast to soft mucin samples, PLS-DA of low and high-grade hard mucin (HM) samples revealed a clear discrimination pattern in the proteome profile between them and control tissues. However, the number of differentially expressed proteins was substantially lower in low (81 upregulated and 92 downregulated proteins) and high-grade (37 upregulated and 63 downregulated proteins) HM samples compared to control tissues than in SM samples, as is illustrated by volcano plots. Additionally, protein expression levels of the identified mucin isoforms showed a significant upregulation in MUC5AC and MUC13 in low grade, but not in high grade HM samples compared to control tissues. However, no statistically significant differences were found between low and high-grade HM samples.

Example 2.3. Proteomic identification of novel targets and pathway analysis in Pseudomyxoma peritonei

Differentially expressed proteins were categorized and displayed in Protein-Protein Interaction (PPI) networks using STRING database. In this regard, we identified three main clusters of proteins for soft mucin samples compared to control tissues, which were associated with specific functional enrichment based on Gene Ontology (GO) and KEGG pathways. Thus, the network of associations termed as cluster 1 was related to metabolic processes, cluster 2 was related to regulation of cytoskeleton and epithelium development, and cluster 3 was mainly related to transport vesicles (Table 2).

Table 2

Table 2: Functional enrichment deri ved from the clusters obtained from the protein-protein interaction networks generated using STRING database for soft and hunt muem samples compared to control tissue.

In addition, we identified mucin isoforms in the PPI networks since mucins are the main proteins that characterize this pathology. In this sense, MUC5AC was identified in cluster 1 of the soft mucin vs control tissue network. It is important to note that MUC2 was not identified in any PPI network generated in this study because this protein is not included in the current STRING database (version 11.5). In the same line, we identified three main clusters in the hard mucin samples vs control tissue network: cluster 1 was related to extracellular matrix, cluster 2 was related to metabolic pathways, and cluster 3 was related to ribonucleoprotein complex (Table 2). Interestingly, MUC5AC and MUC13 were identified outside of those clusters. We also explored the PPI networks for low and high-grade soft and hard mucin samples compared to control tissues. In the low and high-grade SM vs control tissue comparisons, we were not able to identify any cluster associated with functional enrichment. However, MUC5AC was found directly interacting with TFF1 and TFF2 in low grade SM samples compared to control tissue, two proteins from the trefoil factor family. In the high-grade SM vs control tissue comparison, only MUC13 was identified. Furthermore, the PPI network from low grade HM samples compared to control tissue identified two main clusters of proteins, where cluster 1 was not related to any specific functional enrichment, probably due to the high number of proteins included in this cluster, and cluster 2 was related to ribonucleoprotein complex and mRNA binding. In contrast, we did not identify any cluster or mucin isoform in high grade HM samples compared to control tissue. Next, we used GO- enrichment and KEGG/Reactome analyses to identify potential biological processes and pathways associated with the different PMP subtypes as well as with the different mucin isoforms identified. GO-enrichment analyses identified a high number of biological processes and components altered in soft mucin samples compared to control tissues mainly related with metabolic processes (e.g., response to carbohydrate, peptidase activity, organic acid metabolic process, and small molecule biosynthetic process, among others), regulation of immune system (e.g., activation of immune response, positive regulation of immune system process, humoral immune response and interleukin- 12-mediated signalling pathway, among others), and extracellular matrix and cytoskeleton/membrane reorganization (e.g., regulation of anatomical structure size, focal adhesion, anatomical structure homeostasis, collagen containing extracellular matrix, membrane organization, etc.). Interestingly, mucins isoforms were directly associated with collagen-containing extracellular matrix, anatomical structure homeostasis, extracellular exosome, regulation of response to stress, and immune system processes. In the same line, enrichment analysis in hard mucin samples compared to control tissues revealed a high number of altered processes mainly related with cytoskeleton reorganization (e.g., Arp2/3 protein complex, actin filament bundle assembly, actin cytoskeleton, etc.), immune system (complement activation), and metabolic processes (canonical glycolysis, peptidase regulator activity, cellular aldehyde metabolic process, etc.). As before, mucins isoforms were found associated not only with collagen-containing extracellular matrix and extracellular exosome, but also with gastrointestinal epithelium maintenance. Furthermore, KEGG/Reactome pathway enrichment analyses corroborated the results observed with GO analyses, with immune system and metabolism being the most altered pathways in both soft and hard mucin samples compared to control tissues. In addition, GO enrichment analyses of low and high-grade SM samples compared to control tissues did not reveal major differences between both comparisons, with the immune system, extracellular exosome, metabolism, and cytoskeleton reorganization being the most altered processes in both cases. In contrast, enrichment analyses of low and high-grade HM samples compared to control tissues showed some interesting differences between them. In this regard, extracellular space/matrix, immune system, and regulated exocytosis were the most altered biological processes/components in low grade HM samples compared to control tissues. However, complement activation, secretory granule lumen and keratinization were the most altered biological processes in high grade HM samples compared to control tissues.

