[go: up one dir, main page]

WO2024047441A1 - Method for monitoring tumor burden in subjects during therapeutic intervention - Google Patents

Method for monitoring tumor burden in subjects during therapeutic intervention Download PDF

Info

Publication number
WO2024047441A1
WO2024047441A1 PCT/IB2023/058146 IB2023058146W WO2024047441A1 WO 2024047441 A1 WO2024047441 A1 WO 2024047441A1 IB 2023058146 W IB2023058146 W IB 2023058146W WO 2024047441 A1 WO2024047441 A1 WO 2024047441A1
Authority
WO
WIPO (PCT)
Prior art keywords
igg
evs
patients
pdac
plasma
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/IB2023/058146
Other languages
French (fr)
Inventor
Bruno COSTA DA SILVA
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Fundacao D Anna De Sommer Champalimaud E Dr Carlos Montez Champalimaud Centro De Investigacao Da Fundacao Champalimaud
Original Assignee
Fundacao D Anna De Sommer Champalimaud E Dr Carlos Montez Champalimaud Centro De Investigacao Da Fundacao Champalimaud
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Fundacao D Anna De Sommer Champalimaud E Dr Carlos Montez Champalimaud Centro De Investigacao Da Fundacao Champalimaud filed Critical Fundacao D Anna De Sommer Champalimaud E Dr Carlos Montez Champalimaud Centro De Investigacao Da Fundacao Champalimaud
Publication of WO2024047441A1 publication Critical patent/WO2024047441A1/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
    • G01N33/57488—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites involving compounds identifable in body fluids
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G01N33/57438—Specifically defined cancers of liver, pancreas or kidney
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6854—Immunoglobulins
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis

