In vitro method for the diagnosis and / or prognosis of pancreatic cancer; and / or monitoring treatment response of patients suffering from pancreatic cancer

Abstract

The present invention refers to the medical field. Particularly, the present invention refers to an in vitro method for the diagnosis and / or prognosis of pancreatic cancer; and / or monitoring treatment response of patients suffering from pancreatic cancer.

Description

[0001] IN VITRO METHOD FOR THE DIAGNOSIS AND / OR PROGNOSIS OF PANCREATIC CANCER; AND / OR MONITORING TREATMENT RESPONSE OF PATIENTS SUFFERING FROM PANCREATIC CANCER FIELD OF THE INVENTION

[0002] The present invention refers to the medical field. Particularly, the present invention refers to an in vitro method for the diagnosis and / or prognosis of pancreatic cancer (PC); and / or monitoring treatment response of patients suffering from pancreatic cancer.

[0003] STATE OF THE ART PC is one of the deadliest cancers, ranking as the third leading cause of cancer-related deaths globally. The high mortality rate is primarily due to late-stage diagnoses and the limited efficacy of current treatments. This cancer represents a significant public health challenge, as it is associated with high morbidity due to severe pain, bile duct obstruction, and functional decline, leading to a substantial reduction in patients' quality of life. Consequently, there is a critical need to improve early detection and enhance disease monitoring to assess therapeutic response and / or disease progression using minimally invasive techniques.

[0004] Traditionally, the diagnosis and molecular profiling of cancer patients have relied on tissue biopsies. However, in recent years, liquid biopsy, which involves analyzing blood or other body fluids, has emerged as a promising alternative for detailed patient evaluation across various cancer types. One of the main advantages of this technique is its minimal invasiveness, which allows for the early detection of the disease and the real-time study of tumor progression mechanisms and treatment resistances, which can evolve over time. Circulating free DNA (cfDNA) is a relevant component of liquid biopsy, which can exhibit several types of molecular alterations that reflect the tumor’s molecular profile, such as epigenetic modifications.

[0005] Epigenetic modifications, such as DNA methylation, play a crucial role in regulating the expression of tumor suppressor genes and oncogenes in malignant cells. DNA methylation, the most well-characterized and studied epigenetic modification, involves the addition of methyl groups (CH3) to cytosines (C) in CpG dinucleotides of DNA, thus regulating gene expression. Because methylated DNA is released from tumor cells into the bloodstream, analyzing this epigenetic mark in liquid biopsy elements, such as cfDNA, holds significant promise as a biomarker in cancer. In this context, recent studies have shown the clinical utility of cfDNAmethylation analysis for early diagnosis and monitoring therapeutic response in certain gastrointestinal cancers, such as colorectal cancer.

[0006] Early diagnosis of PC remains a formidable challenge. Currently, no approved biomarkers exist for the early detection of PC, which significantly hampers the ability to diagnose the disease at a stage where curative treatment options are still viable. Most cases are diagnosed at an advanced stage, resulting in poor prognosis and limited treatment options. The development of reliable biomarkers for early detection would represent a significant breakthrough in improving the survival rates for PC patients. Liquid biopsy, particularly the analysis of cfDNA methylation, holds potential for the early detection of PC by identifying epigenetic changes that occur in the early stages of tumor development.

[0007] The therapeutic management of PC represents a significant challenge for oncologists. Current follow-up tools, including symptom-based assessments, changes in tumor markers like CEA or CA19.9, and radiological findings, are often insufficiently sensitive and specific for evaluating the treatment efficacy of this tumor type. Discontinuing ineffective treatments reduces toxicity and allows patients to benefit from more effective therapeutic strategies. Since DNA methylation is a mechanism that contributes to disease progression and can be detected through liquid biopsy studies, monitoring methylation in cfDNA could establish new guidelines for predicting and tracking therapy response, ultimately improving patients’ quality and life expectancy.

[0008] There is a clinical need for biomarkers that enable early detection and monitoring therapeutic response in PC to achieve better and more personalized patient management. Therefore, this present invention aims to identify and validate epigenetic biomarkers based on methylation as a useful tool for non-invasive early detection and for monitoring therapeutic response and disease progression of metastatic pancreatic adenocarcinoma patients.

[0009] DESCRIPTION OF THE INVENTION

[0010] Brief description of the invention

[0011] The present invention refers to an in vitro method identifying biomarker signatures for the diagnosis and / or prognosis of pancreatic cancer; and / or monitoring treatment response of patients suffering from pancreatic cancer. Particularly, the present invention refers to an in vitro method for the diagnosis and / or prognosis of pancreatic cancer; and / or monitoring treatment response of patients suffering from pancreatic cancer.Such as it is shown in Example 1 and Example 2 set below, the inventors of the present invention have identified methylation biomarkers in primary tumors of PC and have validated signatures comprising at least two of the following genes: AJAPL PRKCB and WBSCR17 for the non-invasive detection of PC. Although the best statical results are provided by gene combinations comprising at least two of the above cited genes, Example 1 and Example 2 set below also show that the genes have individually given a high AUC value so any of them could be individually used for the diagnosis and / or prognosis of PC; and / or for monitoring therapeutic response and disease progression of patients suffering from PC.

[0012] So, the first embodiment of the present invention refers to an in vitro method for identifying biomarker signatures for the diagnosis and / or prognosis of PC, or a precancerous stage thereof; and / or monitoring treatment response of patients suffering from PC, or a precancerous stage thereof, wherein the method comprises assessing the methylation status of at least two genes selected from the list consisting of: AJAPL PBKCB and / or WBSCR17, in a biological sample obtained from a subject; or to the in vitro use of at least two genes selected from the list consisting of: AJAPL PRKCB and / or WBSCR17, or of a kit comprising reagents for the determining the methylation status the genes, for identifying biomarker signatures for the diagnosis and / or prognosis of PC, or a precancerous stage thereof; and / or monitoring treatment response of patients suffering from PC, or a precancerous stage thereof.

[0013] The second embodiment of the present invention refers to an in vitro method for the diagnosis and / or prognosis of PC, or a precancerous stage thereof; and / or monitoring treatment response of patients suffering from PC, or a precancerous stage thereof, wherein the method comprises assessing the methylation status of at least a gene selected from the list consisting of: AJAP1 and / or WBSCR17, in a biological sample obtained from a subject; or to the in vitro use of at least one gene selected from the list consisting of: AJAP1 and / or WBSCR17, or of a kit comprising reagents for the determining the methylation status the genes, for the diagnosis and / or prognosis of PC, or a precancerous stage thereof; and / or monitoring treatment response of patients suffering from PC, or a precancerous stage thereof.

[0014] In a preferred embodiment, the in vitro method for the diagnosis and / or prognosis of PC, or a precancerous stage thereof; and / or monitoring treatment response of patients suffering from PC, or a precancerous stage thereof, further comprises assessing the methylation status of the gene PRKCB in a biological sample obtained from the patient.In a preferred embodiment, the method of the invention comprises assessing the methylation status of the following gene combinations: AJAP1 + PRKCB; AJAP1 + WBSCR17; PRKCB + WBSCR17; or AJAP1 + PRKCB + WBSCR17.

[0015] In a preferred embodiment, if a deviation of the methylation status is identified, as compared with a pre-established reference value, this is indicative that the biomarker signature may be used for the diagnosis and / or prognosis of PC, or a precancerous stage thereof; and / or monitoring treatment response of patients suffering from PC, or a precancerous stage thereof, or is indicative that the subject is suffering from PC, or a precancerous stage thereof, or has a poor prognosis.

[0016] In a preferred embodiment, the methylation status of the genes is determined in at least a CpG site of the gene.

[0017] In a preferred embodiment, the methylation status of the genes is determined in at least a CpG site of the promoter region.

[0018] In a preferred embodiment, the methylation status of the gene AJAP1 is determined in at least the CpG site cgl3495205, the methylation status of the gene PRKCB is determined in at least the CpG site cg03306374 and / or the methylation status of the gene WBSCR17C determined in at least a CpG site selected from cg03044249 or cg01366419 (see Table 1).

[0019] Table 1

[0020]

[0021]

[0022] In a preferred embodiment, the biological sample is selected from a biological fluid obtained from liquid biopsy, such as plasma, serum, blood, saliva, cerebrospinal fluid or urine, cyst fluid, pancreatic juice or duodenal juice; or a tissue sample; preferably a minimally invasive sample. The third embodiment of the present invention refers to a kit, suitable for the diagnosis and / or prognosis of PC, or a precancerous stage thereof; and / or monitoring treatment response of patients suffering from PC, or a precancerous stage thereof, which comprises a pair of primers and a probe for the amplification and detection of at least a fragment of gene AJAP1 and / or a pair of primers and a probe for the amplification and detection of at least a fragment of gene WBSCR17, and wherein:

[0023] a) The forward primer for the amplification of at least a fragment of gene AJAP1 is selected from SEQ ID NO: 1 or SEQ ID NO: 4;

[0024] b) The reverse primer for the amplification of at least a fragment of gene AJAP1 is selected from SEQ ID NO: 2 or SEQ ID NO: 5;

[0025] c) The probe for the detection of the methylation of the gene AJAP1 is selected from SEQ ID NO: 3 or SEQ ID NO: 6;

[0026] d) The forward primer for the amplification of at least a fragment of gene WBSCR17 is selected from SEQ ID NO: 13 or SEQ ID NO: 16;

[0027] e) The reverse primer for the amplification of at least a fragment of gene WBSCR17 is selected from SEQ ID NO: 14 or SEQ ID NO: 17;

[0028] f) The probe for the detection of the methylation of the gene WBSCR17 is selected from SEQ ID NO: 15 or SEQ ID NO: 18.