Example 2.4. Validation of MUC13 and TFF1 protein concentration levels in soft and hard mucin samples of Pseudomyxoma peritonei

The first proteomic profile in PMP has revealed more than three hundred proteins altered in this tumoral tissue, in soft and hard mucin (Figure 2 CD). Furthermore, protein-protein interactions analysis by STRING in soft and hard mucin revealed new functional protein association networks. In this scenario and focusing just on the protein-protein interactions between extracellular mucins and other proteins, two candidates, MUC13 and TFF1, were selected to be individually validated from the high-throughput proteomic analysis. MUC13 is a transmembrane protein which is part of the mucin family; TFF1 is a protein which has an essential role in the gastric mucosa structure stabilising the MUC2 and MUC5AC polymers, to make the soft extracellular mucin more resistant; these aspects make them good therapeutic candidates. Both proteins were individually quantified by western blot and ELISA in soft and hard mucin from PMP patients (Figure 3). The western blot data (Figure 3 AB) were carried out in soft and hard mucin collected from four LG-PMP and four HG-PMP patients and were compared against four control samples. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was included as a metabolic sensor to detect the presence of cells. Control samples and hard mucin showed similar GAPDH levels in contrast to soft mucin, which didn’t show a GAPDH signal, probably due to the low number of cells in liquid (soft) mucin. Interestingly, MUC13 and TFF1 showed significant higher expression in LG-PMP samples in soft and hard mucin. However, HG-PMP samples in both soft and hard mucin showed sowed no significant differences with the control. To make stronger these results, MUC13 and TFF1 were quantified in the whole cohort by ELISA technology (Figure 3 CD). In this case, both proteins showed a significant rise compared to control in all cases, LG and HG-PMP in soft and hard mucin (Figure 3 CD).

Claims

1. Method to isolate proteins from mucinous carcinoma cancer cells which comprises: a. Dissecting and homogenizing the mucin samples to obtain a homogenate, b. Centrifugating the homogenate to remove any leftover solid fragment and collecting and filtering the supernatant to remove any cellular component or microorganism, c. Processing the filtrated samples to obtain a purified protein extract by performing two consecutive steps: i) capturing and eliminating glycoproteins, polysaccharides, and glycolipids and ii) capturing and eliminating IgG and albumin.

2. Method, according to claim 1, which further comprises: d. Validating the quality of the proteins, and e. Quantitative profiling of the proteins.

3. Method, according to any of the previous claims, wherein the step a) is carried out by using ultrasounds.

4. Method, according to any of the previous claims, wherein the step c) is carried out by liquid chromatography using two different affinity columns to i) eliminate glycoproteins, polysaccharides and glycolipids, and ii) eliminate IgG and albumin from the sample.

5. Method, according to any of the previous claims, wherein the step d) is carried out by SDS-PAGE.

6. Method, according to any of the previous claims, wherein the step e) is carried out by mass spectrometry.

7. Method, according to any of the previous claims, wherein the proteins obtained, validated and quantified are MUC13 and/or TFF1.

8. Method, according to any of the previous claims, wherein the mucinous carcinoma is Pseudomyxoma peritonei.

9. In vitro method for identifying or confirming the presence of mucinous carcinoma cancer cells, which comprises assessing the expression level of a protein selected for the group consisting of: MUC13 and/or TFF1 in a biological sample obtained from the subject, wherein the identification of a higher expression level as compared with a pre-established threshold level is an indication of the presence of mucinous carcinoma cancer cells.

10. In vitro method, according to claim 9, wherein the mucinous carcinoma is Pseudomyxoma peritonei.

11. In vitro use of a protein selected for the group consisting of: MUC13 and/or TFF1 for identifying or confirming the presence of mucinous carcinoma cancer cells.

12. In vitro use, according to claim 11, of a protein selected for the group consisting of:

MUC13 and/or TFF1 for identifying or confirming the presence of Pseudomyxoma peritonei cancer cells.

13. MUC13 and/or TFF1 affinity reagent, inhibitors or blocking agents for use in a method for treating a mucinous carcinoma, preferably Pseudomyxoma peritonei.

14. MUC13 affinity reagents or inhibitors for use, according to claim 13, wherein the affinity reagents or inhibitors are antibodies binding MUC13, preferably monoclonal antibodies.

15. TFF1 blocking agents for use, according to claim 13, wherein the blocking agents are nanoliposomes.