Definitions

  • the present invention belongs to the field of protein biomarkers for diagnosis and discloses a method for monitoring tumor burden in subjects during therapeutic intervention, in order to assess their response to the treatment regimen.
  • the tumor burden can be defined as the tumor load in the body, i.e., the number of cancer cells, the size of a tumor, or the amount of cancer. Since it has long been considered a major prognostic indicator in oncological practice, the improved performance to track changes in tumor burden would be important for assessing whether a patient is responding to the therapy regimen or not.
  • Pancreatic ductal adenocarcinoma was the seventh cause of cancer-related fatalities globally in 2020 1 .
  • SEER End Results Program
  • the incidence of PDAC has increased over the past seven years 3 , being projected to be the second biggest cause of cancer-related deaths by 2030 4 .
  • the majority of metastatic patients continue to have median survival outcomes of less than one year 5, 6 , with less than 30 % of patients eligible for second line chemotherapy 7 .
  • Imaging techniques based on the Response Evaluation Criteria in Solid Tumors (RECIST) criteria 8 which evaluate the number and size of tumor lesions over the course of treatment, remain gold standards for determining treatment response during chemotherapy.
  • RECIST Response Evaluation Criteria in Solid Tumors
  • these techniques have several drawbacks, including a lack of precision in detecting small tumors and an inability to differentiate between benign inflammatory and malignant lesions. This also includes the necessity to arbitrarily target lesions to evaluate the evolution of the disease in accordance with the treatment, and the usual delay between the imagiological response and the real-time detection of differences in tumor dimensions 9 .
  • the evaluation solely based on dimensions doesn’t consider changes in tumor attenuation, nor does it discriminate viable cells from non-viable ones, thus complicating the measurement of treatment response 10 .
  • CA19.9 Carbohydrate antigen 19.9
  • Serological markers like Carbohydrate antigen 19.9 are widely utilized to supplement the ability of RECIST to assess PDAC treatment response 11 .
  • CA19.9 When elevated at the start of treatment, CA19.9 demonstrates dependable outcomes in conjunction with imaging evaluation 12 .
  • the CA19.9 evaluation in PDAC patients has limitations since it is not expressed in 5-20 % of patients 12, 13 .
  • false positives are common, especially in patients with obstructive lesions of the biliary tract, which may affect up to 70 % of PDAC patients 14 .
  • EVs are nanovesicles that contain all active biomolecules, including nucleic acids, lipids, and proteins 19 .
  • EVs are critical mediators of cell-to-cell communication under physiological and pathological settings 20 .
  • Studies using EVs as cancer biomarkers usually focus on specific disease timepoints, disregarding potential changes in the molecular composition and population dynamics of EVs during the course of the disease, and investigating the utility of EVs exclusively for prognosis or diagnosis 21 .
  • studies of EV biomarkers for follow up of metastatic PDAC patients have been limited by challenges involving their dismal prognosis, which complicates patient recruitment for longer longitudinal studies.
  • IgG + plasma EVs obtained from metastatic PDAC patients who were monitored throughout their treatment and evolution of the disease were characterized.
  • a population of plasma EVs bound to IgG was identified, which is connected with the diagnosis of PDAC and the treatment response of PDAC patients (including those without CA19.9 expression).
  • IgG + plasma EVs may represent a new tool for enhancing the efficacy of chemotherapeutic treatments in patients with PDAC by improving detection of therapy response and resistance.
  • Document EP2542696B1 discloses a method for detecting cancer in a subject in need thereof, comprising (a) identifying a biosignature of a population of vesicles isolated from a body fluid of the subject; and (b) comparing the biosignature to a value of reference.
  • the biosignature comprises the presence or level of a particular protein (which varies according to the type of cancer to be analyzed) and its identification makes it possible to verify the presence of the disease or the effectiveness of the treatment used.
  • the vesicle population with a specific biosignature can be detected using a binding agent for the biomarker, such as immunoglobulin molecules or immunologically active portions of immunoglobulin molecules.
  • a binding agent for the biomarker such as immunoglobulin molecules or immunologically active portions of immunoglobulin molecules.
  • the method is performed in vitro, and the vesicles are tested directly from the sample without previous isolation, purification, or concentration.
  • document EP2176665B1 describes a method for the diagnosis and prognosis of pancreatic cancer, which makes it possible to monitor the progression of the disease and/or the therapeutic efficacy of an anticancer treatment, which comprises the steps of (a) collecting a biological sample obtained from a subject with cancer at a first time point; isolating microvesicles from this first sample and measuring an oncogenic microRNA in the microvesicles obtained from the first sample; and (b) collecting a second biological sample obtained from a subject with cancer at a second time point, the second time point occurring after the first time point, isolating microvesicles from this second sample, and measuring the oncogenic microRNA in the microvesicles obtained from the second sample.
  • the first time point occurs before the subject has received anti-cancer treatment and the subsequent time points occur after or while the subject has received said treatment.
  • An increase in the amount of an EV population carrying a specific protein measured from a sample compared to the proportion of EVs carrying the same specific protein obtained from previous sample collections indicates that the cancer has progressed or that the individual is unresponsive to treatment.
  • a decrease in the amount of an EV population carrying a specific protein measured from a sample compared to the proportion of EVs carrying the same specific protein obtained from previous sample collections indicates that the cancer has regressed or that the individual is responding to treatment.
  • document WO2022046576A1 relates to nucleic acid products as PDAC biomarkers and methods of detecting the same in a biological sample from a subject. Methods of diagnosis, prognosis and/or treatment of said disease are also described, wherein a characteristic in relation to the treatment of a patient, a patient's response to a treatment and/or their survival is determined or predicted based on the detection of one or more of the nucleic acid biomarkers. Instead of measuring the overall levels of cancer-related biomolecules, the present invention focuses in monitoring a specific population of EVs, identified by specific proteins, linked with cancer response to therapy.
  • biomarkers described in this invention correspond, in particular, to gene expression signatures and immune signatures of the tumor microenvironment. Quantitative results can be generated by quantifying the proportion of EVs carrying a specific protein of interest in relation to the total EV present in the studied biofluid.
  • document EP2718721A1 describes the use of specific biomolecules to isolate populations of EVs and then evaluate the bulk content of these isolates as a method to (i) identify the stage or progression of a disease, (ii) select candidate treatment regimens for the disease; and (iii) determine the effectiveness of the treatment.
  • the disease is cancer, such as pancreatic cancer.
  • Biomarkers are selected from nucleic acids, proteins and circulating structures such as vesicles and nucleic acid-protein complexes. Vesicles can be analyzed using one or more antibodies specific for one or more antigens present on the vesicle, where the antigens can be immunoglobulin molecules or immunologically active portions of immunoglobulin molecules.
  • the novelty and inventiveness of the present invention are in the fact that the sample obtained from the subject is processed for qualitative and quantitative determination of the population of vesicle-associated proteins, wherein the determination of the proportion of EVs that express IgG are a marker of response to therapy in patients with cancer, more specifically, PDAC.
  • the data processing consists of monitoring the levels (percentages) of EVs that express IgG throughout the treatment, in order to assess whether or not these subjects are responding to therapy (through reduction, stabilization, or increased plasma levels of IgG + EVs over time).
  • the object of the present invention is a method for monitoring tumor burden in subjects during therapeutic intervention, wherein the method comprises the steps of:
  • the percentage of EVs that express IgG is a marker of response to treatment in patients with cancer, wherein the levels of IgG + EVs decrease in case of response to treatment and increase in case of non-response to treatment.
  • PDAC pancreatic ductal adenocarcinoma
  • imaging evaluation cannot detect small lesions and is not an immediate indicator of the tumor's status, as there is a delay between the progression of the disease and its imagiological identification 8, 10 .
  • IgG + EVs plasma extracellular vesicles
  • EVs have the potential to carry biomarkers for liquid biopsies, in both oncologic and non-oncologic diseases 39 , due to their abundance in body fluids. EVs produced by tumor cells can populate and alter the composition of biofluids. As a result, the identification of molecular modifications in EVs associated with tumor profile has been utilized to identify putative cancer biomarkers 19, 40 , including the diagnosis of PDAC 41 . The majority of these studies, however, have focused on the differential expression of specific nucleic acids and proteins in bulk EV samples for diagnosis or prognosis, using single collections from each patient 42 . Also, failure to distinguish between EV populations may obscure real differences between experimental groups. In cancer patients, longitudinal studies like ours evaluating the response to chemotherapy in a single individual are still scarce 43, 44 .
  • the present invention discloses a method for monitoring pancreatic ductal adenocarcinoma (PDAC) in subjects during therapeutic intervention, in order to assess their response to the treatment regimen.
  • PDAC pancreatic ductal adenocarcinoma
  • IgG attaches to the surface of EVs in PDAC patients via an interaction with the tumor antigen MAGE B1, and this process is independent of IgG plasma levels and the inflammatory status of the patient.
  • IgG + EVs can detect therapy response in a subset of individuals with PDAC who lack the standard PDAC marker CA19.9.
  • emerging markers of therapy response in PDAC should assist in separating PDAC patients into new groups, hence aiding the development of tailored, more effective treatments.
  • NTA Nanoparticle Tracking Analysis
  • A samples isolated from healthy controls
  • B PDAC patients at diagnosis that did not respond
  • C responded
  • D is a panel depicting the presence status of the indicated proteins in isolated EVs
  • E is representative Western blots of EV-markers CD9, CD81, Alix and non-EV markers GM130 and Calnexin for EV samples isolated from healthy control and PDAC plasma (H1975 cell lysate was used as a control).
  • FIG. 1 shows (A) Volcano plot representing the identified proteins in MS (comparison between patients (PDAC) and healthy control samples) and (B) Volcano plot representing the identified proteins in mass spectrometry (comparison between patients that are nonresponders and responders to chemotherapy).
  • the green points represent proteins significantly regulated after correction for multiple testing.
  • the blue points represent proteins significantly regulated without correction for multiple testing.
  • Black points represent proteins with insignificant regulation.
  • E ROC curve for the IgG + EV to discriminate response in patients with metastatic PDAC. Data was obtained from patients 15, 23, 34, 49, 63, 69, 70, 73, and 94 (D); and patients 8, 10, 15, 21, 22, 23, 34, 49, 66, and 73 (E).
  • NLR neutrophil/lymphocyte ratio
  • the object of the present invention is a method for monitoring tumor burden in subjects during therapeutic intervention, in order to assess their response to therapy.
  • the method comprises the steps of:
  • step (a) the blood sample is collected from a liquid biopsy of the tumor.
  • a blood sample was collected every time the patient came to the Clinical Centre for a follow-up visit (usually every 1 to 2 months).
  • the clinical data is registered in a database stored in a computing device.
  • the clinical data is at least one of the groups consisting of magnetic resonance imaging (MRI) / Computer Tomography (CT) or conventional biomarkers of the disease to be tested.
  • MRI magnetic resonance imaging
  • CT Computer Tomography
  • biomarker is CA19.9.
  • step (c) in order to prepare the blood sample for the analysis of the protein content of plasma EVs, such blood sample is centrifuged twice in a temperature range from 4 to 10 oC and a centrifugal force in the range from 500 g to 3000 g for a time range from 10 to 20 minutes.
  • the plasma-derived EV samples obtained may be aliquoted and stored in a temperature range from -80 to 4 oC.
  • step (c) the plasma-derived EV sample is analyzed by Nanoparticle Tracking Analysis (NTA), in order to determine particle concentration and size distribution, and Vesicle Flow Cytometry, in order to measure the proportion of EVs bound to IgG.
  • NTA Nanoparticle Tracking Analysis
  • Vsicle Flow Cytometry in order to measure the proportion of EVs bound to IgG.
  • the plasma-derived EV samples are previously diluted in filtered phosphate-buffered saline (PBS) to achieve a concentration within the range for optimal NTA analysis.
  • PBS filtered phosphate-buffered saline
  • the plasma-derived EV samples are submitted to two incubation substeps to stain EVs prior to the Vesicle Flow Cytometry analysis.
  • the EVs are stained with anti-IgG in PBS and incubated for a time range from 1 to 3 h in a temperature range from 20 to 37 °C.
  • the antibody-stained sample is then incubated with an EV specific fluorescent dye for a time range from 1 to 2 h in a temperature range from 20 to 37 °C.
  • the EV specific fluorescent dye is Carboxyfluorescein Diacetate Succinimidyl Ester (CFSE).
  • SEC Size Exclusion Chromatography
  • the method of the invention allows the analysis of EVs directly in plasma, without the need of a centrifugation step of the plasma samples for the isolation of EVs.
  • step (d) the treatment of the data is performed by a central processing unit and consists of monitoring the levels (percentages) of EVs that express IgG throughout the treatment of the patients, in order to assess whether or not these patients are responding to the therapy (through reduction, stabilization, or increased plasma levels of IgG + EVs over time).
  • the method of the present invention allows investigating the percentage of EVs that express IgG as a marker of response to therapy, wherein the levels of IgG + EVs decrease in case of response to treatment and increase in case of non-response to treatment.
  • Treatment proposals were done at the multidisciplinary tumor board. Patients were treated in a sequence of chemotherapy with the following approved regimens: association of 5-fluorouracil, irinotecan, and oxaliplatin (FOLFIRINOX); association of gemcitabine and nab-paclitaxel; association of 5-fluorouracil and liposomal irinotecan; or single gemcitabine.
  • the decision on the choice of the chemotherapy regimen was at the discretion of the treating oncologist. Demographic and clinical information from the patients, including neutrophil-to-lymphocyte ratio and C-Reactive Protein levels, was collected. Treatment response was classified based on the imaging response based on RECIST v1.1 criteria 8 .
  • Timepoints were selected as follows: timepoint I (pretreatment) was collected before the beginning of a new chemotherapy regimen, and timepoint II (posttreatment) at the time of either the best imaging response (if tumor responded with chemotherapy) or the worst (if not responding to chemotherapy).
  • timepoint II posttreatment
  • the same patient can be a responder at one point of treatment and a nonresponder at another.
  • the first samples of one patient that met these criteria were selected until the calculated minimum number of total required samples was obtained.
  • Plasma samples from patients and healthy donors were collected in a 9 mL Vacuette NV EDTA K3 tube and centrifuged twice at 10 degrees Celsius (500 g for 10 minutes and 3 000 g for 20 minutes). Prior to analysis, plasma samples were aliquoted and stored at -80oC. A protocol previously described involving sequential ultracentrifugation combined with sucrose cushion was used to purify EVs 23 .
  • EV samples were analyzed by Nanoparticle Tracking Analysis (NTA) using a NanoSight NS300 equipped with red laser (638 nm) to determine particle concentration and size distribution (Malvern Panalytical, United Kingdom). Samples were pre-diluted in filtered PBS to achieve a concentration within the range for optimal NTA analysis. Video acquisitions were performed at 25oC using a camera level of 16, and a threshold between 4 and 6. Five videos of 30 s with 10-50 particles per frame were captured per sample. The total protein content of EV samples was determined using the PierceTM BCA Protein Assay Kit (Thermo Fisher Scientific).
  • plasma-derived EV samples from PDAC patients were used. Four patients that responded and four patients that displayed disease progression were selected. For each patient, samples were collected at diagnosis and after treatment response evaluation, totaling sixteen samples. In parallel, five plasma-derived EV samples from healthy controls were also compared to eight PDAC patients at diagnosis.
  • the EV solution containing sodium dodecyl sulfate (SDS) and dithiothreitol (DTT) was loaded onto filtering columns and washed exhaustively with 8 M urea in HEPES buffer 24 . Proteins were reduced with DTT and alkylated with IAA. Protein digestion was performed by overnight digestion with trypsin sequencing grade (Promega).
  • Mass spectra were acquired in positive ion mode applying automatic data-dependent switch between one Orbitrap survey MS scan in the mass range of 350–1200 m/z followed by higher-energy collision dissociation (HCD) fragmentation and Orbitrap detection of fragment ions with a cycle time of 2 s between each master scan.
  • MS and MSMS settings maximum injection times were set to “Auto”, normalized collision energy was 30%, ion selection threshold for MSMS analysis was 10,000 counts, and dynamic exclusion of sequenced ions was set to 30 s.
  • TBS-T TBS with 0.1% Tween-20
  • secondary antibodies for 1 h at RT. Incubation was followed by three additional washes with TBS-T, 5 min each. Blots were imaged using the Odyssey Infrared Imaging System (LI-COR Biosciences). The detailed list of primary and secondary antibodies used is provided in Table 1 above.
  • Table 1 List of primary and secondary antibodies used for Western blotting Primary Antibodies Antibody Manufacturer Catalog no Dilution/concentration CD81 Santa Cruz Biotechnology sc-166029 1:50 CD9 Cell Signaling #13174 1:1000 Alix Sigma Aldrich SAB4200476-200UL 1.25 ug/mL Calnexin Abcam ab22595 1:2000 GM130 Abcam ab52649 1:1000 Secondary Antibodies Antibody Manufacturer Catalog no Dilution Goat anti-rabbit IgG IRDye 800CW LI-COR Biosciences 926-32211 1:5000 Goat anti-mouse IgG IRDye 800CW LI-COR Biosciences 926-32210 1:5000
  • Flow cytometry analysis of plasma EVs was performed as described by our group 29 .
  • a volume of plasma containing 2 ⁇ 10 9 particles was used for staining with 0.5 ⁇ L of anti-IgG in PBS, in a final volume of 40 ⁇ L, and incubated for 1 h at 37°C.
  • the antibody-stained sample was then incubated with Carboxyfluorescein Diacetate Succinimidyl Ester (CFSE – Thermo Fisher Scientific LTI C34554, MA, United States) to a final concentration of 25.6 ⁇ M, for 90 min at 37°C.
  • SEC Size Exclusion Chromatography
  • EV-enriched fractions #7, #8, and #9 were then pooled (total of 1500 ⁇ L) and retrieved for analysis with the flow cytometer Apogee A60-Micro-Plus (Apogee Flow Systems, United Kingdom) configured as described in Table 2. For all subsequent analyses, quadrant thresholds were established with unstained and single-stained extracellular vesicles (with CFSE or with anti-IgG) ( ).
  • Table 2 Cytometer configuration and laser power Channel number Short Channel Name Full Channel Name Optical Filter Name Laser Wavelength Laser Power PMT Voltage Ch1 405-SALS Small Angle Light Scatter 405 nm 200 mW 400 V Ch2 405-LALS Large Angle Light Scatter 405 nm 200 mW 400 V Ch3 405-Gm Green Fluorescence BP-525/50 405 nm 200 mW 500 V Ch4 405-Org Orange Fluorescence LWP-590/35 405 nm 200 mW 500 V Ch5 APC Red Fluorescence BP-676/36 638 nm 150 mW 550 V Ch6 CFSE Green Fluorescence BP-525/50 488 nm 200 mW 525 V Ch7 PE Orange Fluorescence BP-575/30 488 nm 200 mW 500 V Ch8 488-Red Red Fluorescence BP-676/36 488 nm 200 mW 500 V Ch9 488-DRed Deep Red Fluorescence
  • biotinylated surface proteins of EVs were collected using streptavidin magnetic beads 30 (DynabeadsTM MyOneTM Streptavidin C, Invitrogen, 65001), and then detached from the beads (deionized water, 70oC) 31 .
  • streptavidin magnetic beads 30 DynabeadsTM MyOneTM Streptavidin C, Invitrogen, 65001
  • surface EV proteins that were associated with IgG were co-immunoprecipitated using magnetic beads (DynabeadsTM Protein G for Immunoprecipitation, Invitrogen, 10003D) conjugated with anti-human IgG antibody (Goat anti-Human IgG F(ab')2 Secondary Antibody, Invitrogen, 31122), then eluted and analyzed by MS.
  • Sample size was based on previous liquid biopsy studies 32 .
  • the analysis involves 155 observations from 30 different situations (15 responders x 15 nonresponders). Experiments were not randomized. The researchers were blinded to allocation during experiments and outcome assessment. The response evaluation to the treatment was previously done by a different researcher. Error bars in graphical data represent means ⁇ standard errors of the means (SEM). Normality and homogeneity of variances from the analyzed variables were tested with Shapiro-Wilk test and Bartlett or Levene tests, respectively. If data were parametric, Student’s t test (two populations) were used. If data were not parametric, Wilcoxon or Mann-Whitney tests were performed.
  • the size distribution and concentration of plasma EVs isolated from patients and healthy controls were characterized. Proteins frequently present or absent in small EVs were measured in our samples ( ). MS analysis of plasma EV samples from five healthy controls and sixteen samples from eight PDAC patients was performed, both at the time of diagnosis and after treatment. Four of these eight patients were considered chemotherapy responders, as tumor shrinkage was observed between the diagnosis and treatment timepoints. In contrast, based on the observed imaging progression of the disease between the two time points, the remaining four patients corresponded to nonresponders to chemotherapy. For the MS analysis, the same amounts of protein (20 ⁇ g) and concentrations (0.5 ⁇ g/ ⁇ L) were utilized.
  • Protein expression analysis revealed that 102 distinct proteins exhibited statistically significant differences between PDAC patients and healthy controls, 59 of which were upregulated in PDAC patients. Of these, the presence of multiple IgG fragments ( Figure 4A, Table 3) was identified. In fact, the functional analysis of proteins significantly upregulated or downregulated in EVs from PDAC patients (Responders and Nonresponders) and as compared to healthy controls revealed enrichment in proteins associated with humoral immune response and complement activation, among others ( ).
  • a new marker should: a) be consistent, independent of the treatment of choice; b) be able to identify differences in patients with and without CA19.9 expression; and c) be able to predict treatment response in comparison to the imagiological evaluation, in order to reinforce the maintenance of applied treatment or to suspend futile treatments early 33, 34 .
  • IgG + EVs may derive, at least in part, from alterations in the inflammatory status of immune cells during the progression of PDAC.
  • the neutrophil/lymphocyte ratio (NLR) is a clinical marker of inflammation calculated as the quotient of the absolute neutrophil and lymphocyte counts 36 .
  • NLR neutrophil/lymphocyte ratio
  • IgG fragments were detected in both healthy controls and PDAC patients.
  • soluble proteins alpha-2-macroglobulin
  • cell surface receptors i.e., Glutamine Receptor
  • cytoskeletal proteins i.e., Keratin
  • cytoplasmic proteins were detected in both groups (e.g. Chloride intracellular channel protein 4, Golgi integral membrane protein 4, Chondroitin sulfate synthase 3 and Protein Argonaute 2).
  • IgG was bound to Melanoma associated antigen B1 (MAGE B1) in the EVs of PDAC patients, in addition to IgG fragments and Albumin.
  • MAGE B1 Melanoma associated antigen B1
  • MAGE B1 is a well-known PDAC antigen 38 , suggesting that the population of IgG + EVs described here is the consequence of an interaction between tumoral antigens on the surface of EVs released by tumor cells and IgG in circulation (Table 5).
  • the analysis of EV bulks is an effective method for identifying molecules of interest (e.g., proteins, lipids, and RNA) in EV liquid biopsies.
  • MS analysis of EVs in bulk was used to identify IgG as a possible EV marker of therapeutic response in PDAC patients.
  • failure to distinguish between EV populations may obscure real differences between experimental groups.
  • the implementation of EV biomarkers in clinical practice is hindered by the laborious and time-consuming isolation and analysis protocols commonly employed for EVs.
  • a vesicle flow cytometry protocol was used 29 . By not requiring EV isolation prior to analysis, the processing time is reduced from >24 hours to 4 hours. Therefore, the use of vesicle flow cytometry has the potential to facilitate the clinical evaluation of IgG + EVs.
  • IgG + EV populations may be utilized in the follow-up of PDAC patients, including those who lack CA19.9 expression. Due to the absence of this established marker, these patients rely solely on imaging evaluations to determine their clinical response to chemotherapy; therefore, a new reliable marker would represent a substantial improvement in their care.
  • cytokines/chemokines 52 proteins that frequently display quantitative changes in cancer patients, such as cytokines/chemokines 52 , extracellular matrix proteins 53 , coagulation factors 54 , complement factors 55 , immunoglobulins 56 and albumin 57 , can interact with EVs after their release and change their composition 51 .
  • MAGE B1 was identified as an EV surface ligand of IgG in 8 different PDAC patients, further study, including methods other than MS, will be necessary to validate this finding in larger cohorts of PDAC patients.
  • immunoglobulins Igs were traditionally thought to be exclusively produced by B-lineage cells, recent studies have shown that these molecules can also be produced by a large diversity of tumor types 21 , including PDAC 35, 60, 61 .
  • MAGE B1 is a tumor antigen found in a variety of tumor types, including melanoma and tumors of epithelial origin, such as breast, colorectal carcinoma, lung, and pancreatic 38, 62-66 .
  • MAGE is identified as an antigen normally expressed by the placenta and male germ cells in cancerous testes. It is expressed in 47 percent of pancreatic tumors 67 , giving cells that express it a survival advantage 68 and negatively correlating with prognosis and patient survival 67, 69 .
  • MAGE B1 was identified as one of the proteins found exclusively in EVs from PDAC patients.
  • IgG + EV is independent of the availability of circulating IgG.
  • this may be the result of elevated levels of tumor EV secretion and/or enhanced packaging of tumor antigens (such as MAGE B1) in PDAC EVs.
  • tumor antigens such as MAGE B1
  • this process may result in tumor-directed IgG absorption by tumor EVs and, as a result, may contribute to tumor immune-escape 45 and the chronic inflammatory state observed in metastatic PDAC patients 70 .
  • the binding of IgGs to EVs could also affect the efficacy of targeted therapies (e.g., immunotherapies). Additional re-search will be required to fully comprehend the aforementioned implications.
  • pancreas 40 Li, X., R. Ni, J. Chen, Z. Liu, M. Xiao, F. Jiang and C. Lu. "The presence of ighg1 in human pancreatic carcinomas is associated with immune evasion mechanisms.” Pancreas 40 (2011): 753-61. 10.1097/MPA.0b013e318213d51b. https://www.ncbi.nlm.nih.gov/pubmed/21654544.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Immunology (AREA)
  • Engineering & Computer Science (AREA)
  • Urology & Nephrology (AREA)
  • Molecular Biology (AREA)
  • Chemical & Material Sciences (AREA)
  • Biomedical Technology (AREA)
  • Hematology (AREA)
  • Cell Biology (AREA)
  • Medicinal Chemistry (AREA)
  • Analytical Chemistry (AREA)
  • Biotechnology (AREA)
  • Pathology (AREA)
  • Food Science & Technology (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Microbiology (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Oncology (AREA)
  • Hospice & Palliative Care (AREA)
  • Gastroenterology & Hepatology (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Investigating Or Analysing Biological Materials (AREA)

Abstract

The present invention discloses a method for monitoring tumor burden in subjects during therapeutic intervention, in order to assess their response to the treatment regimen. More specifically, the method consists of performing a liquid biopsy based on extracellular vesicles (EVs) for the qualitative and quantitative determination of the population of vesicle-associated proteins. The percentage of EVs that express IgG is a marker of response to treatment, wherein the levels of IgG+EVs decrease in case of response and increase in case of non-response.