[0029] In a preferred embodiment, the kit further comprises a pair of primers and a probe for the amplification and detection of gene PRKCB, and wherein:

[0030] a) The forward primer for the amplification of at least a fragment of gene PRKCB is selected from SEQ ID NO: 7 or SEQ ID NO: 10;

[0031] b) The reverse primer for the amplification of at least a fragment of gene PRKCB is selected from SEQ ID NO: 8 or SEQ ID NO: 11;c) The probe for the detection of the methylation of the gene PRKCB is selected from SEQ ID NO: 9 or SEQ ID NO: 12.

[0032] Moreover, the invention is based on the finding that the methylation status of IRF4 and / or RJC3 is useful for:

[0033] • Individual value of the genes IRF4 and RIC3 (Figures 9 to 11).

[0034] • Non-invasive detection of pancreatic cancer (Example 4.2; Figures 10A-10B), and • Non-invasive monitoring of disease progression and response to effective therapy in metastatic PC patients during follow-up (Example 4.4; Figure 11), and

[0035] • Further improving diagnostic performance by combining IRF4 and / or RIC3 methylation with methylation status of additional genes, optionally via a Random Forest model (Example 4.3; Tables 6-7; Figures 4-5), and for monitoring using combined biomarkers over time (Example 4.5; Figure 12).

[0036] So, in one aspect, the invention provides an in vitro method for detecting pancreatic cancer in a subject, comprising obtaining a sample and determining the methylation status of IRF4 and / or RIC3, wherein said methylation status is used to classify the subject as having PC (Example 4.2; Figures 10A-10B).

[0037] The disclosure reports that cfDNA methylation analysis confirms results previously obtained in primary PC tumors, showing significantly higher methylation levels in PC than in controls (Example 4.2; Figure 10A) and significant (p < 0.05) AUCs for PC detection for the individual genes (AUC_IRF4 = 0.696 and AUC_RIC3 = 0.674) (Example 4.2; Figure 10B).

[0038] In particular embodiments, methylation is determined using droplet digital PCR (ddPCR) (Example 3.6; Example 4.4), including assays designed to distinguish methylated (M) and unmethylated (U) sequences for IRF4, and methylation levels for RIC3 normalized to a reference gene (ALB) (Example 3.6; Table 5).

[0039] In further embodiments, the invention relates to determining methylation at CpG positions within promoter regions amplified / detected by the disclosed assays (Example 3.6; Table 5). Table 5 provides:

[0040] • IRF4 assay region chr6:391880-391982 (hg!9) and the CpG positions detected, including an annotated CpG cg06392169 (Example 3.6; Table 5), and

[0041] • RIC3 assay region chrl 1:8190529-8190608 (hg!9) and CpG positions detected, including annotated CpGs cg08383315 and cg25778535 (Example 3.6; Table 5).In another aspect, the invention provides methods where methylation status of IRF4 and / or RIC3 is combined with methylation status of additional genes (AJAP1, PRKCB, WBSCR17) and evaluated using a Random Forest algorithm (Example 3.7; Example 4.3).

[0042] For tissue samples, the disclosure describes Random Forest analysis in the TCGA cohort (176 tumor tissues; 10 adjacent healthy tissues), reporting that combinations of at least two genes among AJAP1, PRKCB, WBSCR17, IRF4 and RIC3 yield AUCs = 1.0 for PC detection (Example 4.3; Table 6; Figure 4).

[0043] For plasma cfDNA, Random Forest analysis in the plasma cohort (41 controls; 44 PC cases) is reported to produce higher AUCs for combinations of at least two of the five genes than single-gene AUCs (Example 4.3; Table 7; Figure 5).

[0044] In a further aspect, the invention provides in vitro monitoring methods in metastatic PC patients by measuring I RIA and / or RIC 3 methylation in cfDNA at different time points and correlating changes with clinical evolution evaluated by standard practice using CT scans (Example 4.4).

[0045] The disclosure states that cfDNA methylation of IRF4 and RJC3 increases with disease progression and decreases in response to effective therapy, with decreases being described as faster than the clinical biomarker CAI 9-9 (Example 4.4; Figure 11).

[0046] Additionally, combined methylation status of at least two of the five genes (AJAP1, PRKCB, WBSCR17, I RIA, RIGS') is used longitudinally in two patients, where combined methylation increases with progression and decreases in response to therapy, with reduction described as faster than CA19-9 (Example 4.5; Figure 12).

[0047] In one embodiment, an in vitro method is provided for identifying biomarker signatures for diagnosis and / or prognosis of pancreatic cancer (PC), or a precancerous stage thereof, and / or for monitoring treatment response, wherein the method comprises assessing the methylation status of at least one gene selected from IRF4 and / or RJC3 in a biological sample obtained from a subject. In a representative implementation, a plasma sample is obtained and cell-free DNA (cfDNA) is isolated, bisulfite converted, and analyzed for methylation by ddPCR as described tor IRF4 and RJC3 (Example 3.6, Table 5). The resulting methylation data are used to identify a biomarker signature capable of discriminating PC from controls (Example 4.2, Figures 10A-10B)

[0048] In one embodiment, IRF4 and / or RIC3 is used in vitro, or a kit comprising reagents for determining the methylation status of IRF4 and / or RJC3 is used in vitro, for identifyingbiomarker signatures suitable for diagnosis and / or prognosis of PC and / or monitoring treatment response. In a representative embodiment, the use comprises applying ddPCR assays targeting IRF4 and / or RIC3 promoter methylation (Example 3.6, Table 5) to cfDNA obtained from plasma samples of PC patients and controls, and analyzing the resulting methylation profiles to identify biomarker signatures with diagnostic utility (Example 4.2, Figures 10A-10B; Example 4.3, Table 7, Figure 5).

[0049] In one embodiment, the method or use comprises assessing the methylation status of both IRF4 and RIC3 in a biological sample obtained from a subject. In a representative example, IRF4 methylation is assessed using methylated (M) and unmethylated (U) assays and RIC3 methylation is assessed using a methylation assay normalized to a reference gene, as described in the ddPCR workflow (Example 3.6, Table 5). The combined IRF4 and RIC3 methylation readouts are used to support PC detection in cfDNA and / or to contribute to a multi-gene signature (Example 4.2, Figure 10; Example 4.3, Table 7).

[0050] In one embodiment, the method or use further comprises assessing methylation status of at least one gene selected from AJAP1, PRKCB and / or WBSCR17 in addition to IRF4 and / or RJC3. In a representative embodiment, methylation data from IRF4 and / or RIC3 are combined with methylation data from A JAPP PRKCB and / or WBSCR17 to build and / or evaluate a biomarker signature using a Random Forest algorithm (Example 3.7; Example 4.3), such that combinations including IRF4 and / or RIC3 provide improved diagnostic accuracy in tissue samples (Table 6, Figure 4) and in plasma cfDNA (Table 7, Figure 5).

[0051] In one embodiment, an in vitro diagnostic and / or prognostic method is provided, and / or a method for monitoring treatment response, wherein the method comprises assessing methylation status of I RIA and / or RIC3 in a biological sample obtained from a subject. In a representative embodiment, promoter methylation of IRF4 and / or RIC3 is determined in cfDNA from plasma, and the methylation level is used to classify a subject as PC or non-PC (Example 4.2, Figures 10A-10B). In further embodiments, longitudinal measurements of I RIA and / or RIC3 methylation are performed to monitor disease evolution or response to therapy (Example 4.4, Figure 11).

[0052] In one embodiment, IRF4 and / or RIC3 is used in vitro, or a kit comprising reagents for determining methylation of IRF4 and / or RJC3 is used in vitro, for diagnosis and / or prognosis of PC and / or monitoring treatment response. In one representative implementation, the kit reagents are used to perform ddPCR-based methylation analysis AARIA and / or RIC3 promoterregions in plasma cfDNA, thereby providing methylation readouts that discriminate PC from controls (Example 4.2, Figure 10B) and / or inform monitoring of progression / response (Example 4.4, Figure 11; Example 4.5, Figure 12).

[0053] In one embodiment, the method or use comprises assessing methylation status of both IRF4 and RJC3 in a biological sample obtained from a subject for diagnosis / prognosis and / or monitoring. In a representative embodiment, IRF4 methylation is determined using methylated and unmethylated ddPCR assays and RJC3 methylation is determined using a methylation ddPCR assay normalized with a reference gene (Example 3.6, Table 5). The combined IRF4+RIC3 methylation status is then used as part of the diagnostic and / or monitoring determination (Example 4.2, Figure 10; Example 4.4, Figure 11).

[0054] In one embodiment, the method or use further comprises assessing methylation of at least one of AJAP1, PRKCB and / or WBSCR17 together with IRF4 and / or RIC3. In representative embodiments, a Random Forest algorithm integrates methylation values from at least two genes among A.JAP1, PRKCB, WBSCR17, IRF4 and RIC3 to generate a diagnostic classification for PC, as described for tissue (Example 4.3, Table 6, Figure 4) and plasma cfDNA (Example 4.3, Table 7, Figure 5). In further embodiments, the combined methylation status of at least two of said genes is used longitudinally for monitoring (Example 4.5, Figure 12).

[0055] In one embodiment, the method or use comprises assessing methylation status of at least one gene combination selected from the following combinations:

[0056] • AJAP1+WBSCR17+IRF4+RIC3,- • AJAP1+PRKCB+WBSCR17+IRF4+RIC3,- • AJAP1+PRKCB+WBSCR17+IRF4,- • AJAP1+WBSCR17+IRF4,- • AJAP1+WB CR17+RIC3,- • AJAP1+PRKCB+WBSCR17+RIC3,- • PRKCB+WBSCR17+IRF4,- • PRKCB+WBSCR17+IRF4+RIC3,- • WBSCR17+IRF4+RIC3,- • WBSCR17+IRF4,- • PRKCB+WB CR17+RIC3,- • WBSCR17+RIC3,-• AJAP1+PRKCB+IRF4,- • AJAR l+PRK(Ai+IRIA+RKA

[0057] • AJAR l+IRJA+RKA

[0058] • AJAP1+IRIA.,

[0059] • AJAP1+PRKCB+RIC3,- • AJAP1+RIC3,- • PRKCB+IRF4+RIC3,- • IRIA+RKA

[0060] • PRKCB+IRF4,- • PRKCB+BJC3.

[0061] In representative embodiments, methylation values for the genes in a selected combination are determined from tissue DNA (Example 4.3, Table 6) or from plasma cfDNA (Example 4.3, Table 7) and processed using a Random Forest model to obtain a classification output indicative of PC.

[0062] In one embodiment, the method or use comprises comparing the measured methylation status with a pre-established reference value, and identifying a deviation as indicative that the biomarker signature is suitable for diagnosis / prognosis / monitoring and / or that the subject suffers from PC or has poor prognosis. In a representative embodiment, the reference value is derived from the distribution of methylation values in controls and / or from an optimal cutoff determined by Youden’s index in a ROC analysis (Example 3.7), and the deviation corresponds to methylation levels consistent with PC as shown for IBF4 and BJC3 (Example 4.2, Figure 10A).

[0063] In one embodiment, methylation status is determined in at least one CpG site of the gene. In representative embodiments, methylation is assessed at CpGs within the amplified / detected regions for I RIA and BIC3 as defined by the ddPCR assays (Example 3.6, Table 5), thereby providing CpG-resolved methylation status supporting diagnostic and / or monitoring determinations (Examples 4.2, 4.4, Figures 10 and 11).

[0064] In one embodiment, methylation status is determined in at least one CpG site of the promoter region. In representative embodiments, I RIA and BJC3 promoter regions are assessed, as the assays are explicitly described as promoter methylation assays (Examples 3.6 and 4.2) and Table 5 indicates that the corresponding regions are promoter regions (“PROMOTER: Yes”). CpG positions interrogated for IRIA a.vA RK'3 promoter regions are provided in Table 5.In one embodiment, methylation status is determined at specific CpG sites, namely: IRF4 at least at cg06392169, and RIC3 at least at cg08383315 or cg25778535, and further optionally AJAP1 at least at cgl3495205, PRKCB at least at cg03306374, and / or WBSCR17 at least at cg03044249 or cgO 1366419. In a representative embodiment for IBF4 and BIC3, ddPCR assays interrogate CpG positions that include cg06392169 for IRF4 and cg08383315 / cg25778535 for BJC3 as annotated in Table 5 (Example 3.6, Table 5). The methylation outputs are then used for PC detection (Example 4.2, Figure 10) and / or in multi-gene combinations (Example 4.3, Tables 6-7)

[0065] Table 2

[0066]

[0067] In one embodiment, the biological sample is a liquid biopsy or a tissue sample. In representative embodiments explicitly described, the sample is plasma from which cfDNA is isolated and analyzed (Examples 3.2-3.6, 4.2-4.5; Figures 10-12). In further embodiments, the sample is a tissue sample, including primary tumor tissue and adjacent healthy tissue analyzed in the TCGA cohort (Example 4.3, Table 6, Figure 4).In one embodiment, a kit is provided for diagnosis / prognosis of PC and / or monitoring treatment response, comprising primers and probes for amplification and detection of IRF4 and / or RIC3. In a representative embodiment, the kit comprises reagents configured for ddPCR detection of methylated and unmethylated IRF4 sequences and methylated RJC3 sequences as characterized in Table 5 (Example 3.6). In one implementation, the kit comprises:

[0068] • IRF4 forward primer selected from SEQ ID NO: 19 or SEQ ID NO: 22;

[0069] • IRF4 reverse primer selected from SEQ ID NO: 20 or SEQ ID NO: 23;

[0070] • IRF4 probe selected from SEQ ID NO: 21 or SEQ ID NO: 24; and

[0071] • RIC3 forward primer SEQ ID NO: 25, RIC3 reverse primer SEQ ID NO: 26, and RIC3 probe SEQ ID NO: 27;

[0072] as suitable for determining promoter methylation status in cfDNA (Example 3.6, Table 5) and for diagnostic and monitoring applications (Examples 4.2, 4.4, Figures 10-11).

[0073] In one embodiment, the kit further comprises primers and probes for amplification and detection of AJAP1 and / or WBSCR17 methylation targets, in addition to IRF4 and / or RIC3, thereby enabling multi-gene methylation panels. In a representative embodiment, the kit includes AJAP1 primer / probe sets and WBSCR17 primer / probe sets as recited in claim 16, and is used to determine methylation status for combinations of at least two genes among AJAPI, PRKCB, WBSCR17, IRF4, and RIC3, consistent with the combined-biomarker diagnostic and monitoring approaches described (Example 4.3, Tables 6-7; Example 4.5, Figure 12).

[0074] In one embodiment, the kit further comprises primers and probes for amplification and detection of PRKCB methylation targets, thereby supporting expanded panels comprising A.JAPl. WBSCR17, PRKCB, and IRF4 / RIC3. In a representative embodiment, the kit is used to assess methylation status of gene combinations including PRKCB together with / / ? / ’ - / and / or RJC3, such as those reported in Tables 5 and 6 (Example 4.3) and for longitudinal monitoring using combined methylation status (Example 4.5, Figure 12).

[0075] The present invention also refers to a method for treating PC, or a precancerous stage thereof which comprises, as a first step, diagnosis and / or prognosis of PC, or a precancerous stage thereof; and / or for monitoring therapeutic response and disease progression of patients suffering from PC, or a precancerous stage thereof, following the method of the present invention. The main treatments for PC, or a precancerous stage thereof, depend on the stage and type of the cancer, as well as the patient’s overall health. The most common treatmentoptions that would be used once the patient is diagnosed with PC, or a precancerous stage thereof are:

[0076] 1. Surgery:

[0077] • Whipple Procedure (Pancreaticoduodenectomy): This is the most common surgery for tumors in the head of the pancreas.

[0078] • Distal Pancreatectomy: Removes the tail and sometimes part of the body of the pancreas.

[0079] • Total Pancreatectomy: Involves the removal of the entire pancreas, though it’s less common.

[0080] 2. Radiation Therapy:

[0081] • Often used to shrink tumors before surgery or to kill any remaining cancer cells after surgery.

[0082] • External beam radiation therapy is the most common type.

[0083] 3. Chemotherapy:

[0084] • Drugs like gemcitabine or combinations such as FOLFIRINOX (a combination of fluorouracil, leucovorin, irinotecan, and oxaliplatin) are frequently used.

[0085] • Chemotherapy may be used before surgery (neoadjuvant) or after surgery (adjuvant) to improve outcomes.

[0086] 4. Targeted Therapy:

[0087] • Drugs that target specific genetic mutations or proteins involved in cancer cell growth.

[0088] • Erlotinib, which targets the EGFR protein, is sometimes used for certain patients.

[0089] 5. Immunotherapy:

[0090] • Typically used for PC with specific genetic changes, such as mismatch repair deficiency or high microsatellite instability.

[0091] • Checkpoint inhibitors like pembrolizumab can be options for these cases.

[0092] 6. Palliative Care:

[0093] • Focuses on relieving symptoms and improving quality of life, especially for advanced- stage pancreatic cancer.

[0094] • This might include pain management, nutritional support, and treatment of jaundice (e.g., stent placement for bile duct obstruction).

[0095] 7. Clinical Trials:Participation in clinical trials can provide access to new and experimental treatments that may be effective when standard therapies are not.

[0096] Treatment plans often involve a combination of these approaches, customized to each patient’s needs and the cancer’s specifics.

[0097] In a preferred embodiment, the present invention is a computer-implemented invention, wherein a processing unit (hardware) and a software are configured to: Receive the methylation status of any of the above cited biomarkers or signatures, process the methylation status received for finding substantial variations or deviations, and provide an output through a terminal display of the variation or deviation of the methylation status, wherein the variation or deviation of the methylation status indicates that the subject may be suffering from PC or that the patient has a poor prognosis.

[0098] The diagnosis, prognosis or monitoring carried out in the context of the present invention by means of assessing the methylation status of the genes, may be also combined with image techniques. Several imaging techniques are used to diagnose, stage, and monitor treatment response in pancreatic cancer. Here are the main imaging methods that could be used in the context of the present invention:

[0099] 1. Computed Tomography (CT) Scan:

[0100] • A CT scan is one of the most commonly used imaging techniques for detecting pancreatic cancer.

[0101] • Multiphase CT scans with contrast provide detailed images of the pancreas and surrounding structures, helping determine the size and spread of the tumor.

[0102] 2. Magnetic Resonance Imaging (MRI):

[0103] • An MRI uses magnetic fields and radio waves to produce detailed images of the pancreas.