Description

METHOD FOR MONITORING TUMOR BURDEN IN SUBJECTS DURING THERAPEUTIC INTERVENTION
The present invention belongs to the field of protein biomarkers for diagnosis and discloses a method for monitoring tumor burden in subjects during therapeutic intervention, in order to assess their response to the treatment regimen.
The tumor burden can be defined as the tumor load in the body, i.e., the number of cancer cells, the size of a tumor, or the amount of cancer. Since it has long been considered a major prognostic indicator in oncological practice, the improved performance to track changes in tumor burden would be important for assessing whether a patient is responding to the therapy regimen or not.
Pancreatic ductal adenocarcinoma (PDAC) was the seventh cause of cancer-related fatalities globally in 20201. As reported by the Surveillance, Epidemiology, and End Results Program (SEER)2, the incidence of PDAC has increased over the past seven years3, being projected to be the second biggest cause of cancer-related deaths by 20304. The majority of metastatic patients continue to have median survival outcomes of less than one year5, 6, with less than 30 % of patients eligible for second line chemotherapy7.
Imaging techniques based on the Response Evaluation Criteria in Solid Tumors (RECIST) criteria8, which evaluate the number and size of tumor lesions over the course of treatment, remain gold standards for determining treatment response during chemotherapy. However, these techniques have several drawbacks, including a lack of precision in detecting small tumors and an inability to differentiate between benign inflammatory and malignant lesions. This also includes the necessity to arbitrarily target lesions to evaluate the evolution of the disease in accordance with the treatment, and the usual delay between the imagiological response and the real-time detection of differences in tumor dimensions9. In addition, the evaluation solely based on dimensions doesn’t consider changes in tumor attenuation, nor does it discriminate viable cells from non-viable ones, thus complicating the measurement of treatment response10.
Serological markers like Carbohydrate antigen 19.9 (CA19.9) are widely utilized to supplement the ability of RECIST to assess PDAC treatment response11. When elevated at the start of treatment, CA19.9 demonstrates dependable outcomes in conjunction with imaging evaluation12. However, the CA19.9 evaluation in PDAC patients has limitations since it is not expressed in 5-20 % of patients12, 13. In addition, false positives are common, especially in patients with obstructive lesions of the biliary tract, which may affect up to 70 % of PDAC patients14.
Due to the aforementioned constraints, it is of the utmost need to develop additional techniques for assessing the therapy response of PDAC. Studies with potential new response indicators, including circulating tumor cells and circulating tumor DNA, have previously been conducted to uncover new readouts15. This also includes recent findings that plasma extracellular vesicles (EVs) can detect incidence16, predict progression17, and locate18 metastases in PDAC patients. However, the majority of current research lacks longitudinal information on response to therapy and clinical follow-up.
EVs are nanovesicles that contain all active biomolecules, including nucleic acids, lipids, and proteins19. EVs are critical mediators of cell-to-cell communication under physiological and pathological settings20. Studies using EVs as cancer biomarkers usually focus on specific disease timepoints, disregarding potential changes in the molecular composition and population dynamics of EVs during the course of the disease, and investigating the utility of EVs exclusively for prognosis or diagnosis21. Besides, studies of EV biomarkers for follow up of metastatic PDAC patients have been limited by challenges involving their dismal prognosis, which complicates patient recruitment for longer longitudinal studies.
In the present invention, EVs obtained from metastatic PDAC patients who were monitored throughout their treatment and evolution of the disease were characterized. A population of plasma EVs bound to IgG (IgG+ EVs) was identified, which is connected with the diagnosis of PDAC and the treatment response of PDAC patients (including those without CA19.9 expression). Thus, a longitudinal examination of IgG+ plasma EVs may represent a new tool for enhancing the efficacy of chemotherapeutic treatments in patients with PDAC by improving detection of therapy response and resistance.
Some documents of the state of the art mention the expression of proteins in EVs as a potential marker of cancer diseases. Differently from the present invention, which identifies a protein biomarker to monitor a population of EVs linked with response to therapy in patients already diagnosed with cancer, Document EP2542696B1 discloses a method for detecting cancer in a subject in need thereof, comprising (a) identifying a biosignature of a population of vesicles isolated from a body fluid of the subject; and (b) comparing the biosignature to a value of reference.
The biosignature comprises the presence or level of a particular protein (which varies according to the type of cancer to be analyzed) and its identification makes it possible to verify the presence of the disease or the effectiveness of the treatment used.
The vesicle population with a specific biosignature can be detected using a binding agent for the biomarker, such as immunoglobulin molecules or immunologically active portions of immunoglobulin molecules. The method is performed in vitro, and the vesicles are tested directly from the sample without previous isolation, purification, or concentration.
Differently from the present invention, which utilizes a protein biomarker to identify an EV population linked with cancer response to therapy, document EP2176665B1 describes a method for the diagnosis and prognosis of pancreatic cancer, which makes it possible to monitor the progression of the disease and/or the therapeutic efficacy of an anticancer treatment, which comprises the steps of (a) collecting a biological sample obtained from a subject with cancer at a first time point; isolating microvesicles from this first sample and measuring an oncogenic microRNA in the microvesicles obtained from the first sample; and (b) collecting a second biological sample obtained from a subject with cancer at a second time point, the second time point occurring after the first time point, isolating microvesicles from this second sample, and measuring the oncogenic microRNA in the microvesicles obtained from the second sample.
The first time point occurs before the subject has received anti-cancer treatment and the subsequent time points occur after or while the subject has received said treatment. An increase in the amount of an EV population carrying a specific protein measured from a sample compared to the proportion of EVs carrying the same specific protein obtained from previous sample collections indicates that the cancer has progressed or that the individual is unresponsive to treatment. Likewise, a decrease in the amount of an EV population carrying a specific protein measured from a sample compared to the proportion of EVs carrying the same specific protein obtained from previous sample collections indicates that the cancer has regressed or that the individual is responding to treatment.
Differently from the present invention, which utilizes a protein biomarker to identify an EV population linked with cancer response to therapy, document WO2022046576A1 relates to nucleic acid products as PDAC biomarkers and methods of detecting the same in a biological sample from a subject. Methods of diagnosis, prognosis and/or treatment of said disease are also described, wherein a characteristic in relation to the treatment of a patient, a patient's response to a treatment and/or their survival is determined or predicted based on the detection of one or more of the nucleic acid biomarkers. Instead of measuring the overall levels of cancer-related biomolecules, the present invention focuses in monitoring a specific population of EVs, identified by specific proteins, linked with cancer response to therapy.
The biomarkers described in this invention correspond, in particular, to gene expression signatures and immune signatures of the tumor microenvironment. Quantitative results can be generated by quantifying the proportion of EVs carrying a specific protein of interest in relation to the total EV present in the studied biofluid.
Differently from the present invention, which utilizes a protein biomarker to monitor the proportion of an EV population linked with cancer response to therapy in biofluids, document EP2718721A1 describes the use of specific biomolecules to isolate populations of EVs and then evaluate the bulk content of these isolates as a method to (i) identify the stage or progression of a disease, (ii) select candidate treatment regimens for the disease; and (iii) determine the effectiveness of the treatment. Preferably, the disease is cancer, such as pancreatic cancer.
Biomarkers are selected from nucleic acids, proteins and circulating structures such as vesicles and nucleic acid-protein complexes. Vesicles can be analyzed using one or more antibodies specific for one or more antigens present on the vesicle, where the antigens can be immunoglobulin molecules or immunologically active portions of immunoglobulin molecules.
Although these documents mention the expression of proteins in EVs as a potential marker of cancer, the novelty and inventiveness of the present invention are in the fact that the sample obtained from the subject is processed for qualitative and quantitative determination of the population of vesicle-associated proteins, wherein the determination of the proportion of EVs that express IgG are a marker of response to therapy in patients with cancer, more specifically, PDAC.
The data processing consists of monitoring the levels (percentages) of EVs that express IgG throughout the treatment, in order to assess whether or not these subjects are responding to therapy (through reduction, stabilization, or increased plasma levels of IgG+EVs over time).
The object of the present invention is a method for monitoring tumor burden in subjects during therapeutic intervention, wherein the method comprises the steps of:
  1. collection of a blood sample from the subject;
  2. recording of the clinical data;
  3. centrifuging the blood sample in a centrifuge and processing the plasma sample for extracellular vesicle (EV) staining followed by qualitative and quantitative determination of the population of vesicle-associated proteins; and
  4. data processing to assess the subject’s response to therapy.
The percentage of EVs that express IgG is a marker of response to treatment in patients with cancer, wherein the levels of IgG+EVs decrease in case of response to treatment and increase in case of non-response to treatment.
Currently, the assessment of treatment response in patients with metastatic pancreatic ductal adenocarcinoma (PDAC) relies mainly on CA19.9 and imaging evaluations, both of which have limitations. 5 - 20 % of PDAC patients lack CA19.9, and false-positive elevations are associated with biliary tree inflammation or infections12, 13.
On the other hand, imaging evaluation cannot detect small lesions and is not an immediate indicator of the tumor's status, as there is a delay between the progression of the disease and its imagiological identification8, 10. By characterizing a population of plasma extracellular vesicles (IgG+ EVs) that correlates with the treatment response status of metastatic PDAC patients, a potentially new tool to complement treatment response evaluation is described.
EVs have the potential to carry biomarkers for liquid biopsies, in both oncologic and non-oncologic diseases39, due to their abundance in body fluids. EVs produced by tumor cells can populate and alter the composition of biofluids. As a result, the identification of molecular modifications in EVs associated with tumor profile has been utilized to identify putative cancer biomarkers19, 40, including the diagnosis of PDAC41. The majority of these studies, however, have focused on the differential expression of specific nucleic acids and proteins in bulk EV samples for diagnosis or prognosis, using single collections from each patient42. Also, failure to distinguish between EV populations may obscure real differences between experimental groups. In cancer patients, longitudinal studies like ours evaluating the response to chemotherapy in a single individual are still scarce43, 44.
The present invention discloses a method for monitoring pancreatic ductal adenocarcinoma (PDAC) in subjects during therapeutic intervention, in order to assess their response to the treatment regimen.
IgG attaches to the surface of EVs in PDAC patients via an interaction with the tumor antigen MAGE B1, and this process is independent of IgG plasma levels and the inflammatory status of the patient.
A longitudinal population analysis of plasma EVs revealed that EVs bound to IgG increase during disease progression and decrease when patients react to chemotherapy. Importantly, IgG+ EVs can detect therapy response in a subset of individuals with PDAC who lack the standard PDAC marker CA19.9.
These findings not only have the potential to expand the current monitoring options for PDAC metastasis, but also represent a promising new tool for identifying effective treatments and indicating alternate treatments in the event of disease progression.
In addition, emerging markers of therapy response in PDAC, such as IgG+ EVs, should assist in separating PDAC patients into new groups, hence aiding the development of tailored, more effective treatments.
In order to promote an understanding of the principles according to the modalities of the present invention, reference will be made to the embodiments illustrated in the figures and the language used to describe them.
It should also be understood that there is no intention to limit the scope of the invention to the content of the figures and that modifications to the inventive features illustrated herein, as well as additional applications of the principles and embodiments illustrated, which would normally occur to a person skilled in the art having the possession of this description, are considered within the scope of the claimed invention.
Fig.1
depicts the metastatic profile, treatment protocols, and sample collections from patients analyzed in the present invention. PDAC patient (PC) number, Age, Gender (M, F) and metastatic location(s) are provided.
Fig.2
shows (A) Internal controls: representative plots of unstained plasma, plasma stained with CFSE, plasma stained with anti-IgG (FAB), and plasma stained with both CFSE and anti-IgG; the indicated counts correspond to the events within the upper right quadrant (CFSE+IgG+) and (B) Characterization of EVs in plasma: representative plots of particles labeled with CFSE and anti-IgG (FAB) from plasma; the right panel indicates the IgG+ (top right quadrant) and IgG- (lower right quadrant) events within CFSE+ particles.
Fig.3
shows representative size distribution histograms by Nanoparticle Tracking Analysis (NTA) of samples isolated from healthy controls (A), and from PDAC patients at diagnosis that did not respond (B) or that responded (C) to therapy. (D) is a panel depicting the presence status of the indicated proteins in isolated EVs and (E) is representative Western blots of EV-markers CD9, CD81, Alix and non-EV markers GM130 and Calnexin for EV samples isolated from healthy control and PDAC plasma (H1975 cell lysate was used as a control).
Fig.4
shows (A) Volcano plot representing the identified proteins in MS (comparison between patients (PDAC) and healthy control samples) and (B) Volcano plot representing the identified proteins in mass spectrometry (comparison between patients that are nonresponders and responders to chemotherapy). The green points represent proteins significantly regulated after correction for multiple testing. The blue points represent proteins significantly regulated without correction for multiple testing. Black points represent proteins with insignificant regulation.
Fig.5
shows the functional enrichment of significantly regulated EV proteins based on biological processes.
Fig.6
shows vesicle flow cytometry data of plasma samples from PDAC patients and healthy controls, as indicated, wherein (A) Comparison of IgG+ EVs between PDAC patients and healthy controls, P=0.037, by Wilcoxon test. (B, C) Longitudinal evaluation of plasma IgG+ EVs in patients followed during at least two lines of chemotherapy (CT - mFOLFIRINOX in 1st line, and Gemcitabine with Nabpaclitaxel in 2nd line, as indicated), that express ( patients 15, 23, 34 and 49) or not (patients 8 and 63) CA19.9. The moments of imagiological evaluation are indicated (R for response, S for stabilization and P for progression to chemotherapy). (C, D) Evolution of IgG+ EVs before and after treatment with chemotherapy in 15 situations of response (C) and no response (D) to treatment. P=0.039 (C) and P=0.037 (D), by t-test. (E) ROC curve for the IgG+ EV to discriminate response in patients with metastatic PDAC. Data was obtained from patients 15, 23, 34, 49, 63, 69, 70, 73, and 94 (D); and patients 8, 10, 15, 21, 22, 23, 34, 49, 66, and 73 (E).
Fig.7
shows the evolution of CA19.9 before and after treatment with chemotherapy in responders (A) and nonresponders (B). Depicted are the same patients and timepoints as in D and E. Wilcoxon test was used for statistical analysis. (C) ROC curve for the CA19.9 to discriminate response in patients with metastatic PDAC.
Fig.8
shows the Comparison between plasmatic levels of IgG before and after treatment with chemotherapy in responders, by Wilcoxon test (A) and nonresponders, by t-test (B). Depicted are the same patients and timepoints as in D and E. There were no significant differences in both groups. C. Correlation of the same time points of plasmatic IgG (mg/dL) vs percentage of IgG+ EVs. Statistical analysis was performed by simple linear regression.
Fig.9
shows the Evolution of the neutrophil/lymphocyte ratio (NLR) between timepoints pre and post treatment with chemotherapy in responders, by t-test (A) and nonresponders, by Wilcoxon test (B). Depicted are the same patients and timepoints as in D and E. There were no significant differences in both groups. Correlation of the same time points of IgG+ EVs vs NLR (C) and vs C-Reactive Protein (CRP) (D). Statistical analysis was performed by simple linear regression.
The object of the present invention is a method for monitoring tumor burden in subjects during therapeutic intervention, in order to assess their response to therapy.
More specifically, the method comprises the steps of:
  1. collection of a blood sample from the subject;
  2. recording of the clinical data;
  3. centrifuging the blood sample in a centrifuge and processing the plasma sample for extracellular vesicle (EV) staining followed by qualitative and quantitative determination of the population of vesicle-associated proteins; and
  4. data processing to assess the subject’s response to therapy.
In step (a), the blood sample is collected from a liquid biopsy of the tumor. In order to perform the monitoring of the disease, a blood sample was collected every time the patient came to the Clinical Centre for a follow-up visit (usually every 1 to 2 months).
In step (b), for each blood sample collection, the clinical data is registered in a database stored in a computing device. In a specific embodiment of the invention, the clinical data is at least one of the groups consisting of magnetic resonance imaging (MRI) / Computer Tomography (CT) or conventional biomarkers of the disease to be tested. In a preferred embodiment of the invention, the disease is pancreatic ductal adenocarcinoma (PDAC), and the biomarker is CA19.9.