[0104] • MRCP (Magnetic Resonance Cholangiopancreatography) is a special type of MRI that focuses on the bile ducts and pancreatic ducts, useful for identifying blockages or abnormalities.

[0105] 3. Endoscopic Ultrasound (EUS):

[0106] • Combines endoscopy and ultrasound to create detailed images of the pancreas from inside the digestive tract.

[0107] • EUS is highly effective for detecting small tumors and can be used to guide fine- needle aspiration (FNA) for biopsy.4. Positron Emission Tomography (PET) Scan:

[0108] • Often combined with CT (PET / CT), it uses a radioactive tracer to detect cancerous cells based on their increased glucose metabolism.

[0109] • Useful for assessing the spread (metastasis) of cancer and evaluating treatment response.

[0110] 5. Ultrasound (Abdominal Ultrasound):

[0111] • A non-invasive test that can sometimes detect pancreatic tumors or cysts but is less detailed than CT or MRI.

[0112] • Often used as an initial screening tool if a patient presents with nonspecific abdominal symptoms.

[0113] 6. Endoscopic Retrograde Cholangiopancreatography (ERCP):

[0114] • Primarily used to diagnose and treat blockages in the bile or pancreatic ducts. • A contrast dye is injected, and X-rays are taken to visualize the ducts. ERCP can also be used to take tissue samples or place stents to relieve blockages.

[0115] 7. X-rays:

[0116] • Typically, not detailed enough to diagnose PC but may be used in conjunction with other imaging to assess complications, such as bowel obstruction.

[0117] 8. Laparoscopy:

[0118] • A minimally invasive surgical procedure that allows direct visualization of the pancreas and surrounding areas.

[0119] • Used for staging the cancer to check for metastases not detected by non-invasive imaging.

[0120] Each imaging technique has specific benefits and is chosen based on the clinical context, such as initial diagnosis, staging, or treatment planning.

[0121] For the purpose of the present invention the following terms are defined:

[0122] • “CpG site”: The CpG sites refers to dinucleotides formed by a cytosine followed by a guanine. Cytosines in CpG dinucleotide can be methylated to form 5-methylcytosines.

[0123] • “CpG island” (CGI): CpG islands (or CG islands) are regions with a high frequency of CpG sites. The usual formal definition is a region with at least 200 bp and a GC percentage greater than 50%.

[0124] • “Beta-value”: The beta-value provides a quantitative measure of methylation at a specific locus. It is calculated as the ratio of methylated signal intensity to the totalsignal intensity (M / M+U) at a specific cytosine site. Its value ranges from 0 (unmethylated) to 1 (fully methylated).

[0125] • “Bisulfite treatment”: Treatment of DNA with sodium bisulfite converts unmethylated cytosine to uracil, which is subsequently converted to thymine during PCR amplification, while methylcytosine remains unchanged, as cytosine.

[0126] • “Promoter region”: The promoter region of the gene comprises the sequences of the 1500 bp upstream of the transcription start site (TSS), 5’UTR and the first exon of the gene.

[0127] • As used herein, the term "methylation" will be understood to mean the presence of a methyl group added by the action of a DNA methyl transferase enzyme to a cytosine base followed by a guanine (CpG) nucleotide of a nucleic acid.

[0128] • Accordingly, the term, “methylation status” as used herein refers to the presence or absence of methylation in a specific nucleic acid region.

[0129] • The expression “higher level of methylation” refers to an increase in the relative amount of methylation of a nucleic acid, as compared with the subject used as control that, in this case, are healthy subjects or subjects not suffering from PC. Thus, in the present disclosure, the “higher level of methylation” is generally determined with reference to a baseline level represented by the methylation status of a given genomic region in a sample obtained from control subjects. For example, “higher level of methylation” may be at least 2% greater than the baseline level of methylation, for example at least 5% greater than the baseline level of methylation, or at least 10% greater than the baseline level of methylation, or at least 15% greater than the baseline level of methylation, or at least 20% greater than the baseline level of methylation, or at least 25% greater than the baseline level of methylation, or at least 30% greater than the baseline level of methylation, or at least 40% greater than the baseline level of methylation, or at least 50% greater than the baseline level of methylation, or at least 60% greater than the baseline level of methylation, or at least 70% greater than the baseline level of methylation, or at least 80% greater than the baseline level of methylation, or at least 90% greater than the baseline level of methylation.

[0130] • The term "comprising" means "including", but not limited to what follows the term "comprising". Thus, the use of the term "comprising" indicates that the elements listed are necessary or mandatory, but that other elements are optional and may or may not be present.• The term "consisting of means "including" but is limited to what follows the term "consisting of. Thus, the term "consists of indicates that the elements listed are mandatory, and that other elements may not be present.

[0131] • By “pancreatic cancer” it is understood any stage of this disease, including precancerous conditions of the pancreas, comprising changes to pancreas cells that make them more likely to develop into cancer. These conditions are not yet cancer but it there are not treated there is a chance that these abnormal changes may become pancreatic cancer.

[0132] • The expression “liquid biopsy” refers to any sample of biologic fluids that may contain tumor-derived material. Particularly, a liquid biopsy is any sample of biologic fluids (for example: plasma, serum, blood, saliva, cerebrospinal fluid or urine, cyst fluid, pancreatic juice or duodenal juice) that may contain tumor-derived material, such as circulating tumor DNA.

[0133] • The expression “minimally invasive biological sample” refers to any sample which is taken from the body of the patient without the need of using harmful instruments, other than fine needles used for taking the blood from the patient, and consequently without being harmfully for the patient. Specifically, minimally invasive biological sample refers in the present invention to blood, serum, or plasma samples.

[0134] • The “pre-established reference value” is determined from methylation measurements obtained in a control population, namely healthy subjects and / or subjects not suffering from pancreatic cancer, thereby providing a baseline distribution of methylation levels for the corresponding genomic region(s) interrogated by the assays (e.g., promoter CpG sites within the amplicons defined for the biomarkers). The reference value may be set as a statistical parameter derived from said control distribution (e.g., mean, median, or percentile), and / or as an optimal cut-off determined by Receiver Operating Characteristic (ROC) curve analysis using Youden’s index, as described for the calculation of sensitivity and specificity at optimal cut-off values. In representative embodiments described herein for cfDNA analysis by droplet digital PCR (ddPCR), the methylation percentage for biomarkers assessed methylation assay and normalized to a reference gene (R), wherein albumin (ALB) is used as the reference gene, and the methylation percentage is calculated as (M / R) x 100, where M represents copies / pL of methylated RIC3 and R represents copies / pL of the unmethylated ALB reference gene in cfDNA. A deviation of the methylation status of one or more biomarkers orbiomarker combinations from said pre-established reference value is indicative that the biomarker signature may be used for diagnosis, prognosis and / or monitoring, and / or is indicative that the subject suffers from pancreatic cancer (or a precancerous stage thereof) or has a poor prognosis.

[0135] Brief description of the figures

[0136] Figure 1. Identification of methylation biomarkers in pancreatic cancer. (A) Workflow for the discovery of methylation biomarkers in primary PC tumors. (B) Venn diagram showing the overlap between hypermethylated and downregulated biomarkers in primary PC tumors. Figure 2. Promoter hypermethylation o AJ API, PRKCB, and WBSCR17 in tissue samples of primary pancreatic cancer. (A) DNA methylation levels at the promoters of AJAP1, PRKCB, and WBSCR17 in primary pancreatic tumors (T) compared to adjacent non-tumoral pancreatic tissues (N). (B) Receiver Operating Characteristic (ROC) curves showing the diagnostic performance of promoter hypermethylation of AJAP1, PRKCB, and WBSCR17 for detecting pancreatic cancer. AUC, Area under the ROC curve.

[0137] Figure 3. Validation of methylation status and diagnostic potential of AJAP1, PRKCB, and WBSCR17 in cfDNA. (A) DNA methylation levels ofAJAPl, PRKCB, and WBSCR17 in cfDNA from 44 PC patients compared to 41 healthy controls. (B) Receiver Operating Characteristic (ROC) curves showing the diagnostic performance of promoter hypermethylation of AJAP1, PRKCB, and WBSCR17 in cfDNA for detecting pancreatic cancer. PC, pancreatic cancer; Ctrl, controls; AUC, Area under the ROC curve.

[0138] Figure 4. Diagnostic performance of combining the promoter methylation of AJAP1, PRKCB, and WBSCR17 for PC detection in tissue samples. ROC curves show the AUC for detecting PC in tissue samples using the combined promoter methylation of AJAP1, PRKCB, and WBSCR17 in a cohort from TCGA (176 primary tumor tissue samples and 10 adjacent healthy tissue samples from adenocarcinoma PC patients). AUC, Area under the ROC curve.

[0139] Figure 5. Combined promoter methylation analysis of AJAP1, PRKCB, and WBSCR17 in cfDNA for detecting pancreatic cancer. The methylation data of AJAP1, PRKCB, and WBSCR17 were combined and analyzed using a random forest algorithm in a cohort of 44 PCpatients and 41 healthy controls. Receiver Operating Characteristic (ROC) curves shows the diagnostic performance of this combination. AUC, Area under the ROC curve.

[0140] Figure 6. Promoter methylation of AJAP1, PRKCB, and WBSCR17 in cfDNA for monitoring treatment response in metastatic pancreatic cancer. Promoter methylation levels of A.JAP1, PRKCB, and WBSCR17 in cfDNA, analyzed by ddPCR at different time points during the disease course in a subgroup of metastatic PC patients. GEM, gemcitabine; ABRAX, abraxane; No CT, computed tomography scan not performed; B, baseline; PD, progressive disease; PR, partial response.

[0141] Figure 7. Combined promoter methylation of AJAP1, PRKCB, and WBSCR17 in cfDNA for monitoring treatment response in metastatic pancreatic cancer. Promoter methylation levels oiAJAPl, PRKCB, and WBSCR17 in cfDNA, were analyzed by ddPCR at different time points during the disease course in a subgroup of metastatic PC patients. For each longitudinal time point, the methylation average of the combined biomarkers is represented. GEM, gemcitabine; ABRAX, abraxane; No CT, computed tomography scan not performed; B, baseline; PD, progressive disease; PR, partial response.

[0142] Figure 8. Prognostic value of promoter methylation of AJAP1 in cfDNA of PC patients.

[0143] Kaplan-Meir survival curves of PC patients stratified by promoter methylation status oiAJAPl in cfDNA. Patients with methylation levels above the median were classified as methylated (M), while those below the median were classified as unmethylated (U). The significance of the Kaplan-Meier curve was evaluated by log-rank test.

[0144] Figure 9. Promoter hypermethylation of IRF4 and RIC3 in tissue samples of primary pancreatic cancer. (A) DNA methylation levels at the promoters oiIRF4 a.vARIC3 in primary pancreatic tumors (T) compared to adjacent non-tumoral pancreatic tissues (N). (B) Receiver Operating Characteristic (ROC) curves showing the diagnostic performance of promoter hypermethylation of IRF4 and RIC3 for detecting pancreatic cancer. AUC, Area under de ROC curve.

[0145] Figure 10. Validation of methylation status and diagnostic potential of IRF4 and RIC3 in cfDNA. (A) DNA methylation levels of IRF4 and RJC3 in cfDNA from 44 PC patients compared to 41 healthy controls. (B) Receiver Operating Characteristic (ROC) curves showing the diagnostic performance of promoter hypermethylation of IRF4 and RIC3 in cfDNA for detecting pancreatic cancer. PC, pancreatic cancer; Ctrl, controls; AUC, Area under de ROC curve.Figure 11. Promoter methylation of IRF4 and RIC3 in cfDNA for monitoring treatment response in metastatic pancreatic cancer. Promoter methylation levels of IRF4 an RICB in cfDNA, analyzed by ddPCR at different time points during the disease course in a subgroup of metastatic PC patients. No CT, computed tomography scan not performed; B, baseline; PD, progressive disease; PR, partial response.

[0146] Figure 12. Combined promoter methylation of AJAP1, PRKCB, WBSCR17, IRF4 and RIC3 in cfDNA for monitoring treatment response in metastatic pancreatic cancer.

[0147] Promoter methylation levels of AJAP1, PRKCB, WBSCR17, IRF4 and RIC3 in cfDNA, were analyzed by ddPCR at different time points during the disease course in a subgroup of metastatic PC patients. For each longitudinal time point, the methylation average of the combined biomarkers is represented. No CT, computed tomography scan not performed; B, baseline; PD, progressive disease; PR, partial response.

[0148] Detailed description of the invention

[0149] The present invention is illustrated by means of the Examples set below without the intention of limiting its scope of protection.

[0150] FIRST STUDY

[0151] Example 1. Material and methods

[0152] Example 1.1. Genome-wide DNA methylation and transcriptomic analysis in pancreatic primary tumors from the retrospective cohort

[0153] Data from genome-wide DNA methylation analysis were sourced from The Cancer Genome Atlas (TCGA) database. IDAT files from the Infmium Human Methylation 450K BeadChip (450K) analysis from 176 primary tumor tissue samples and 10 adjacent healthy tissue samples from adenocarcinoma PC patients were downloaded using the TCGAbiolinks R package. To obtain DNA methylation data, the IDAT files underwent preprocessing, normalization, and quality control using the minfi and limma R packages.

[0154] To determine the functional relevance of the differentially methylated genes identified in PC, we utilized normalized RNA-sequencing (RNA-seq) expression data from the same cohort of patients, which were also obtained via the TCGAbiolinks R package. Differentially expressed genes were identified using generalized linear models within the package. Criteria fordifferential expression were set at an FDR < 0.05 and |log2 fold change (FC)| >1. Genes differentially expressed between PC and normal tissue samples from TCGA were correlated with the differentially methylated genes using Venn diagrams, created with the VennDiagram R package.

[0155] Example 1.2. Study participants from the prospective cohort

[0156] In the prospective cohort of this study (Table 3), 44 patients with metastatic PC (mPC) and 41 healthy individuals (controls) were included. Inclusion criteria for the study were to have a recent diagnosis of metastatic or locally advanced pancreatic adenocarcinoma, age over 18 years, not have received any prior treatment with antineoplastic agents in the context of advanced disease. Participants were recruited between 2021 and 2023 at the Medical Oncology Department of the University Clinical Hospital of Santiago de Compostela (CHUS) and the Reina Sofia University Hospital of Cordoba (HURS) in Spain. The study received approval from the Galician Ethical Committee and was conducted following Good Clinical Practice guidelines and the Declaration of Helsinki. All participants provided written informed consent. Patient responses to treatment were monitored using CT scans during follow-up and established according to RECIST 1.1 criteria.

[0157] Table 3. Clinical characteristics of patients.

[0158]

[0159]

[0160] Mean ± SD 66 ± 10 55 ± 12

[0161] Stage

[0162]

[0163] Adenocarcinoma 44

[0164] SD: Standard deviation

[0165] Example 1.3. Blood sample collection and plasma isolation

[0166] Serial blood samples were collected in 10 mL Cell-Free DNA BCT collection tubes (Streck, USA) at several time-points: i) just before starting the antineoplastic treatment established by the oncologist, ii) at 4 weeks, iii) at 12 and 24 weeks of treatment (coinciding with evaluationof response by CT scans), iv) as well as at the time of tumor progression. Blood samples were processed within the next 2 hours after collection. Plasma was isolated by an initial centrifugation at 1,600 x g for 10 min at 4°C, followed by a second centrifugation at 16,000 x g for 10 min at 4°C. Isolated plasma was stored at -80°C until use.

[0167] Example 1.4. Extraction and quantification of cfDNA

[0168] CfDNA was isolated from 2 mL of plasma using the QIAamp® Circulating Nucleic Acid Kit (Qiagen, Germany) and the QIAvac 24 Plus vacuum system (Qiagen, Germany) following the manufacturer's instructions. CfDNA was eluted in 50 pL of the kit's elution buffer. Concentrations were measured using a Qubit 4.0 Fluorometer (Thermo Fisher Scientific, USA) with the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, USA) before storing the samples at -20°C.

[0169] Example 1.5. Bisulfite conversion of cfDNA

[0170] Bisulfite conversion of 10-20 ng of cfDNA was carried out using the EZ DNA Methylation-Lightning Kit (Zymo Research, USA) following the manufacturer’s recommendations. The bisulfite treatment was conducted using a ProFlex PCR System (Thermo Fisher Scientific, USA) with the following cycling conditions: initial denaturation at 98°C for 8 minutes, followed by incubation at 54°C for 60 minutes and a final hold step of 4°C. The treated cfDNA was then eluted in 10 pL of elution buffer and stored at -20°C.

[0171] Example 1.6. Methylation analysis of cfDNA by ddPCR

[0172] The methylation status of A.JAP1, PRKCB, and WBSCR17 promoters in cfDNA was analyzed using on a QX200 system (Bio-Rad, USA). Primers for each gene were designed using Methyl Primer Express Software vl.O (Thermo Fisher Scientific, USA) to detect both methylated (M) and unmethylated (U) sequences (Table 4). Probes were manually designed to distinguish between methylated and unmethylated CpGs and verified with OligoEvaluator (Sigma-Aldrich, USA) to prevent duplex formation and secondary structures.

[0173] A preamplification reaction was performed initially. For this preamplification, 2 ng of bisulfite-treated cfDNA were mixed with 6.25 pL SsoAdvance PreAmp Supermix (Bio-Rad, USA), 1.25 pL of a primer mix (AJAP1, PBKCB and WBSCR17), and 3 pL of nuclease-free water, in a final volume of 12.5 pL. PCR conditions were set as follows: 3 minutes at 95°C, followed by 12 cycles of 95°C for 15 seconds and 58°C for 4 minutes, with a final hold at 4°C. The reaction volume was then diluted 1 :2 with nuclease-free water.Next, a multiplex reaction mix was prepared by combining 2 pL of the preamplified product with I I pL ddPCR Supermix for Probes (No dUTP) (Bio-Rad, USA), 1.1 pL of a primer-probe mix for each gene (at a ratio of 0.400 / 0.250 pM, respectively), and 7.9 pL of nuclease-free water, in a final volume of 22 pL. Droplets were generated using the QX200™ Droplet Generator (Bio-Rad, USA). The thermocycling conditions were: 10 minutes at 95°C, followed by 45 cycles of 94°C for 15 seconds and 54-57°C for 30 seconds, with a final step of 98°C for 10 minutes and a hold at 4°C. The temperature ramp increment was 2.5°C / s for all steps. Droplets were counted and analyzed using the QX200™ Droplet Reader (Bio-Rad, USA), and data acquisition was performed with QuantaSoft software (Bio-Rad, USA). Water served as a no-template control, while human methylated and non-methylated DNA (Zymo Research, USA) were used as positive controls for methylation and unmethylation, respectively. Reactions were conducted in duplicate. cfDNA methylation was calculated using the formula: Methylation (%) = [M / (U + M)] x 100, where M represents copies / pL of methylated cfDNA and U represents copies / pL of unmethylated cfDNA.