In step (c), in order to prepare the blood sample for the analysis of the protein content of plasma EVs, such blood sample is centrifuged twice in a temperature range from 4 to 10 ºC and a centrifugal force in the range from 500 g to 3000 g for a time range from 10 to 20 minutes. Prior to analysis, the plasma-derived EV samples obtained may be aliquoted and stored in a temperature range from -80 to 4 ºC.
In step (c), the plasma-derived EV sample is analyzed by Nanoparticle Tracking Analysis (NTA), in order to determine particle concentration and size distribution, and Vesicle Flow Cytometry, in order to measure the proportion of EVs bound to IgG.
For the NTA, the plasma-derived EV samples are previously diluted in filtered phosphate-buffered saline (PBS) to achieve a concentration within the range for optimal NTA analysis.
The plasma-derived EV samples are submitted to two incubation substeps to stain EVs prior to the Vesicle Flow Cytometry analysis. In the first incubation substep, the EVs are stained with anti-IgG in PBS and incubated for a time range from 1 to 3 h in a temperature range from 20 to 37 °C. In the second incubation step, the antibody-stained sample is then incubated with an EV specific fluorescent dye for a time range from 1 to 2 h in a temperature range from 20 to 37 °C. In a preferred embodiment of the invention, the EV specific fluorescent dye is Carboxyfluorescein Diacetate Succinimidyl Ester (CFSE).
For the removal of unbound EV specific fluorescent dye and antibody, Size Exclusion Chromatography (SEC) columns were used. The EV-enriched fractions are pooled and retrieved for analysis with the flow cytometer.
The method of the invention allows the analysis of EVs directly in plasma, without the need of a centrifugation step of the plasma samples for the isolation of EVs.
At last, in step (d), the treatment of the data is performed by a central processing unit and consists of monitoring the levels (percentages) of EVs that express IgG throughout the treatment of the patients, in order to assess whether or not these patients are responding to the therapy (through reduction, stabilization, or increased plasma levels of IgG+EVs over time).
More specifically, the method of the present invention allows investigating the percentage of EVs that express IgG as a marker of response to therapy, wherein the levels of IgG+EVs decrease in case of response to treatment and increase in case of non-response to treatment.
Examples
Materials and Methods
Patients and Controls
Patients were eligible to participate if they had a confirmed histological or cytological diagnosis of metastatic PDAC, according to the 8th edition of AJCC Cancer Staging22, and were able to receive chemotherapy. Samples from healthy individuals were collected from voluntary donors at Champalimaud Clinical Center and Champalimaud Research. Exclusion criteria were previous diagnosis of other tumors or inflammatory diseases. Written informed consent was obtained from both patients and control individuals, and samples were deidentified for confidentiality. All the procedures were conducted in accordance with the Helsinki Declaration and its amendments, with the approval of the Ethical Committees of Nova Medical School and of the Champalimaud Foundation.
Treatment proposals were done at the multidisciplinary tumor board. Patients were treated in a sequence of chemotherapy with the following approved regimens: association of 5-fluorouracil, irinotecan, and oxaliplatin (FOLFIRINOX); association of gemcitabine and nab-paclitaxel; association of 5-fluorouracil and liposomal irinotecan; or single gemcitabine. The decision on the choice of the chemotherapy regimen was at the discretion of the treating oncologist. Demographic and clinical information from the patients, including neutrophil-to-lymphocyte ratio and C-Reactive Protein levels, was collected. Treatment response was classified based on the imaging response based on RECIST v1.1 criteria8. Timepoints were selected as follows: timepoint I (pretreatment) was collected before the beginning of a new chemotherapy regimen, and timepoint II (posttreatment) at the time of either the best imaging response (if tumor responded with chemotherapy) or the worst (if not responding to chemotherapy). The same patient can be a responder at one point of treatment and a nonresponder at another. The first samples of one patient that met these criteria were selected until the calculated minimum number of total required samples was obtained.
Five of the 19 patients were used exclusively for the proteomic analysis of EVs, as there were no samples left for the subsequent experiments. The remaining 14 patients, in a total of 155 plasma samples, were studied by vesicles flow cytometry. provides information on the metastatic profile, treatment regimens, and sample collection times. For the control group, the same exclusion criteria (previous oncological diagnosis or previous inflammatory diseases) were applied. The group included 21 individuals (ten males and eleven females) with a mean age of 47 years old (minimum of 25 years; maximum of 85 years).
Purification and characterization of EVs from plasma
Blood samples from patients and healthy donors were collected in a 9 mL Vacuette NV EDTA K3 tube and centrifuged twice at 10 degrees Celsius (500 g for 10 minutes and 3 000 g for 20 minutes). Prior to analysis, plasma samples were aliquoted and stored at -80oC. A protocol previously described involving sequential ultracentrifugation combined with sucrose cushion was used to purify EVs23.
All EV samples were analyzed by Nanoparticle Tracking Analysis (NTA) using a NanoSight NS300 equipped with red laser (638 nm) to determine particle concentration and size distribution (Malvern Panalytical, United Kingdom). Samples were pre-diluted in filtered PBS to achieve a concentration within the range for optimal NTA analysis. Video acquisitions were performed at 25ºC using a camera level of 16, and a threshold between 4 and 6. Five videos of 30 s with 10-50 particles per frame were captured per sample. The total protein content of EV samples was determined using the PierceTM BCA Protein Assay Kit (Thermo Fisher Scientific).
Characterization of EV protein composition by Mass Spectrometry (MS)
For evaluation by MS, plasma-derived EV samples from PDAC patients were used. Four patients that responded and four patients that displayed disease progression were selected. For each patient, samples were collected at diagnosis and after treatment response evaluation, totaling sixteen samples. In parallel, five plasma-derived EV samples from healthy controls were also compared to eight PDAC patients at diagnosis.
The EV solution containing sodium dodecyl sulfate (SDS) and dithiothreitol (DTT) was loaded onto filtering columns and washed exhaustively with 8 M urea in HEPES buffer24. Proteins were reduced with DTT and alkylated with IAA. Protein digestion was performed by overnight digestion with trypsin sequencing grade (Promega).
Peptides samples were analyzed by nano-LC-MSMS (Dionex RSLCnano 3000) coupled to an Exploris 480 Orbitrap mass spectrometer (Thermo Scientific, Hemel Hempstead, UK) virtually as previously described25. Briefly, samples were loaded (flowrate 5 µL per minute for 6 minutes) onto a custom made fused capillary pre-column (2 cm length, 360 µm OD, 75 µm ID) packed with ReproSil Pur C18 5.0 µm resin (Dr. Maish, Ammerbuch-Entringen, Germany) followed by the separation on a custom made fused capillary column (25 cm length, 360 µm outer diameter, 75 µm inner diameter) packed with ReproSil Pur C18 1.9-µm resin (Dr. Maish, Ammerbuch-Entringen, Germany) using a flow of 250 nL per minute. Gradient was from 89% A (0.1% formic acid) to 32% B (0.1% formic acid in 80% acetonitrile) over 56 min. Mass spectra were acquired in positive ion mode applying automatic data-dependent switch between one Orbitrap survey MS scan in the mass range of 350–1200 m/z followed by higher-energy collision dissociation (HCD) fragmentation and Orbitrap detection of fragment ions with a cycle time of 2 s between each master scan. MS and MSMS settings: maximum injection times were set to “Auto”, normalized collision energy was 30%, ion selection threshold for MSMS analysis was 10,000 counts, and dynamic exclusion of sequenced ions was set to 30 s.
Data obtained from 46 LC-MS runs of 21 subjects and 32 LC-MS runs of IgG bound proteins were searched using VEMS26, 27 and MaxQuant28. A standard proteome database from UniProt (3AUP000005640), in which common contaminants were included, was also searched. Trypsin cleavage, allowing a maximum of four missed cleavages, was used. Carbamidomethyl cysteine was included as fixed modification. Methionine oxidation and N-terminal protein acetylation were included as variable modifications; 5 ppm mass accuracy was specified for precursor ions and 0.01 m/z for fragment ions. The FDR for protein identification was set at 1% for peptide and protein identifications. No restriction was applied to the minimal peptide length for VEMS search. Identified proteins were divided into evidence groups as defined27. Functional pathway analysis was performed with STRING (string-db.org).
Western blotting
Western blotting was used to assess the presence of EV and non-EV protein markers. Equal protein amounts of EV samples were mixed with 4X Laemmli buffer (Bio-Rad), denatured for 5 min at 95°C, and loaded onto 4-20% Mini-PROTEAN TGX Stain-Free Protein Gels (Bio-Rad). SDS-PAGE was run for 1.5 h at 90 V and then proteins were transferred to nitrocellulose membranes (Cytiva) at 100 V for 1 h. Membranes were blocked with LI-COR Intercept Blocking Buffer (LI-COR Biosciences) for 1 h at RT. Blocked membranes were incubated overnight at 4°C with primary antibodies diluted in LI-COR blocking buffer with 0.1% Tween-20. Membranes were washed with TBS-T (TBS with 0.1% Tween-20) three times for 5 min and then incubated with secondary antibodies for 1 h at RT. Incubation was followed by three additional washes with TBS-T, 5 min each. Blots were imaged using the Odyssey Infrared Imaging System (LI-COR Biosciences). The detailed list of primary and secondary antibodies used is provided in Table 1 above.
Table 1 – List of primary and secondary antibodies used for Western blotting
Primary Antibodies
Antibody Manufacturer Catalog no Dilution/concentration
CD81 Santa Cruz Biotechnology sc-166029 1:50
CD9 Cell Signaling #13174 1:1000
Alix Sigma Aldrich SAB4200476-200UL 1.25 ug/mL
Calnexin Abcam ab22595 1:2000
GM130 Abcam ab52649 1:1000
Secondary Antibodies
Antibody Manufacturer Catalog no Dilution
Goat anti-rabbit IgG IRDye 800CW LI-COR Biosciences 926-32211
 1:5000
Goat anti-mouse IgG IRDye 800CW LI-COR Biosciences 926-32210
 1:5000
Analysis of IgG + EV population by vesicle flow cytometry
Flow cytometry analysis of plasma EVs was performed as described by our group29. A volume of plasma containing 2 × 109 particles was used for staining with 0.5 μL of anti-IgG in PBS, in a final volume of 40 μL, and incubated for 1 h at 37°C. The antibody-stained sample was then incubated with Carboxyfluorescein Diacetate Succinimidyl Ester (CFSE – Thermo Fisher Scientific LTI C34554, MA, United States) to a final concentration of 25.6 μM, for 90 min at 37°C. For the removal of unbound CFSE and antibody, Size Exclusion Chromatography (SEC) columns (iZON qEV original columns SP1, United Kingdom) were used. EV-enriched fractions #7, #8, and #9 were then pooled (total of 1500 μL) and retrieved for analysis with the flow cytometer Apogee A60-Micro-Plus (Apogee Flow Systems, United Kingdom) configured as described in Table 2. For all subsequent analyses, quadrant thresholds were established with unstained and single-stained extracellular vesicles (with CFSE or with anti-IgG) ( ).
Internal controls across assays were performed as previously described29. The acquired data was exported and analyzed with FlowJo software v10.4.2 (FlowJo LLC, United States).
Table 2 – Cytometer configuration and laser power
Channel number Short Channel Name Full Channel Name Optical Filter Name Laser Wavelength Laser Power PMT Voltage
Ch1 405-SALS Small Angle Light Scatter 405 nm 200 mW 400 V
Ch2 405-LALS Large Angle Light Scatter 405 nm 200 mW 400 V
Ch3 405-Gm Green Fluorescence BP-525/50 405 nm 200 mW 500 V
Ch4 405-Org Orange Fluorescence LWP-590/35 405 nm 200 mW 500 V
Ch5 APC Red Fluorescence BP-676/36 638 nm 150 mW 550 V
Ch6 CFSE Green Fluorescence BP-525/50 488 nm 200 mW 525 V
Ch7 PE Orange Fluorescence BP-575/30 488 nm 200 mW 500 V
Ch8 488-Red Red Fluorescence BP-676/36 488 nm 200 mW 500 V
Ch9 488-DRed Deep Red Fluorescence LWP-740 488 nm 200 mW 500 V
Total plasma immunoglobulin G quantification
For the quantification of the plasma IgG, samples were processed using the nephelometry method (BNProSpec – Synlab, Portugal). Reference values for healthy controls were 700 - 1 600 mg/dL.
Identification of EV surface proteins associated with IgG
To identify which surface proteins of EVs bind to IgG, 200 μg of EVs isolated from 8 PDAC patients and 8 healthy controls were first biotinylated (EZ-Link™ Sulfo-NHS-SS-Biotin, Thermo Scientific, 21331) and then lysed using 0.5% NP-40 lysis buffer (150 mM NaCl, 10 mM Tris-HCl pH 7.5, 0.5 mM EDTA, 0.5% NP-40). Next, the biotinylated surface proteins of EVs were collected using streptavidin magnetic beads30 (Dynabeads™ MyOne™ Streptavidin C, Invitrogen, 65001), and then detached from the beads (deionized water, 70ºC)31. After separation, surface EV proteins that were associated with IgG were co-immunoprecipitated using magnetic beads (Dynabeads™ Protein G for Immunoprecipitation, Invitrogen, 10003D) conjugated with anti-human IgG antibody (Goat anti-Human IgG F(ab')2 Secondary Antibody, Invitrogen, 31122), then eluted and analyzed by MS.
Statistical analysis
Sample size was based on previous liquid biopsy studies32. The analysis involves 155 observations from 30 different situations (15 responders x 15 nonresponders). Experiments were not randomized. The researchers were blinded to allocation during experiments and outcome assessment. The response evaluation to the treatment was previously done by a different researcher. Error bars in graphical data represent means ± standard errors of the means (SEM). Normality and homogeneity of variances from the analyzed variables were tested with Shapiro-Wilk test and Bartlett or Levene tests, respectively. If data were parametric, Student’s t test (two populations) were used. If data were not parametric, Wilcoxon or Mann-Whitney tests were performed. For ROC analysis, values were obtained from the division of IgG+EVs and CA19.9 readings after treatment by those before treatment in each studied point, as previously described11. Statistical packages used were R v.4.0.2. For all evaluations, a P-value under 0.05 was considered statistically significant and the null hypothesis was rejected. Graphical design was performed with the GraphPad Prism software (GraphPad software)
Results
Identification of possible EV markers for PDAC diagnosis and therapeutic response
The size distribution and concentration of plasma EVs isolated from patients and healthy controls were characterized. Proteins frequently present or absent in small EVs were measured in our samples ( ). MS analysis of plasma EV samples from five healthy controls and sixteen samples from eight PDAC patients was performed, both at the time of diagnosis and after treatment. Four of these eight patients were considered chemotherapy responders, as tumor shrinkage was observed between the diagnosis and treatment timepoints. In contrast, based on the observed imaging progression of the disease between the two time points, the remaining four patients corresponded to nonresponders to chemotherapy. For the MS analysis, the same amounts of protein (20 μg) and concentrations (0.5 μg/μL) were utilized.
Protein expression analysis revealed that 102 distinct proteins exhibited statistically significant differences between PDAC patients and healthy controls, 59 of which were upregulated in PDAC patients. Of these, the presence of multiple IgG fragments (Figure 4A, Table 3) was identified. In fact, the functional analysis of proteins significantly upregulated or downregulated in EVs from PDAC patients (Responders and Nonresponders) and as compared to healthy controls revealed enrichment in proteins associated with humoral immune response and complement activation, among others ( ).
Table 3 – EV proteins upregulated in patients with PDAC (vs. healthy controls)
Protein IDs Gene names Mean Healthy Controls Mean PDAC logFC P.Value
P02675 FGB 30,80916 32,63287 1,823711 1,7E-15
P01591 IGJ 31,04684 29,11787 -1,92898 1,71E-12
Q16610;Q16610-4;Q16610-2;Q16610-3 ECM1 22,3285 24,74706 2,418563 5,83E-10
P01871;P01871-2 IGHM 32,80839 31,13976 -1,66863 3,11E-09
P02679 FGG 31,73688 32,79369 1,056811 5,31E-09
P07360 C8G 24,25857 25,95475 1,69618 1,28E-08
Q9Y2I7 PIKFYVE 8.610452 18,488365 9,877913 5,23E-08
P08603;P08603-2 CFH 25,70408 26,9523 1,24822 8,16E-08
P02751;P02751-8;P02751-3;P02751-15 FN1 29,12809 30,96719 1,839091 8,28E-08
O75636;O75636-2 FCN3 27,6463 30,25062 2,604322 1,07E-07
P12111-4;P12111-2;P12111;P12111-3;P12111-5 COL6A3 0.7664544 13,3798081 12,61335 1,14E-07
Q9C0K7-3;Q9C0K7-2;Q9C0K7 STRADB 6.21537 19,08732 12,87195 1,86E-07
P81605;P81605-2 DCD 15,62981 1.59704 -14,0328 2,21E-07
P00488;P16452-3;P16452;P16452-2 F13A1 23,53023 25,2402 1,709968 2,47E-07
A0A0B4J1V6 IGHV3-73 24,99557 23,20821 -1,78736 3,07E-07
P09871 C1S 22,88309 23,99478 1,111685 3,33E-07
P02679-2 FGG 13,1101 21,87546 8,765356 6,49E-07
O43866 CD5L 29,42484 27,78712 -1,63772 8,76E-07
Q08830 FGL1 13,01631 21,4914 8,475092 9,83E-07
P0DOY3 32,86217 31,9975 -0,86467 1,03E-06
P01709 27,85825 25,97132 -1,88692 1,71E-06
A0A0B4J1V0 IGHV3-15 25,76245 24,63909 -1,12337 2,45E-06
P00451;P00451-2 F8 18,60007 21,25979 2,659721 3,93E-06
P07358 C8B 23,55035 24,45246 0,902108 5,32E-06
P01011;P01011-2 SERPINA3 24,59445 25,81344 1,218999 1E-05
P01042 KNG1 22,39664 23,79217 1,395532 1,05E-05
O14791;O14791-2;O14791-3 APOL1 26,07611 24,37361 -1,7025 1,76E-05
P04275;P04275-2 VWF 27,3174 29,24253 1,925138 1,9E-05
P04196 HRG 24,01787 25,45884 1,440968 2,94E-05
P12109 COL6A1 5.053083 15,624026 10,57094 3,88E-05
P01780 29,81985 28,87501 -0,94484 6,6E-05
A0A087WW87;P01614 IGKV2-40 28,98482 28,20821 -0,77661 9,96E-05
P06312 IGKV4-1 27,12003 26,34662 -0,77341 0,00014
P04211 23,414648 9.843152 -13,5715 0,000152
Q08380 LGALS3BP 27,12358 25,96748 -1,1561 0,000168
P13671 C6 22,07315 23,13764 1,064495 0,000171
P10909-4;P10909;P10909-5;P10909-2;P10909-3 CLU 26,44996 27,21772 0,767758 0,000203
A0M8Q6 IGLC7 16,343175 4.559787 -11,7834 0,000234
P02792 FTL 23,44187 26,07389 2,632021 0,000249
Q4LDE5;Q4LDE5-4;Q4LDE5-3;Q4LDE5-2 SVEP1 4.153104 14,13788 9,984777 0,000271
Q9HCU4 CELSR2 7.209845 18,779373 11,56953 0,000376
Q14520-2;Q14520 HABP2 9.420802 18,345494 8,924692 0,000449
Q9Y6R7 FCGBP 22,3186 20,91387 -1,40473 0,000479
Q9NZT1 CALML5 15,381387 4.785034 -10,5964 0,000542
Q08554-2;Q08554 DSC1 12,67823 3.226449 -9,45178 0,000723
Q96IY4;Q96IY4-2 CPB2 9.124999 18,288738 9,163739 0,000772
P01009;P01009-2;P01009-3;P20848 SERPINA1 27,45175 28,67493 1,223177 0,000823
A0A0C4DH68 IGKV2-24 27,98671 27,01923 -0,96748 0,00084
P02743 APCS 26,49926 27,12377 0,624511 0,000918
O43166 SIPA1L1 3,611292 0 -3,61129 0,001353
P01859 IGHG2 28,50515 29,53058 1,025437 0,001748
Q01469 FABP5 13,333104 3.547731 -9,78537 0,001785
Q562R1 ACTBL2 4,454414 0 -4,45441 0,001804
A0A0A0MRZ9 IGLV5-52 4,580166 0 -4,58017 0,001869
P01860 IGHG3 28,57457 29,43901 0,86444 0,002166
P02763 ORM1 23,88044 25,18618 1,30574 0,002263
P29622 SERPINA4 15,58681 20,29616 4,709354 0,002959
O00189 AP4M1 8.074653 16,858605 8,783952 0,003489
P07357 C8A 24,73622 25,25004 0,513816 0,004064
P01594;P01593 23,88627 24,80415 0,91788 0,004506
Q15582 TGFBI 5.345572 13,679386 8,333814 0,005173
P98160 HSPG2 8.859966 15,662051 6,802085 0,005736
A2NJV5;A0A075B6S2;A0A0A0MRZ7 IGKV A18;IGKV2D-29;IGKV2D-26 6,2640694 0,5281823 -5,73589 0,006043
P00748 F12 21,33945 22,1381 0,798658 0,006085
P22792 CPN2 22,25886 22,8205 0,561642 0,006173
P11021 HSPA5 3.720965 11,713255 7,992289 0,006826
P01876 IGHA1 31,13867 30,24995 -0,88872 0,006911
Q92496-2;Q92496;Q92496-3 CFHR4 2.42436 11,03821 8,613846 0,007766
A0A075B6I1 IGLV4-60 13,87167 5.185394 -8,68628 0,010051