[0174] Example 1.7. Statistical analysis

[0175] To assess methylation differences between groups in TCGA data, hierarchical linear models were employed using RnBeads 2.0. P-values were adjusted for multiple testing with the Benjamini -Hochberg method, setting a threshold of p < 0.05 for statistical significance. For comparing methylation data obtained from ddPCR, the Kolmogorov-Smirnov test was initially used to evaluate the normality of the data distribution. Depending on the results, either the Mann-Whitney U test or the Student's t-test was subsequently applied. A p-value < 0.05 was considered statistically significant.

[0176] To evaluate diagnostic accuracy, a receiver operating characteristic (ROC) curve was generated considering the mean methylation levels of the DMCpGs obtained between PC and non-tumor tissues. A Random Forest algorithm was used to determine the diagnostic potential of combined gene data, utilizing the R packages caret, randomF orest, and pROC. The utility of biomarkers as prognostic factors was evaluated by Kaplan-Meier curves. Statistical analyses and graphical representations were conducted using GraphPad Prism 8.0 (GraphPad Software, USA) and R statistical software (v4.2.0).Table 4. Characteristics of primers and probes for cfDNA methylation analysis.

[0177] "

[0178]

[0179]

[0180] IChromosomal position, according to hg!9, of the DNA fragment amplified and detected. 2 Chromosomal position of the CpGs detected, according to hgl 9 , for each primerand probe. 3cgl3495205. 4cg03306374. 5cg01366419. 6cg03044249.

[0181] Abbreviations: M, methylated; U, unmethylated; F, Forward primer; R, Reverse primer; P, Probe.

[0182] Example 2. Results

[0183] Example 2.1. Identification of Methylation Biomarkers in primary tumors of PC

[0184] To identify DMCpGs sites between PC and non-tumoral tissues, we first analysed the methylation data sourced from the TCGA database, encompassing 176 primary PC tissue samples and 10 adjacent non-tumoral pancreatic tissue samples (controls). By employing stringent criteria (FDR < 0.05), we identified 158,226 CpG sites with significant differential methylation between PC and non-tumoral tissues (controls) (Figure 1A). Since the hypermethylaton of CpG islands and shores in promoters of tumor suppressor genes is a relevant feature of cancer cells, among the 158,226 DMCpGs, we selected CpG sites only located within CpG islands or shore regions of promoters. This filtering process reduced the number of DMCpG sites to 64,760, corresponding to 13,269 genes (Figure 1A). Next, to identify genes that were both hypermethylated and downregulated in PC, we performed an integrative analysis with methylation and gene expression data from the TCGA cohort previously described. Thus, we identified 224 gene candidates that were both hypermethylated and downregulated in PC (Figure IB). To further refine the list of gene candidate, we selected the genes with higher methylation differences between PC and controls (Delta > 0.2) across consecutive CpG sites. This step ensures that the selected genes exhibit consistent and substantial methylation changes across multiple CpG sites, thereby enhancing their reliability as potential biomarkers. This filtering process allowed the selection of 27 candidate genes. Among the potential 27 candidates, we finally selected the 3 genes (AJAP1, PRKCB, and WBSCR17) with highest differences in methylation levels between PC and non-tumor tissues (Figure 2A) and highest AUCs in ROC curve analyses for the detection of PC (Figure 2B). Example 2.2. Validation and diagnostic potential of the promoter methylation of AJAP1, PRKCB and WBSCR17 for the non-invasive detection of PC

[0185] To validate the methylation status of the 3 candidate genes (A.JAPR PRKCB, and WBSCR17) in cfDNA and to evaluate their diagnostic potential in PC, we selected a cohort of 41 healthy individuals (controls) and 44 PC patients. Importantly, the cfDNA methylation analysis in this cohort confirmed the results previously obtained in primary tumors of PC, showing significantly higher methylation levels in PC than in controls (Figure 3A), as well as significantAUCs for the detection of PC (AUC ; = 0.721, AUC ':r« = 0.670, K\JCWBSCRI7 = 0.683) (Figure 3B).

[0186] Example 2.3. Diagnostic utility of combined promoter methylation of AJAP1, PRKCB, and WBSCR17 for detecting PC.

[0187] DNA methylation analysis in tissue samples

[0188] To explore whether combining the methylation profiles of the previously analyzed genes (AJAP1, PRKCB, WBSCR17) in DNA from tissue samples could enhance their individual diagnostic accuracy for PC detection, we performed a random forest algorithm on the TCGA cohort: 176 primary tumor tissue samples and 10 adjacent healthy tissue samples from adenocarcinoma PC patients. This analysis revealed that the combination of at least two of these genes resulted in higher AUC values (AUCs=1.0) for PC detection (Figure 4) compared to each gene alone (Figure 2B). Specifically, the diagnostic accuracy was improved by the methylation status of the following combination of genes: AJAP1 + PRKCB,' AJAP1 + WBSCR17,- PRKCB + WBSCR17,' or AJ. API + PRKCB + WBSCR17.

[0189] CfDNA methylation analysis in plasma samples

[0190] We also assessed in plasma cfDNA whether the diagnostic accuracy of the three individual genes previously analyzed (AJAP1, PRKCB, WBSCR17) could be enhanced by combining the methylation status of at least two of these genes. Using a random forest algorithm on our cohort of plasma samples (41 controls vs. 44 PC cases), we found that combining at least two genes yielded higher AUCs for PC detection (Figure 5) compared to the AUCs of individual genes (Figure 3B). These findings indicate that the detection of PC is improved by the methylation status of the following combination of genes: AJAP1 + PRKCB,' AJAP1 + WBSCR17,' PRKCB + WBSCR17,' or AJAP1 + PRKCB + WBSCR17. Notably, the simultaneous combination of the three genes (AJA Pl + PRKCB + WBSCR17) produced the highest diagnostic performance (AUC of 0.943), with high sensitivity (89%) and specificity (100%).

[0191] Example 2.4. Clinical utility of the promoter hypermethylation of AJAP1, PRKCB and WBSCR17 for the non-invasive monitoring of PC

[0192] To evaluate the clinical utility oiAJAPl, PRKCB, and WBSCR17 for non-invasive monitoring of treatment response in metastatic PC patients, we analyzed the methylation status of each of these 3 genes in cfDNA by ddPCR at different time points of the disease in a subgroup of patients from our previous cohort, whose disease evolution was evaluated according to thestandard clinical practice using CT scans. Of note, the cfDNA methylation of A.JAP1, PRKCB and WBSCR17 increased (Figure 6, Patient 1) with the progression of the disease and decreased in response to effective therapy (Figure 6, Patient 2), being this decrease faster than the current clinical biomarker CAI 9-9. These results represent a proof of concept of the utility of evaluating the methylation of these 3 genes as potential non-invasive biomarkers to monitor the disease during the follow-up of metastatic PC patients.

[0193] Example 2.5. Clinical utility of combined promoter methylation of AJAP1, PRKCB and WBSCR17 for the non-invasive monitoring of PC

[0194] To evaluate the clinical utility of combining the methylation status of A.JAP1, PRKCB, and WBSCR17 for non-invasive monitoring of treatment response in metastatic PC patients, we analyzed the methylation status of these 3 genes in cfDNA by ddPCR at different time points of the disease in two patients of our cohort of plasma samples. Thus, for each longitudinal time point, the methylation average of the combined biomarkers was calculated. The evolution of disease was evaluated according to standard clinical practice using CT scans. Of note, the combined methylation status of at least two of the three genes analyzed in cfDNA (AJAP1 + PRKCB,- AJAP1 + WBSCR17,- PRKCB + WBSCR17,- or AJAP1 + PRKCB + WBSCR17) increased with the progression of the disease (Figure 7, Patient 1) and decreased in response to effective therapy (Figure 7, Patient 2). This reduction occurred faster than the decrease observed in CAI 9-9 serum levels, which is the standard biomarker used in the clinic to monitor the evolution of the PC disease. These results confirm the utility of combining the methylation status of at least two of these 3 genes as non-invasive biomarkers to monitor the disease during the follow-up of PC patients.

[0195] Example 2.6. Utility of promoter methylation of AJA Pl. PRKCB and WBSCR17 in cfDNA as a prognostic biomarker in PC

[0196] To evaluate the utility of AJAP1, PRKCB and WBSCR17 in cfDNA as prognostic factors of PC, we analyzed the individual (AJAP1, PRKCB, or WBSCR1 ) and combined (A. J API + PRKCB,- AJAP1 + WBSCR17,- PRKCB + WBSCR17,- or AJAP1 + PRKCB + WBSCR17) promoter methylation of these genes in the plasma cohort of PC patients (n=44) of our study. This analysis indicated that only the promoter methylation AJAP1 in cfDNA, was significantly associated with poorer survival outcomes in PC (Figure 8).

[0197] SECOND STUDY

[0198] Example 3. Material and methodsExample 3.1. Genome-wide DNA methylation and transcriptomic analysis in pancreatic primary tumors from the retrospective cohort

[0199] Please refer to Example 1.1.

[0200] Example 3.2. Study participants from the prospective cohort

[0201] The same prospective cohort as described in Example 1.2 was utilized for this substudy. Specifically, 44 patients with metastatic pancreatic cancer (mPC) and 41 healthy individuals (controls) were included. Inclusion criteria for the study were to have a recent diagnosis of metastatic or locally advanced pancreatic adenocarcinoma, age over 18 years, and to not have received any prior treatment with antineoplastic agents in the context of advanced disease. Participants were recruited between 2021 and 2023 at the Medical Oncology Department of the University Clinical Hospital of Santiago de Compostela (CHUS) and the Reina Sofia University Hospital of Cordoba (HURS) in Spain. The study received approval from the Galician Ethical Committee and was conducted following Good Clinical Practice guidelines and the Declaration of Helsinki. All participants provided written informed consent. Patient responses to treatment were monitored using CT scans during follow-up and established according to RECIST 1.1 criteria.

[0202] Example 3.3. Blood sample collection and plasma isolation

[0203] Please refer to Example 1.3.

[0204] Example 3.4. Extraction and quantification of cfDNA

[0205] Please refer to Example 1.4.

[0206] Example 3.5. Bisulfite conversion of cfDNA

[0207] Please refer to Example 1.5.

[0208] Example 3.6. Methylation analysis of cfDNA by ddPCR

[0209] The methylation status of IRF4 and RIC3 promoters in cfDNA was analyzed using a QX200 system (Bio-Rad, USA). Primers for IRF4 and RIC3 were designed using Methyl Primer Express Software vl.O (Thermo Fisher Scientific, USA). For IRF4, primers were designed to specifically amplify methylated (M) and unmethylated (U) sequences of the gene. In the case of RIC3, primers were designed to specifically assess its methylation levels, and albumin (ALB) gene was used as a reference gene (R) for normalization (Table 5).Table 5. Characteristics of primers and probes for cfDNA methylation analysis.

[0210]

[0211]

[0212] ‘Chromosomal position, according to hg!9, of the DNA fragment amplified and detected.2Chromosomal position of the CpGs detected, according to hgl 9, for each primer and probe.3cg06392169.2cg08383315.scg25778535. Abbreviations: M, methylated; U, unmethylated; F, Forward primer; R, Reverse primer; P, Probe.

[0213] Probes were manually designed to distinguish between methylated and unmethylated CpGs and verified with OligoEvaluator (Sigma- Aldrich, USA) to prevent duplex formation and secondary structures. A preamplification reaction was first performed by mixing 2 ng of bisulfite-treated cfDNA with 6.25 pL of SsoAdvance PreAmp Supermix (Bio-Rad, USA), 1.25 pL of a primer mix (IRF4, RIC3 and ALB), and 3 pE of nuclease-free water, resulting in a total reaction volume of 12.5 pL. The PCR conditions for preamplification were as follows: 3 minutes at 95°C, followed by 12 cycles of 95°C for 15 secondsand 58°C for 4 minutes, with a final hold at 4°C. The reaction was then diluted 1:2 with nuclease-free water.

[0214] For the multiplex ddPCR reaction, 2 pU of the preamplified product was combined with 1 1 pL ddPCR Supermix for Probes (No dUTP) (Bio-Rad, USA), 1.1 pL of a primer-probe mix (at a final concentration of 0.900 / 0.250 pM orIRF4 and 0.400-0.300 pM for RIC3). and 7.9 pL of nuclease-free water, totaling 22 pL. Droplets were generated using the QX200™ Droplet Generator (Bio-Rad, USA). The thermocycling conditions for ddPCR were as follows: 10 minutes at 95°C, followed by 45 cycles of 94°C for 15 seconds and 58°C for 30 seconds, with a final hold at 98°C for 10 minutes and storage at 4°C. The ramp rate was 2.5°C / s for all steps. Droplets were read and analyzed using the QX200™ Droplet Reader (Bio-Rad, USA), and data were processed with QuantaSoft software (Bio-Rad, USA). Water was used as a no-template control, and human methylated and unmethylated DNA (Zymo Research, USA) were included as positive controls for methylation and unmethylation, respectively. All reactions were performed in duplicate.

[0215] Methylation percentage for IRF4 was calculated using the formula: Methylation (%) = [M / (U + M)] x 100, where M represents the copies / pU of methylated IRF4, and U represents the copies / pU of unmethylated IRF4. Methylation percentage for RIC3 was calculated as: Methylation (%) = (M / R) x 100, where M represents the copies / pU of methylated RIC3, and R represents the copies / pU of the unmethylated reference gene ALB in cfDNA.

[0216] Example 3.7. Statistical analysis

[0217] To compare methylation data obtained from ddPCR between PC patients and healthy controls, the Kolmogorov-Smirnov test was initially used to evaluate the normality of data distribution. Depending on the results, either the Mann-Whitney U test or the Student's t-test was subsequently applied. A p-value < 0.05 was considered statistically significant.

[0218] To evaluate diagnostic accuracy, a receiver operating characteristic (ROC) curve was generated considering the mean methylation levels of IRF4 and RIC3 between PC patients and healthy controls. Area under the curve (AUC) values, sensitivity, and specificity were calculated at optimal cutoff values determined by Youden's index.

[0219] A Random Forest algorithm was used to determine the diagnostic potential of IRF4 and RIC3 in combination with each other and with the previously identified biomarkers (AJAP1, PRKCB and WBSCR17). Statistical analyses and graphical representations were conducted using GraphPad Prism 8.0 (GraphPad Software, USA) and R statistical software v4.2.0, utilizing the R packages caret, randomForest, and pROC.Example 4. Results

[0220] Example 4.1. Analysis with the genes IRF4 and RIC3

[0221] To further expand the signature of 3 genes previously obtained (AJAP1, PRKCB and WBSCR17) for PC detection, we conducted an exploratory analysis with the next two genes (IRF4 m RIC3) with highest differences in methylation levels (Figure 9A) and highest AUCs (Figure 9B) between PC and non-tumor tissues.

[0222] Example 4.2. Validation and diagnostic potential of the promoter methylation of IRF4 and RIC3 for the non-invasive detection of PC

[0223] To validate the methylation status of these 2 candidate genes (IRF4 and RIGS') in cfDNA and to evaluate their diagnostic potential in PC, we used the same cohort previously described in Example 2.2. Importantly, the cfDNA methylation analysis in this cohort confirmed the results previously obtained in primary tumors of PC, showing significantly higher methylation levels in PC than in controls (Figure 10A), as well as significant (p < 0.05) AUCs for the detection of PC (A\JCIRF4 = 0.696, KUCRIC3 = 0.674) (Figure 10B).

[0224] Example 4.3. Diagnostic utility of combined promoter methylation of AJAP1, PRKCB, WBSCR17, IRF4 and RIC3 for detecting PC.

[0225] DNA methylation analysis in tissue samples

[0226] To explore whether combining the methylation profiles of the previously analyzed genes (AJAP1, PRKCB, WBSCR17, IRF4 mA RIGS) in DNA from tissue samples could enhance their individual diagnostic accuracy for PC detection, we performed a random forest algorithm on the TCGA cohort: 176 primary tumor tissue samples and 10 adjacent healthy tissue samples from adenocarcinoma PC patients. This analysis revealed that the combination of at least two of these genes resulted in higher AUC values (AUCs=1.0) for PC detection (Figure 4 and Table 6) compared to each gene alone (Figure 2B and 9B).

[0227] Table 6. Diagnostic performance across the different gene combinations including R1C3 or IRF4 to detect PC in tissue samples from the TCGA cohort.

[0228]

[0229]

[0230] CfDNA methylation analysis in plasma samples

[0231] We also assessed in plasma cfDNA whether the diagnostic accuracy of the five individual genes previously analyzed (AJAP1, PRKCB, WBSCR17, IRF4, RIC3) could be enhanced by combining the methylation status of at least two of these genes. Using a random forest algorithm on our cohort of plasma samples (41 controls vs. 44 PC cases), we found that combining at least two genes yielded higher AUCs for PC detection (Figure 5 and Table 7) compared to the AUCs of individual genes (Figure 3B and Figure 10B).Table 7. Diagnostic performance across the different gene combinations including RIC3 or IRF4 to detect PC in cfDNA from the plasma samples cohort.

[0232]

[0233] Notably, the simultaneous combination of the three genes AJAP1 + WBSCR17 + IRF4 produced a high diagnostic performance (AUC of 0.997), with high sensitivity (95%) and specificity (100%).Example 4.4. Clinical utility of the promoter hypermethylation of IRF4 and RIC3 for the non-invasive monitoring of PC

[0234] To evaluate the clinical utility of IRF4 and RIC3 for non-invasive monitoring of treatment response in metastatic PC patients, we analyzed the methylation status of each of these 2 genes in cfDNA by ddPCR at different time points of the disease in a subgroup of patients from our previous cohort, whose disease evolution was evaluated according to the standard clinical practice using CT scans. Of note, the cfDNA methylation o IRF4 and RIC3 increased (Figure 11, Patient 1) with the progression of the disease and decreased in response to effective therapy (Figure 11, Patient 2), being this decrease faster than the current clinical biomarker CAI 9-9. These results represent proof of concept of the utility of evaluating the methylation of these 2 genes as potential non-invasive biomarkers to monitor the disease during the follow-up of metastatic PC patients.