P01703 17,37782 23,23569 5,857872 0,010649
P18428 LBP 11,47416 18,31519 6,841032 0,011323
Q8NI99 ANGPTL6 11,526017 4.144387 -7,38163 0,011906
A0A0B4J1U7 IGHV6-1 22,59996 26,62302 4,023052 0,012004
Q9BXR6 CFHR5 5.800607 13,338935 7,538328 0,014154
P02751-10;P02751-13 FN1 9.087818 16,69571 7,607892 0,01441
P02790 HPX 25,28332 26,11439 0,831076 0,014443
P21980-2;P21980;P21980-3 TGM2 4,2720915 0,4457934 -3,8263 0,014565
P02746 C1QB 27,56949 28,01983 0,450336 0,019773
P02760 AMBP 24,33717 24,93431 0,597142 0,020355
P01701 27,48918 26,60877 -0,88041 0,021183
P01766 24,45966 17,99343 -6,46623 0,023374
P01019 AGT 23,4802 22,67968 -0,80053 0,023513
A0A0G2JS06 25,3019 19,47542 -5,82649 0,024552
P01704 19,9328 25,27153 5,338733 0,024827
Q5T749 KPRP 4,4687764 0,5660902 -3,90269 0,024872
O00187;O00187-2 MASP2 19,93343 20,97891 1,04548 0,026433
Q03591 CFHR1 23,1884 23,76836 0,579961 0,02717
P11217-2;P11217;P06737-2;P11216;P06737 PYGM 4,6574862 0,6082011 -4,04929 0,027593
P07225 PROS1 25,57112 25,99843 0,427312 0,029207
Q12805-5;Q12805-2;Q12805-4;Q12805-3;Q12805 EFEMP1 2,296374 8,973493 6,67712 0,029245
A0A075B6H9 IGLV4-69 25,56016 20,4878 -5,07236 0,029928
P01715 19,30839 11,32053 -7,98786 0,031146
Q02413;Q02413-2 DSG1 3,6552677 0,5459463 -3,10932 0,036858
Q66K66 TMEM198 20,92864 23,50407 2,575437 0,038233
P08473 MME 15,310028 8.574814 -6,73521 0,038239
P68133;P68032;P63267;P62736;P63267-2 ACTA1;ACTC1;ACTG2;ACTA2 7.430236 13,875415 6,44518 0,040955
A0A0C4DH24 IGKV6-21 24,16357 18,66946 -5,4941 0,041297
P01817 12,696209 5.607219 -7,08899 0,042827
P15144 ANPEP 20,31834 14,37981 -5,93853 0,045148
P62987;P62979;P0CG47;P0CG48 UBA52;RPS27A;UBB;UBC 19,56519 12,92859 -6,63661 0,046162
A0A075B6S6 IGKV2D-30 12,474057 5.802547 -6,67151 0,048532
P08519;Q16609 LPA 5.945479 12,500058 6,554579 0,049001
It was also an objective to identify treatment response indicators. By comparing EVs isolated from PDAC patients who responded to therapy with those who did not, 43 proteins that exhibited statistically significant differences between responders and nonresponders were identified, with 24 of these upregulated in nonresponders (Figure 4B, Table 4). It was found that 16 of the upregulated proteins in patients who did not respond to chemotherapy were IgG fragments. As a result, it was chosen to investigate further whether the presence of IgG in populations of plasma EVs is indeed associated with PDAC diagnosis and therapeutic response.
Table 4 – EV proteins upregulated in nonresponders PDAC patients (vs. responders)
Protein IDs Gene names Mean Non-Responders Mean Responders logFC P.Value
A0A0C4DH72;A0A0C4DH73;P01611 IGKV1-6 15,060969 3.574371 -11,4866 0,000489
P04432;P01597 14,692537 3.558026 -11,1345 0,000562
P01817 10,90292 0.00000 -10,9029 0,000293
A0A0C4DH68 IGKV2-24 26,58469 27,47934 0,894657 0,000201
P02746 C1QB 27,77124 28,28304 0,511799 0,001447
P01714 25,10489 26,84546 1,740563 0,00226
P15814;P15814-2 IGLL1 8,060513 0 -8,06051 0,002832
Q08554-2;Q08554 DSC1 0 6,642689 6,642689 0,004575
A0A075B6S5;A0A0C4DH69 IGKV1-27 15,14843 5.662179 -9,48625 0,004863
P62987;P62979;P0CG47;P0CG48 UBA52;RPS27A;UBB;UBC 8.622959 17,487488 8,864529 0,007196
P15144 ANPEP 10,49458 18,49358 7,998998 0,009811
A0A0B4J1V0 IGHV3-15 24,3923 24,90039 0,508095 0,009961
Q9NZP8 C1RL 9,544 2,343031 -7,20097 0,010317
A0A0A0MT36 IGKV6D-21 10,839119 2.553621 -8,2855 0,013266
P07357 C8A 25,08541 25,42435 0,338941 0,013604
Q13103 SPP2 9,603383 2,31008 -7,2933 0,014323
Q9BXR6 CFHR5 16,619122 9.865795 -6,75333 0,017651
Q8TCG1-2;Q8TCG1 KIAA1524 8,173923 1,876418 -6,2975 0,020795
P08637;O75015 FCGR3A 11,79168 18,71712 6,925439 0,021632
Q9BWP8-8;Q9BWP8-7;Q9BWP8-6;Q9BWP8-5 COLEC11 12,04857 4.62706 -7,42151 0,023107
P01857 IGHG1 32,56523 31,99845 -0,56677 0,02629
P03952;P20718 KLKB1 23,64847 23,94836 0,299895 0,026916
P13164;Q01629;Q01628 IFITM1;IFITM2;IFITM3 0 5,501942 5,501942 0,027497
P05106-2;P05106-3;P05106 ITGB3 0 3,959515 3,959515 0,027508
P02745 C1QA 27,34568 27,7409 0,395215 0,02757
A0A0C4DH43;P01814 23,72722 17,35704 -6,37018 0,028112
A0A0A0MS15 IGHV3-49 23,7816 22,93867 -0,84293 0,028814
P07225 PROS1 25,81825 26,18921 0,370953 0,028895
P04278-4;P04278-2;P04278-3;P04278;P04278-5 SHBG 13,2751 6.543573 -6,73153 0,029683
P08670;P17661;Q16352;P07197-2 VIM 0 4,58408 4,58408 0,029827
P02747 C1QC 28,01889 28,39234 0,373451 0,030901
P23083 25,56806 26,25761 0,689546 0,031442
P28074-3;P28074 PSMB5 8,442728 2,336585 -6,10614 0,03599
P04430 21,55277 16,68508 -4,8677 0,036212
A0A087WSX0 IGLV5-45 5,619149 0 -5,61915 0,037874
P01699 4,512501 0 -4,5125 0,037909
P27169 PON1 23,04364 23,63591 0,592268 0,040998
A0A0C4DH29 IGHV1-3 21,04053 15,33251 -5,70801 0,042131
A0A075B6I0 IGLV8-61 25,2758 19,73274 -5,54307 0,04525
A0A075B6Q5 IGHV3-64 18,17808 11,41315 -6,76493 0,047306
Q01469 FABP5 1,009046 6,235751 5,226705 0,04767
P01861 IGHG4 27,21168 26,56055 -0,65113 0,048619
P00739;P00739-2 HPR 26,29785 25,7406 -0,55725 0,049185
Evaluation of IgG + EVs as possible markers of PDAC treatment response
In accordance with the MS findings, vesicle flow cytometry revealed that metastatic PDAC patients have a larger population of IgG+ EVs compared to healthy control donors (Figure 6A), suggesting that IgG+ EVs may be a useful diagnostic marker for advanced PDAC disease. Next, it was examined whether these IgG+ EV populations could be used to determine whether a patient is responding to chemotherapy or not.
At and after diagnosis, prospective clinical information, CT data, serum levels of CA19.9, hemograms, and serial whole-blood results were collected from patients with stage IV PDAC. To be considered suitable in clinical practice for the care of metastatic PDAC patients, a new marker should: a) be consistent, independent of the treatment of choice; b) be able to identify differences in patients with and without CA19.9 expression; and c) be able to predict treatment response in comparison to the imagiological evaluation, in order to reinforce the maintenance of applied treatment or to suspend futile treatments early33, 34.
Keeping this in mind, the proportion of IgG+ EVs in the plasma of multiple patients who had received at least two lines of chemotherapy (FOLFIRINOX - first line; Gemcitabine/nabpaclitaxel - second line) was analyzed. In contrast to the increase of IgG+ EV populations in the plasma of PDAC patients during tumor progression (nonresponse), the monthly evaluation of IgG+ EV populations in the plasma of PDAC patients (Figure 6B) revealed a decrease in IgG+ EVs upon imagiological response.
To determine whether IgG+ EVs meet the criteria for independence from CA19.9 expression, it was performed the same monthly IgG+ EVs analysis on two patients without CA19.9 expression. The same pattern was observed as for patients with CA19.9 expression, suggesting that the evaluation of IgG+ EVs as a marker of response to therapy may be applicable to all metastatic PDAC patients (Figure 6C). In addition, the measurement of IgG+ EVs was compared to the measurement of CA19.9, the gold standard serological marker in PDAC12. As expected, it was observed a significant downregulation of CA19.9 in patients who responded to therapy (Figure 7A) and an upregulation of CA19.9 in nonresponders (Figure 7B).
In addition to the individual longitudinal analysis of patients, chemotherapy response and nonresponse timepoints were grouped to investigate the population of IgG+ EVs in responders and nonresponders. As shown in Figure 6D, it was observed a significant decrease in the IgG+ EV population in responders following treatment. In contrast, the proportion of IgG+ EVs significantly increased in patients who did not respond to chemotherapy (Figure 6E). The same patterns of response were observed with CA19.9, as predicted. The evaluation of sensitivity and specificity of IgG+EV for response evaluation of patients with metastatic PDAC showed an AUC of 0.8311 (95% confidence interval 0.6788 to 0.9834, p= 0.0020) (Figure 6F), compared to an AUC of 0.9911 (95% confidence interval 0.9679 to 1.00, p<0001) for the same evaluation for CA19.9 in our population ( ).
Our findings suggest that levels of IgG+ EVs are associated with imaging evaluations of clinical response to treatment, suggesting that IgG+ EVs could be used as a readout in PDAC clinical settings.
Evaluation of the potential role of plasmatic IgG levels and inflammation status in IgG + EV proportion
The fluctuations in the plasmatic proportion of IgG+ EVs could, in theory, be a direct result of variations in plasma IgG35 levels. To test this hypothesis, the total amount of plasmatic IgG was measured in the same set of patients. It was found no significant differences in plasmatic IgG levels during treatment response or disease progression in patients with PDAC (Figure 8A and 8B). Consequently, it was found no correlation between the percentage of IgG+ EVs and total plasmatic IgG levels, indicating that the variation of IgG+ EVs is not due to fluctuations in plasmatic IgG levels (Figure 8C).
Alternately, IgG+ EVs may derive, at least in part, from alterations in the inflammatory status of immune cells during the progression of PDAC. The neutrophil/lymphocyte ratio (NLR) is a clinical marker of inflammation calculated as the quotient of the absolute neutrophil and lymphocyte counts36. In light of this, it was found no differences between the groups (responders and nonresponders) and no correlation between levels of IgG+ EVs and neutrophil-to-lymphocyte ratio. In addition, it was found no correlation between the levels the inflammatory marker C-Reactive Protein37 and the levels of IgG+ EVs ( ), further suggesting that in the context of the present invention, the inflammatory status has no effect on IgG+ EV levels.
Identification of IgG-associated proteins on the surface of PDAC patient EVs
Next, it was investigated potential differences in the molecular mechanisms of IgG transport by plasma EVs between healthy controls and PDAC patients. To test this, a method for characterizing protein interactions at the surface of EVs was devised. Briefly, biotinylated surface proteins of lysed EVs were isolated by streptavidin binding, followed by co-immunoprecipitation with an antigen-specific antibody. Here, IgG was precipitated from the mixture and searched for EV surface proteins bound to IgG.
Several IgG fragments were detected in both healthy controls and PDAC patients. In addition, soluble proteins (alpha-2-macroglobulin), cell surface receptors (i.e., Glutamine Receptor), cytoskeletal proteins (i.e., Keratin), and cytoplasmic proteins were detected in both groups (e.g. Chloride intracellular channel protein 4, Golgi integral membrane protein 4, Chondroitin sulfate synthase 3 and Protein Argonaute 2). IgG was bound to Melanoma associated antigen B1 (MAGE B1) in the EVs of PDAC patients, in addition to IgG fragments and Albumin. MAGE B1 is a well-known PDAC antigen38, suggesting that the population of IgG+ EVs described here is the consequence of an interaction between tumoral antigens on the surface of EVs released by tumor cells and IgG in circulation (Table 5).
Table 5 – IgG associated proteins identified by MS
Cancer Patients Immunoglobulin lambda variable 3-21
Melanoma-associated antigen B1
Albumin
Probable non-functional immunoglobulin heavy variable 3-16
Healthy + Cancer Keratin, type II cytoskeletal 1
Chloride intracellular channel protein 4
Alpha-2-macroglobulin
Glutamate receptor ionotropic, kainate 3
Keratin, type II cytoskeletal 6B
HUMAN Ig kappa chain V-I region Lay
HUMAN Ig kappa chain V-III region NG9 (Fragment)
HUMAN Ig kappa chain V-III region POM
HUMAN Ig kappa chain V-III region CLL
HUMAN Ig kappa chain V-III region VH (Fragment)
Immunoglobulin kappa variable 3D-7
Immunoglobulin kappa variable 3/OR2-268 (non-functional)
Immunoglobulin kappa constant
Immunoglobulin kappa variable 3-15
Probable non-functional immunoglobulin kappa variable 3-7
Ig lambda chain V-II region NIG-84
Chondroitin sulfate synthase 3
Ig lambda chain V-II region BUR
Keratin, type I cytoskeletal 10
Protein argonaute-2
Golgi integral membrane protein 4
Healthy Donors S100A9
Ig kappa chain V-III region Ti
Uncharacterized protein (Fragment)
The analysis of EV bulks is an effective method for identifying molecules of interest (e.g., proteins, lipids, and RNA) in EV liquid biopsies. In fact, MS analysis of EVs in bulk was used to identify IgG as a possible EV marker of therapeutic response in PDAC patients. However, failure to distinguish between EV populations may obscure real differences between experimental groups. Moreover, the implementation of EV biomarkers in clinical practice is hindered by the laborious and time-consuming isolation and analysis protocols commonly employed for EVs. In an effort to characterize populations of extracellular vesicles (EVs) using a method that permits rapid analysis of markers in EV samples, a vesicle flow cytometry protocol was used29. By not requiring EV isolation prior to analysis, the processing time is reduced from >24 hours to 4 hours. Therefore, the use of vesicle flow cytometry has the potential to facilitate the clinical evaluation of IgG+ EVs.
Although the main objective of our work was to identify a novel biomarker to monitor therapy response of diagnosed metastatic PDAC patients, instead of identifying a new marker to diagnose PDAC, it was found elevated levels of IgG+ EVs in PDAC patients compared to healthy controls, reinforcing previous descriptions of the potential application of this marker for PDAC diagnosis45. In the present invention, it was found that levels of IgG+ EVs did not correlate with validated clinical markers for inflammation (Neutrophil-to-Lymphocyte Ratio and C-Reactive Protein), suggesting this biomarker is not affected by the inflammatory background of the patients. However, as the recruitment criteria for the analysis excluded individuals with inflammatory conditions, further study will be necessary to address the potential impact of inflammatory background in the specificity of IgG+ EVs as a diagnostic marker for PDAC.
It was possible to observe the dynamics of IgG+ EV populations within each PDAC patient and their association with the evaluation of chemotherapy response. It is also demonstrated that the analysis of IgG+ EV populations may be utilized in the follow-up of PDAC patients, including those who lack CA19.9 expression. Due to the absence of this established marker, these patients rely solely on imaging evaluations to determine their clinical response to chemotherapy; therefore, a new reliable marker would represent a substantial improvement in their care.
During the characterization of proteins bound to IgG in circulating EVs, a number of proteins were identified in both healthy donors and PDAC patients. This suggests that at least some IgGs bind to EVs via protein-protein interactions that may occur after EVs are secreted by cells. Consequently, circulating IgG and IgG ligands in EVs may interact in the extracellular environment of both cancer patients and healthy donors46. This is further supported by previous studies on autoimmune diseases, showing that immuno-globulins can bind to circulating EVs and form immune complexes that contribute to disease pathology47-50. In fact, the composition of EVs is not a mere result of their intracellular biogenesis, as their surface is highly interactive with proteins present at the extracellular milieu. The interaction of EVs with secreted proteins has been shown to modulate their immune recognition, mobility, uptake and signaling capabilities51. Remarkably, many proteins that frequently display quantitative changes in cancer patients, such as cytokines/chemokines52, extracellular matrix proteins53, coagulation factors54, complement factors55, immunoglobulins56 and albumin57, can interact with EVs after their release and change their composition51.
The MS analysis of proteins bound to IgG focused on EV surface proteins, as only these proteins were biotinylated during the immunoprecipitation protocol. Also, as vesicle flow cytometry studies were performed with intact EVs, all IgG+ signals derived from IgG bound at the external face of the EV surface. This agrees with the conventional mechanism of IgG binding to the surface of cells, which depends on the binding of IgG to a cell membrane protein on B cells, the Igα/Igβ heterodimer (CD79α/CD79β) [58, 59]. Therefore, as to CD79α/CD79β, at least part of IgG anchorage on EVs relies on binding to surface proteins of EVs, such as MAGE B1. Although MAGE B1 was identified as an EV surface ligand of IgG in 8 different PDAC patients, further study, including methods other than MS, will be necessary to validate this finding in larger cohorts of PDAC patients. Importantly, although immunoglobulins (Igs) were traditionally thought to be exclusively produced by B-lineage cells, recent studies have shown that these molecules can also be produced by a large diversity of tumor types21, including PDAC35, 60, 61. Therefore, although the association with MAGE B1 suggests that IgG binding to circulating EVs may be the result of post-secretion interactions with tumor neoantigens present on tumor EVs, at least part of the circulating IgG+EVs associated with metastatic PDAC burden could potentially be packed and secreted at the surface of EVs directly by PDAC cells.
MAGE B1 is a tumor antigen found in a variety of tumor types, including melanoma and tumors of epithelial origin, such as breast, colorectal carcinoma, lung, and pancreatic38, 62-66. In addition, MAGE is identified as an antigen normally expressed by the placenta and male germ cells in cancerous testes. It is expressed in 47 percent of pancreatic tumors67, giving cells that express it a survival advantage68 and negatively correlating with prognosis and patient survival67, 69. MAGE B1 was identified as one of the proteins found exclusively in EVs from PDAC patients. In addition, it was found that healthy controls had a significantly smaller IgG+ EV population, being the presence of IgG on the surface of these EVs unrelated to neo-antigens such as MAGE B1 (Table 5). Although the correlation between IgG+ EVs and the response of PDAC patients to chemotherapy is insufficient to conclude that the IgG+ EV population that varies based on chemotherapy response is tumor derived, it is proposed that at least a portion of the IgG+ EVs upregulation during PDAC progression may be due to IgG binding with PDAC EVs expressing MAGE B1. Due to samples limitations, it is not possible to further validate this result by additional methods. In spite of that, as the interaction of IgG with MAGE1 and other proteins was verified in all of the 8 different PDAC patients studied in our MS analysis, the results found are unlikely to be false positive.
We also found that the proportion of IgG+ EV is independent of the availability of circulating IgG. Alternatively, this may be the result of elevated levels of tumor EV secretion and/or enhanced packaging of tumor antigens (such as MAGE B1) in PDAC EVs. Moreover, it is plausible that this process may result in tumor-directed IgG absorption by tumor EVs and, as a result, may contribute to tumor immune-escape45 and the chronic inflammatory state observed in metastatic PDAC patients70. The binding of IgGs to EVs could also affect the efficacy of targeted therapies (e.g., immunotherapies). Additional re-search will be required to fully comprehend the aforementioned implications.
The subject matter described above is provided as an illustration of the present invention and, therefore, should not be construed to limit it. The terminology employed for the purpose of describing preferred embodiments of the present invention should not be restricted to them.
As used in the description, defined and indefinite articles, in their singular form, are intended for interpretation to also include plural forms, unless the context of the description explicitly indicates otherwise.
Undefined articles "one" should generally be interpreted as "one or more", unless the meaning of a singular modality is clearly defined in a specific situation.