[0235] Example 4.5. Clinical utility of combined promoter methylation of AJAP1, PRKCB, WBSCR17, IRF4 and RIC3 for the non-invasive monitoring of PC

[0236] To evaluate the clinical utility of combining the methylation status of AJAP1, PRKCB, WBSCR17, IRF4 and RIC3 for non-invasive monitoring of treatment response in metastatic PC patients, we analyzed the methylation status of these 5 genes in cfDNA by ddPCR at different time points of the disease in two patients of our cohort of plasma samples. Thus, for each longitudinal time point, the methylation average of the combined biomarkers was calculated. The evolution of disease was evaluated according to standard clinical practice using CT scans. Of note, the combined methylation status of at least two of the five genes analyzed in cfDNA (AJA Pl + / RIA AJAP1 + RIC3: PRKCB + IRF4; PRKCB + RIC3: WBSCR17 + IRF4- WBSCR17 + R / C3; IRF4 + R / C3; AJAP1 + PRKCB + IRF4: AJAP1 + PRKCB + RIC3- AJAP1 + WBSCRI7 + IRF4- AJAP1 + WBSCR17 + RIC3: AJAP1 + IRF4 + RIC3: PRKCB + WBSCR17 + IRF4- PRKCB + WBSCR17 + R / C3; PRKCB + IRF4 + R / C3; WBSCR17 + IRF4 + R / C3; AJAP1 + PRKCB + WBSCR17 + IRF4' AJAP1 + PRKCB + WBSCR17 + RIC3' AJAP1 + PRKCB + IRF4 + RIC3- AJAP1 + WBSCR17 + IRF4 + RIC3' PRKCB + WBSCR17 + IRF4 + RIC3' AJAP1 + PRKCB + WBSCR17 + IRF4 + RIC3) increased with the progression of the disease (Figure 12, Patient 1) and decreased in response to effective therapy (Figure 12, Patient 2). This reduction occurred faster than the decrease observed in CAI 9-9 serum levels, which is the standard biomarker used in the clinic to monitor the evolution of the PC disease. These results confirm the utility of combining the methylation status of at least two of these 5 genes as non-invasive biomarkers to monitor the disease during the follow-up of PC patients.

Claims

1. CLAIMS1. In vitro method for identifying biomarker signatures for the diagnosis and / or prognosis of pancreatic cancer, or a precancerous stage thereof; and / or monitoring treatment response of patients suffering from pancreatic cancer, or a precancerous stage thereof, wherein the method comprises assessing the methylation status of at least a gene selected from the list consisting of: IRF4 and / or BJC3, in a biological sample obtained from a subject.

2. In vitro use of a gene selected from the list consisting of: IRF4 and / or BJC3, or of a kit comprising reagents for the determining the methylation status the genes, for identifying biomarker signatures for the diagnosis and / or prognosis of pancreatic cancer, or a precancerous stage thereof; and / or monitoring treatment response of patients suffering from pancreatic cancer, or a precancerous stage thereof.

3. In vitro method, or in vitro use, according to any of the claims 1 or 2, wherein the method comprises assessing the methylation status of at least the two genes IRF4 and BJC3, in a biological sample obtained from a subject.

4. In vitro method, or in vitro use, according to any of the claims 1 to 3, which further comprises assessing the methylation status of at least a gene selected from the group consisting of: AJAPL PRKCB and / or WBSCR17, in a biological sample obtained from a subject.

5. In vitro method for the diagnosis and / or prognosis of pancreatic cancer, or a precancerous stage thereof; and / or monitoring treatment response of patients suffering from pancreatic cancer, or a precancerous stage thereof, wherein the method comprises assessing the methylation status of at least a gene selected from the list consisting of: IRF4 and / or BIC3, in a biological sample obtained from a subject.

6. In vitro use of a gene selected from the list consisting of: IRF4 and / or RIC3, or of a kit comprising reagents for the determining the methylation status the genes, for the diagnosis and / or prognosis of pancreatic cancer, or a precancerous stage thereof; and / or monitoring treatment response of patients suffering from pancreatic cancer, or a precancerous stage thereof.

7. In vitro, or in vitro use, according to any of the claims 5 or 6, wherein the method comprises assessing the methylation status of at least the two genes IRF4 and RJC3, in a biological sample obtained from a subject.

8. In vitro method, or in vitro use, according to any of the claims 5 to 7, which further comprises assessing the methylation status of at least a gene selected from the group consisting of: AJAP1, PRKCB and / or WBSCR17, in a biological sample obtained from a subject.

9. In vitro method, or in vitro use, according to any of the claims 2 to 8, which comprises assessing the methylation status of at least one of the following gene combinations: AJAP 1+WBSCR17+IRF4+RIC3,' AJAP 1+PRKCB+WBSCR17+IRI4+RIC3,- AJAP 1+PRKCB+WBSCR17+IRF4,- AJAP1+WBSCR17+IRF4,- AJAP1+WBSCR17+RIC3- AJAPI+PRKCB+WBSCRI 7+RIC3; PRKCB+WBSCR17+IRF4; PRKCB+WBSCRI7+IRF4+RIC3- WBSCR17+IRF4+RIC3- WBSCR17+IRF4- PRKCB+WBSCR17+RIC3,- WBSCR17+RIC3,- AJAP 1+PRKCB+IRF4,- AJAP1+PRKCB+IRF4+RIC3,- AJAP 1+IRI4+RIC3,- AJAP 1+IRF4,- AJAP1+PRKCB+RIC3,- AJAP 1+RIC3,- PRKCB+IRF4+RIC3,- IRF4+RIC3,- PRKCB+IRF4, - PRKCB+RIC3.

10. In vitro method, or in vitro use, according to any of the claims 2 to 9, wherein if a deviation of the methylation status is identified, as compared with a pre-established reference value, this is indicative that the biomarker signature may be used for the diagnosis, prognosis or monitoring pancreatic cancer, or a precancerous stage thereof, or is indicative that the subject is suffering from pancreatic cancer, or a precancerous stage thereof, or has a poor prognosis.

11. In vitro method, or in vitro use, according to any of the claims 2 to 10, wherein the methylation status of the genes is determined in at least a CpG site of the gene.

12. In vitro method, or in vitro use, according to any of the claims 2 to 11, wherein the methylation status of the genes is determined in at least a CpG site of the promoter region.

13. In vitro method, or in vitro use, according to any of the claims 2 to 10, wherein the methylation status of the gene IRF4 is determined in at least the CpG site cg06392169, wherein the methylation status of the gene RJC3 is determined in at least a CpG site selected from cg08383315 or cg25778535, wherein the methylationstatus of the gene AJAP1 is determined in at least the CpG site cgl3495205, wherein the methylation status of the gene PRKCB is determined in at least the CpG site cg03306374 and / or wherein the methylation status of the gene WBSCR17 is determined in at least a CpG site selected from cg03044249 or cg01366419.

14. In vitro method, or in vitro use, according to any of the claims 2 to 13, wherein the biological sample is selected from: a liquid biopsy selected from plasma, serum, blood, saliva, cerebrospinal fluid or urine, cyst fluid, pancreatic juice or duodenal juice; or a tissue sample.

15. Kit, suitable for the diagnosis and / or prognosis of pancreatic cancer, or a precancerous stage thereof, and / or for monitoring treatment response in patients suffering from pancreatic cancer, or a precancerous stage thereof, which comprises: a pair of primers and a probe for the amplification and detection of at least a fragment of the gene IRF4 and / or a pair of primers and a probe for the amplification and detection of at least a fragment of the gene RIC3, wherein:a) the forward primer for the amplification of at least a fragment of the gene IRF4 is selected from SEQ ID NO: 19 or SEQ ID NO: 22;b) the reverse primer for the amplification of at least a fragment of the gene IRF4 is selected from SEQ ID NO: 20 or SEQ ID NO: 23;c) the probe for the detection of the methylation or unmethylation of the gene IRF4 is selected from SEQ ID NO: 21 or SEQ ID NO: 24;d) the forward primer for the amplification of at least a fragment of the gene RJC3 is SEQ ID NO: 25;e) the reverse primer for the amplification of at least a fragment of the gene BIC3 is SEQ ID NO: 26; andf) the probe for the detection of the methylation of the gene RIC3 is SEQ ID NO: 27.

16. Kit, according to claim 15, which further comprises a pair of primers and a probe for the amplification and detection of at least a fragment of the gene AJAP1 and / or a pair of primers and a probe for the amplification and detection of at least a fragment of the gene WBSCR17, wherein:a) The forward primer for the amplification of at least a fragment of gene AJAP1 is selected from SEQ ID NO: 1 or SEQ ID NO: 4;b) The reverse primer for the amplification of at least a fragment of gene AJAP1 is selected from SEQ ID NO: 2 or SEQ ID NO: 5;c) The probe for the detection of the methylation of the gene AJAP1 is selected from SEQ ID NO: 3 or SEQ ID NO: 6;d) The forward primer for the amplification of at least a fragment of gene WBSCR17 is selected from SEQ ID NO: 13 or SEQ ID NO: 16;e) The reverse primer for the amplification of at least a fragment of gene WBSCR17 is selected from SEQ ID NO: 14 or SEQ ID NO: 17;f) The probe for the detection of the methylation of the gene WBSCR17 is selected from SEQ ID NO: 15 or SEQ ID NO: 18.

17. Kit, according to claim 16, which further comprises a pair of primers and a probe for the amplification and detection of gene PRKCB, wherein:a) The forward primer for the amplification of at least a fragment of gene PRKCB is selected from SEQ ID NO: 7 or SEQ ID NO: 10;b) The reverse primer for the amplification of at least a fragment of gene PRKCB is selected from SEQ ID NO: 8 or SEQ ID NO: 11;c) The probe for the detection of the methylation of the gene PRKCB is selected from SEQ ID NO: 9 or SEQ ID NO: 12.

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