It will be understood that the terms "understand" and "include", when used in this description, specify the presence of characteristics, elements, components, steps, and related operations, but do not exclude the possibility of other characteristics, elements, components, steps, and operations as well contemplated.
As used throughout this patent application, the term "or" is used in an inclusive sense rather than an exclusive sense, unless the exclusive meaning is clearly defined in a specific situation. In this context, a phrase of the type "X uses A or B" should be interpreted as including all relevant inclusive combinations, for example "X uses A", "X uses B" and "X uses A and B".
In addition, the articles “a” and “an” as used in this application and the appended claims should generally be construed to mean “one or more” unless specified otherwise or clear from the context to be directed to a singular form.
All changes, provided they do not modify the essential characteristics of the following claims, must be considered within the scope of the protection of the present invention.
The citation list is as follows:
EP2542696B1
EP2176665B1
WO2022046576A1
EP2718721A1
Non-Patent Literature
1: Sung, H., J. Ferlay, R. L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal and F. Bray. "Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries." CA: A Cancer Journal for Clinicians 71 (2021): 209-49. https://doi.org/10.3322/caac.21660. https://acsjournals.onlinelibrary.wiley.com/doi/abs/10.3322/caac.21660.
2: Siegel, R. L., K. D. Miller, H. E. Fuchs and A. Jemal. "Cancer statistics, 2021." CA: A Cancer Journal for Clinicians 71 (2021): 7-33. https://doi.org/10.3322/caac.21654. https://acsjournals.onlinelibrary.wiley.com/doi/abs/10.3322/caac.21654.
3: Carioli, G., M. Malvezzi, P. Bertuccio, P. Boffetta, F. Levi, C. La Vecchia and E. Negri. "European cancer mortality predictions for the year 2021 with focus on pancreatic and female lung cancer." Ann Oncol 32 (2021): 478-87. 10.1016/j.annonc.2021.01.006.
4: Rahib, L., B. D. Smith, R. Aizenberg, A. B. Rosenzweig, J. M. Fleshman and L. M. Matrisian. "Projecting cancer incidence and deaths to 2030: The unexpected burden of thyroid, liver, and pancreas cancers in the United States." Cancer Research 74 (2014): 2913-21. 10.1158/0008-5472.can-14-0155. https://cancerres.aacrjournals.org/content/canres/74/11/2913.full.pdf.
5: Conroy, T., F. Desseigne, M. Ychou, O. Bouché, R. Guimbaud, Y. Bécouarn, A. Adenis, J. L. Raoul, S. Gourgou-Bourgade, C. de la Fouchardière, et al. "Folfirinox versus gemcitabine for metastatic pancreatic cancer." N Engl J Med 364 (2011): 1817-25. 10.1056/NEJMoa1011923.
6: Von Hoff, D. D., T. Ervin, F. P. Arena, E. G. Chiorean, J. Infante, M. Moore, T. Seay, S. A. Tjulandin, W. W. Ma, M. N. Saleh, et al. "Increased survival in pancreatic cancer with nab-paclitaxel plus gemcitabine." N Engl J Med 369 (2013): 1691-703. 10.1056/NEJMoa1304369.
7: Gränsmark, E., N. Bågenholm Bylin, H. Blomstrand, M. Fredrikson, E. Åvall-Lundqvist and N. O. Elander. "Real world evidence on second-line palliative chemotherapy in advanced pancreatic cancer." Frontiers in Oncology 10 (2020): 10.3389/fonc.2020.01176. https://www.frontiersin.org/article/10.3389/fonc.2020.01176.
8: Eisenhauer, E. A., P. Therasse, J. Bogaerts, L. H. Schwartz, D. Sargent, R. Ford, J. Dancey, S. Arbuck, S. Gwyther, M. Mooney, et al. "New response evaluation criteria in solid tumours: Revised recist guideline (version 1.1)." Eur J Cancer 45 (2009): 228-47. 10.1016/j.ejca.2008.10.026. http://www.ejcancer.com/article/S0959-8049(08)00873-3/abstract.
9: Marom, E. M., S. Sarvis, J. E. Herndon, 2nd and E. F. Patz, Jr. "T1 lung cancers: Sensitivity of diagnosis with fluorodeoxyglucose pet." Radiology 223 (2002): 453-9. 10.1148/radiol.2232011131. https://www.ncbi.nlm.nih.gov/pubmed/11997552.
10: Tirkes, T., M. A. Hollar, M. Tann, M. D. Kohli, F. Akisik and K. Sandrasegaran. "Response criteria in oncologic imaging: Review of traditional and new criteria." Radiographics 33 (2013): 1323-41. 10.1148/rg.335125214.
11: Azizian, A., F. Ruhlmann, T. Krause, M. Bernhardt, P. Jo, A. Konig, M. Kleiss, A. Leha, M. Ghadimi and J. Gaedcke. "Ca19-9 for detecting recurrence of pancreatic cancer." Sci Rep 10 (2020): 1332. 10.1038/s41598-020-57930-x. https://www.ncbi.nlm.nih.gov/pubmed/31992753.
12: Scarà, S., P. Bottoni and R. Scatena. "Ca 19-9: Biochemical and clinical aspects." Adv Exp Med Biol 867 (2015): 247-60. 10.1007/978-94-017-7215-0_15.
13: Liu, C., S. Deng, K. Jin, Y. Gong, H. Cheng, Z. Fan, Y. Qian, Q. Huang, Q. Ni, G. Luo, et al. "Lewis antigen‑negative pancreatic cancer: An aggressive subgroup." Int J Oncol 56 (2020): 900-08. 10.3892/ijo.2020.4989. https://doi.org/10.3892/ijo.2020.4989.
14: Boulay, B. R. and M. Parepally. "Managing malignant biliary obstruction in pancreas cancer: Choosing the appropriate strategy." World J Gastroenterol 20 (2014): 9345-53. 10.3748/wjg.v20.i28.9345.
15: Kahlert, C., S. A. Melo, A. Protopopov, J. Tang, S. Seth, M. Koch, J. Zhang, J. Weitz, L. Chin, A. Futreal, et al. "Identification of double-stranded genomic DNA spanning all chromosomes with mutated kras and p53 DNA in the serum exosomes of patients with pancreatic cancer." J Biol Chem 289 (2014): 3869-75. 10.1074/jbc.C113.532267.
16: Hoshino, A., H. S. Kim, L. Bojmar, K. E. Gyan, M. Cioffi, J. Hernandez, C. P. Zambirinis, G. Rodrigues, H. Molina, S. Heissel, et al. "Extracellular vesicle and particle biomarkers define multiple human cancers." Cell 182 (2020): 1044-61 e18. 10.1016/j.cell.2020.07.009. https://www.ncbi.nlm.nih.gov/pubmed/32795414.
17: Costa-Silva, B., N. M. Aiello, A. J. Ocean, S. Singh, H. Zhang, Basant K. Thakur, A. Becker, A. Hoshino, M. T. Mark, H. Molina, et al. "Pancreatic cancer exosomes initiate pre-metastatic niche formation in the liver." Nature Cell Biology 17 (2015): 816-26. 10.1038/ncb3169. https://doi.org/10.1038/ncb3169.
18: Hoshino, A., B. Costa-Silva, T.-L. Shen, G. Rodrigues, A. Hashimoto, M. Tesic Mark, H. Molina, S. Kohsaka, A. Di Giannatale, S. Ceder, et al. "Tumour exosome integrins determine organotropic metastasis." Nature 527 (2015): 329-35. 10.1038/nature15756. https://doi.org/10.1038/nature15756.
19: Thakur, B. K., H. Zhang, A. Becker, I. Matei, Y. Huang, B. Costa-Silva, Y. Zheng, A. Hoshino, H. Brazier, J. Xiang, et al. "Double-stranded DNA in exosomes: A novel biomarker in cancer detection." Cell Research 24 (2014): 766-69. 10.1038/cr.2014.44. https://doi.org/10.1038/cr.2014.44.
20: Skog, J., T. Würdinger, S. van Rijn, D. H. Meijer, L. Gainche, W. T. Curry, B. S. Carter, A. M. Krichevsky and X. O. Breakefield. "Glioblastoma microvesicles transport rna and proteins that promote tumour growth and provide diagnostic biomarkers." Nature Cell Biology 10 (2008): 1470-76. 10.1038/ncb1800. https://doi.org/10.1038/ncb1800.
21: Cui, M., J. Huang, S. Zhang, Q. Liu, Q. Liao and X. Qiu. "Immunoglobulin expression in cancer cells and its critical roles in tumorigenesis." Front Immunol 12 (2021): 613530. 10.3389/fimmu.2021.613530. https://www.ncbi.nlm.nih.gov/pubmed/33841396.
22: Edge, S. B. Ajcc cancer staging manual 8th ed. 8th. New York: Springer, 2017,
23: Maia, J., A. H. Otake, J. Pocas, A. S. Carvalho, H. C. Beck, A. Magalhaes, R. Matthiesen, M. C. Strano Moraes and B. Costa-Silva. "Transcriptome reprogramming of cd11b(+) bone marrow cells by pancreatic cancer extracellular vesicles." Front Cell Dev Biol 8 (2020): 592518. 10.3389/fcell.2020.592518. https://www.ncbi.nlm.nih.gov/pubmed/33330473.
24: Wiśniewski, J. R., A. Zougman, N. Nagaraj and M. Mann. "Universal sample preparation method for proteome analysis." Nat Methods 6 (2009): 359-62. 10.1038/nmeth.1322.
25: Carvalho, A. S., H. Baeta, A. F. A. Henriques, M. Ejtehadifar, E. M. Tranfield, A. L. Sousa, A. Farinho, B. C. Silva, J. Cabecadas, P. Gameiro, et al. "Proteomic landscape of extracellular vesicles for diffuse large b-cell lymphoma subtyping." Int J Mol Sci 22 (2021): 10.3390/ijms222011004. https://www.ncbi.nlm.nih.gov/pubmed/34681663.
26: Carvalho, A. S., H. Ribeiro, P. Voabil, D. Penque, O. N. Jensen, H. Molina and R. Matthiesen. "Global mass spectrometry and transcriptomics array based drug profiling provides novel insight into glucosamine induced endoplasmic reticulum stress." Mol Cell Proteomics 13 (2014): 3294-307. 10.1074/mcp.M113.034363.
27: Matthiesen, R., G. Prieto, A. Amorim, K. Aloria, A. Fullaondo, A. S. Carvalho and J. M. Arizmendi. "Sir: Deterministic protein inference from peptides assigned to ms data." J Proteomics 75 (2012): 4176-83. 10.1016/j.jprot.2012.05.010.
28: Cox, J. and M. Mann. "Maxquant enables high peptide identification rates, individualized p.P.B.-range mass accuracies and proteome-wide protein quantification." Nat Biotechnol 26 (2008): 1367-72. 10.1038/nbt.1511.
29: Maia, J., S. Batista, N. Couto, A. C. Gregório, C. Bodo, J. Elzanowska, M. C. Strano Moraes and B. Costa-Silva. "Employing flow cytometry to extracellular vesicles sample microvolume analysis and quality control." Frontiers in Cell and Developmental Biology 8 (2020): 10.3389/fcell.2020.593750. https://www.frontiersin.org/article/10.3389/fcell.2020.593750.
30: Cvjetkovic, A., S. C. Jang, B. Konečná, J. L. Höög, C. Sihlbom, C. Lässer and J. Lötvall. "Detailed analysis of protein topology of extracellular vesicles-evidence of unconventional membrane protein orientation." Sci Rep 6 (2016): 36338. 10.1038/srep36338.
31: Holmberg, A., A. Blomstergren, O. Nord, M. Lukacs, J. Lundeberg and M. Uhlén. "The biotin-streptavidin interaction can be reversibly broken using water at elevated temperatures." Electrophoresis 26 (2005): 501-10. 10.1002/elps.200410070.
32: Dawson, S. J., D. W. Tsui, M. Murtaza, H. Biggs, O. M. Rueda, S. F. Chin, M. J. Dunning, D. Gale, T. Forshew, B. Mahler-Araujo, et al. "Analysis of circulating tumor DNA to monitor metastatic breast cancer." N Engl J Med 368 (2013): 1199-209. 10.1056/NEJMoa1213261.
33: Daamen, L. A., V. P. Groot, H. D. Heerkens, M. P. W. Intven, H. C. van Santvoort and I. Q. Molenaar. "Systematic review on the role of serum tumor markers in the detection of recurrent pancreatic cancer." HPB 20 (2018): 297-304. https://doi.org/10.1016/j.hpb.2017.11.009. https://www.sciencedirect.com/science/article/pii/S1365182X17311577.
34: Hou, J., X. Li and K.-P. Xie. "Coupled liquid biopsy and bioinformatics for pancreatic cancer early detection and precision prognostication." Molecular Cancer 20 (2021): 34. 10.1186/s12943-021-01309-7. https://doi.org/10.1186/s12943-021-01309-7.
35: Cui, M., L. You, B. Zheng, X. Huang, Q. Liu, J. Huang, B. Pan, X. Qiu, Q. Liao and Y. Zhao. "High expression of cancer-derived glycosylated immunoglobulin g predicts poor prognosis in pancreatic ductal adenocarcinoma." J Cancer 11 (2020): 2213-21. 10.7150/jca.39800.
36: Mei, Z., L. Shi, B. Wang, J. Yang, Z. Xiao, P. Du, Q. Wang and W. Yang. "Prognostic role of pretreatment blood neutrophil-to-lymphocyte ratio in advanced cancer survivors: A systematic review and meta-analysis of 66 cohort studies." Cancer Treatment Reviews 58 (2017): 1-13. 10.1016/j.ctrv.2017.05.005. https://doi.org/10.1016/j.ctrv.2017.05.005.
37: Allin, K. H. and B. G. Nordestgaard. "Elevated c-reactive protein in the diagnosis, prognosis, and cause of cancer." Crit Rev Clin Lab Sci 48 (2011): 155-70. 10.3109/10408363.2011.599831. https://www.ncbi.nlm.nih.gov/pubmed/22035340.
38: Schmitz-Winnenthal, F. H., L. V. Galindo-Escobedo, D. Rimoldi, W. Geng, P. Romero, M. Koch, J. Weitz, R. Krempien, A. G. Niethammer, P. Beckhove, et al. "Potential target antigens for immunotherapy in human pancreatic cancer." Cancer Lett 252 (2007): 290-8. 10.1016/j.canlet.2007.01.003.
39: Record, M., K. Carayon, M. Poirot and S. Silvente-Poirot. "Exosomes as new vesicular lipid transporters involved in cell-cell communication and various pathophysiologies." Biochim Biophys Acta 1841 (2014): 108-20. 10.1016/j.bbalip.2013.10.004. http://www.ncbi.nlm.nih.gov/pubmed/24140720.
40: Taylor, D. D. and C. Gercel-Taylor. "Microrna signatures of tumor-derived exosomes as diagnostic biomarkers of ovarian cancer." Gynecol Oncol 110 (2008): 13-21. 10.1016/j.ygyno.2008.04.033.
41: Nuzhat, Z., V. Kinhal, S. Sharma, G. E. Rice, V. Joshi and C. Salomon. "Tumour-derived exosomes as a signature of pancreatic cancer - liquid biopsies as indicators of tumour progression." Oncotarget 8 (2017): 17279-91. 10.18632/oncotarget.13973.
42: Couto, N., S. Caja, J. Maia, M. C. Strano Moraes and B. Costa-Silva. "Exosomes as emerging players in cancer biology." Biochimie 155 (2018): 2-10. 10.1016/j.biochi.2018.03.006.
43: Bernard, V., D. U. Kim, F. A. San Lucas, J. Castillo, K. Allenson, F. C. Mulu, B. M. Stephens, J. Huang, A. Semaan, P. A. Guerrero, et al. "Circulating nucleic acids are associated with outcomes of patients with pancreatic cancer." Gastroenterology 156 (2019): 108-18.e4. 10.1053/j.gastro.2018.09.022.
44: Liang, K., F. Liu, J. Fan, D. Sun, C. Liu, C. J. Lyon, D. W. Bernard, Y. Li, K. Yokoi, M. H. Katz, et al. "Nanoplasmonic quantification of tumour-derived extracellular vesicles in plasma microsamples for diagnosis and treatment monitoring." Nature Biomedical Engineering 1 (2017): 0021. 10.1038/s41551-016-0021. https://doi.org/10.1038/s41551-016-0021.
45: Capello, M., J. V. Vykoukal, H. Katayama, L. E. Bantis, H. Wang, D. L. Kundnani, C. Aguilar-Bonavides, M. Aguilar, S. C. Tripathi, D. S. Dhillon, et al. "Exosomes harbor b cell targets in pancreatic adenocarcinoma and exert decoy function against complement-mediated cytotoxicity." Nat Commun 10 (2019): 254. 10.1038/s41467-018-08109-6.
46: Hofmann, L., S. Ludwig, P. J. Schuler, T. K. Hoffmann, C. Brunner and M. N. Theodoraki. "The potential of cd16 on plasma-derived exosomes as a liquid biomarker in head and neck cancer." Int J Mol Sci 21 (2020): 10.3390/ijms21113739.
47: Cloutier, N., S. Tan, L. H. Boudreau, C. Cramb, R. Subbaiah, L. Lahey, A. Albert, R. Shnayder, R. Gobezie, P. A. Nigrovic, et al. "The exposure of autoantigens by microparticles underlies the formation of potent inflammatory components: The microparticle-associated immune complexes." EMBO Mol Med 5 (2013): 235-49. 10.1002/emmm.201201846. https://www.ncbi.nlm.nih.gov/pubmed/23165896.
48: Buzas, E. I., B. Gyorgy, G. Nagy, A. Falus and S. Gay. "Emerging role of extracellular vesicles in inflammatory diseases." Nat Rev Rheumatol 10 (2014): 356-64. 10.1038/nrrheum.2014.19. https://www.ncbi.nlm.nih.gov/pubmed/24535546.
49: Fortin, P. R., N. Cloutier, V. Bissonnette, E. Aghdassi, L. Eder, D. Simonyan, N. Laflamme and E. Boilard. "Distinct subtypes of microparticle-containing immune complexes are associated with disease activity, damage, and carotid intima-media thickness in systemic lupus erythematosus." J Rheumatol 43 (2016): 2019-25. 10.3899/jrheum.160050. https://www.ncbi.nlm.nih.gov/pubmed/27585687.
50: Perez-Hernandez, J., J. Redon and R. Cortes. "Extracellular vesicles as therapeutic agents in systemic lupus erythematosus." Int J Mol Sci 18 (2017): 10.3390/ijms18040717. https://www.ncbi.nlm.nih.gov/pubmed/28350323.
51: Buzas, E. I., E. A. Toth, B. W. Sodar and K. E. Szabo-Taylor. "Molecular interactions at the surface of extracellular vesicles." Semin Immunopathol 40 (2018): 453-64. 10.1007/s00281-018-0682-0. https://www.ncbi.nlm.nih.gov/pubmed/29663027.
52: Robinson, J. L., A. Feizi, M. Uhlen and J. Nielsen. "A systematic investigation of the malignant functions and diagnostic potential of the cancer secretome." Cell Rep 26 (2019): 2622-35 e5. 10.1016/j.celrep.2019.02.025. https://www.ncbi.nlm.nih.gov/pubmed/30840886.
53: Walker, C., E. Mojares and A. Del Rio Hernandez. "Role of extracellular matrix in development and cancer progression." Int J Mol Sci 19 (2018): 10.3390/ijms19103028. https://www.ncbi.nlm.nih.gov/pubmed/30287763.
54: Donati, M. B. and R. Lorenzet. "Coagulation factors and tumor cell biology: The role of tissue factor." Pathophysiol Haemost Thromb 33 Suppl 1 (2003): 22-5. 10.1159/000073284. https://www.ncbi.nlm.nih.gov/pubmed/12954995.
55: Kochanek, D. M., S. M. Ghouse, M. M. Karbowniczek and M. M. Markiewski. "Complementing cancer metastasis." Front Immunol 9 (2018): 1629. 10.3389/fimmu.2018.01629. https://www.ncbi.nlm.nih.gov/pubmed/30061895.
56: Lee, Y. T. "Quantitative change of serum protein and immunoglobulin in patients with solid cancers." J Surg Oncol 9 (1977): 179-87. 10.1002/jso.2930090212. https://www.ncbi.nlm.nih.gov/pubmed/68175.
57: Seve, P., I. Ray-Coquard, V. Trillet-Lenoir, M. Sawyer, J. Hanson, C. Broussolle, S. Negrier, C. Dumontet and J. R. Mackey. "Low serum albumin levels and liver metastasis are powerful prognostic markers for survival in patients with carcinomas of unknown primary site." Cancer 107 (2006): 2698-705. 10.1002/cncr.22300. https://www.ncbi.nlm.nih.gov/pubmed/17063500.
58: Pleiman, C. M., D. D'Ambrosio and J. C. Cambier. "The b-cell antigen receptor complex: Structure and signal transduction." Immunol Today 15 (1994): 393-9. 10.1016/0167-5699(94)90267-4.
59: DeFranco, A. L. "Structure and function of the b cell antigen receptor." Annu Rev Cell Biol 9 (1993): 377-410. 10.1146/annurev.cb.09.110193.002113.
60: Li, X., R. Ni, J. Chen, Z. Liu, M. Xiao, F. Jiang and C. Lu. "The presence of ighg1 in human pancreatic carcinomas is associated with immune evasion mechanisms." Pancreas 40 (2011): 753-61. 10.1097/MPA.0b013e318213d51b. https://www.ncbi.nlm.nih.gov/pubmed/21654544.
61: Wan, X., Y. Lei, Z. Li, J. Wang, Z. Chen, M. McNutt, D. Lin, C. Zhao, C. Jiang, J. Li, et al. "Pancreatic expression of immunoglobulin g in human pancreatic cancer and associated diabetes." Pancreas 44 (2015): 1304-13. 10.1097/MPA.0000000000000544. https://www.ncbi.nlm.nih.gov/pubmed/26390427.
62: Fujie, T., M. Mori, H. Ueo, K. Sugimachi and T. Akiyoshi. "Expression of mage and bage genes in japanese breast cancers." Annals of Oncology 8 (1997): 369-72. 10.1023/A:1008255630202. https://doi.org/10.1023/A:1008255630202.
63: Kubuschok, B., X. Xie, R. Jesnowski, K.-D. Preuss, B. F. M. Romeike, F. Neumann, E. Regitz, G. Pistorius, M. Schilling, P. Scheunemann, et al. "Expression of cancer testis antigens in pancreatic carcinoma cell lines, pancreatic adenocarcinoma and chronic pancreatitis." Int J Cancer 109 (2004): 568-75. https://doi.org/10.1002/ijc.20006. https://onlinelibrary.wiley.com/doi/abs/10.1002/ijc.20006.
64: Rogner, U. C., K. Wilke, E. Steck, B. Korn and A. Poustka. "The melanoma antigen gene (mage) family is clustered in the chromosomal band xq28." Genomics 29 (1995): 725-31. 10.1006/geno.1995.9945.
65: Shantha Kumara, H. M. C., M. J. Grieco, O. L. Caballero, T. Su, A. Ahmed, E. Ritter, S. Gnjatic, V. Cekic, L. J. Old, A. J. Simpson, et al. "Mage-a3 is highly expressed in a subset of colorectal cancer patients." Cancer immunity 12 (2012): 16-16. https://pubmed.ncbi.nlm.nih.gov/23390371
66: Sienel, W., C. Varwerk, A. Linder, D. Kaiser, M. Teschner, M. Delire, G. Stamatis and B. Passlick. "Melanoma associated antigen (mage)-a3 expression in stages i and ii non-small cell lung cancer: Results of a multi-center study." European Journal of Cardio-Thoracic Surgery 25 (2004): 131-34. 10.1016/j.ejcts.2003.09.015. https://doi.org/10.1016/j.ejcts.2003.09.015.
67: Kim, J., H. A. Reber, O. J. Hines, K. K. Kazanjian, A. Tran, X. Ye, F. F. Amersi, S. R. Martinez, S. M. Dry, A. J. Bilchik, et al. "The clinical significance of magea3 expression in pancreatic cancer." Int J Cancer 118 (2006): 2269-75. https://doi.org/10.1002/ijc.21656. https://onlinelibrary.wiley.com/doi/abs/10.1002/ijc.21656.
68: Das, B. and S. Senapati. "Functional and mechanistic studies reveal magea3 as a pro-survival factor in pancreatic cancer cells." Journal of Experimental & Clinical Cancer Research 38 (2019): 294. 10.1186/s13046-019-1272-2. https://doi.org/10.1186/s13046-019-1272-2.
69: Poojary, M., P. V. Jishnu and S. P. Kabekkodu. "Prognostic value of melanoma-associated antigen-a (mage-a) gene expression in various human cancers: A systematic review and meta-analysis of 7428 patients and 44 studies." Molecular Diagnosis & Therapy 24 (2020): 537-55. 10.1007/s40291-020-00476-5. https://doi.org/10.1007/s40291-020-00476-5.
70: Padoan, A., M. Plebani and D. Basso. "Inflammation and pancreatic cancer: Focus on metabolism, cytokines, and immunity." Int J Mol Sci 20 (2019): 676. 10.3390/ijms20030676. https://pubmed.ncbi.nlm.nih.gov/30764482

Claims (13)

  1. Method for monitoring tumor burden in subjects during therapeutic intervention characterized in that it comprises the steps of:
    1. collection of a blood sample from the subject;
    2. recording of the clinical data;
    3. centrifuging the blood sample in a centrifuge and processing of the plasma sample for extracellular vesicle staining followed by qualitative and quantitative determination of the population of vesicle-associated proteins; and
    4. data processing to assess the subject’s response to therapy.
  2. Method, according to claim 1, characterized in that, in step (a), the blood sample is collected from a liquid biopsy of the tumor every time the patient came to a follow-up visit.
  3. Method, according to claim 1, characterized in that, in step (b), for each blood sample collection, the clinical data is registered.
  4. Method, according to claim 3, characterized in that the clinical data is at least one of the groups consisting of Magnetic Resonance Imaging / Computer Tomography or conventional biomarkers of the disease to be tested.
  5. Method, according to claim 4, characterized in that the disease to be tested is pancreatic ductal adenocarcinoma and the biomarker is CA19.9.
  6. Method, according to claim 1, characterized in that, in step (c), the blood sample is centrifuged twice in a temperature range from 4 to 10 ºC and a centrifugal force in the range from 500 g to 3000 g for a time range from 10 to 20 minutes and the plasma-derived extracellular vesicle sample obtained is analyzed by Nanoparticle Tracking Analysis and Vesicle Flow Cytometry.
  7. Method, according to claim 6, characterized in that the plasma-derived extracellular vesicle sample is diluted in filtered phosphate-buffered saline prior to the Nanoparticle Tracking Analysis.
  8. Method, according to claim 6, characterized in that the plasma-derived extracellular vesicle sample is submitted to two incubation substeps to stain extracellular vesicles prior to the Vesicle Flow Cytometry analysis.
  9. Method, according to claim 8, characterized in that the first incubation substep comprises staining the plasma-derived extracellular vesicle sample with anti-IgG in phosphate-buffered saline for a time range from 1 to 3 h and in a temperature range from 20 to 37 °C.
  10. Method, according to claim 8, characterized in that the second incubation substep comprises adding an extracellular vesicle specific fluorescent dye to the antibody-stained sample for a time range from 1 to 2 h in a temperature range from 20 to 37 °C.
  11. Method, according to claim 8, characterized in that the extracellular vesicle specific fluorescent dye is Carboxyfluorescein Diacetate Succinimidyl Ester.
  12. Method, according to claims 8 to 11, characterized in that the unbound extracellular vesicle specific fluorescent dye and antibody are removed prior to the Vesicle Flow Cytometry by Size Exclusion Chromatography.
  13. Method, according to claim 1, characterized in that, in step (d), the treatment of the data consists of monitoring the levels of extracellular vesicles that express IgG throughout the treatment of the patients.
PCT/IB2023/058146 2022-09-01 2023-08-11 Method for monitoring tumor burden in subjects during therapeutic intervention Ceased WO2024047441A1 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
PT11817922 2022-09-01
PT118179 2022-09-01

Publications (1)

Publication Number Publication Date
WO2024047441A1 true WO2024047441A1 (en) 2024-03-07

Family

ID=87845713

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/IB2023/058146 Ceased WO2024047441A1 (en) 2022-09-01 2023-08-11 Method for monitoring tumor burden in subjects during therapeutic intervention

Country Status (1)

Country Link
WO (1) WO2024047441A1 (en)

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2718721A1 (en) 2011-06-07 2014-04-16 Caris Life Sciences Luxembourg Holdings, S.a.r.l. Circulating biomarkers for cancer
US20140186293A1 (en) * 2012-12-31 2014-07-03 University Of Louisville Research Foundation, Inc. Immunoglobulin-bound extracellular vesicles and uses thereof
EP2176665B1 (en) 2007-08-16 2016-03-02 The Royal Institution for the Advancement of Learning / McGill University Tumor cell-derived microvesicles
EP2542696B1 (en) 2010-03-01 2016-09-28 Caris Life Sciences Switzerland Holdings GmbH Biomarkers for theranostics
US20180045728A1 (en) * 2013-12-04 2018-02-15 Board Of Regents, The University Of Texas System Analysis of genomic dna, rna, and proteins in exosomes for diagnosis and theranosis
WO2020163591A1 (en) * 2019-02-06 2020-08-13 Beth Israel Deaconess Medical Center, Inc. Compositions and methods for characterizing pancreatic ductal adenocarcinoma
WO2022046576A1 (en) 2020-08-22 2022-03-03 The Broad Institute, Inc. Pancreatic ductal adenocarcinoma signatures and uses thereof
US20220118452A1 (en) * 2020-10-20 2022-04-21 The Board Of Trustees Of The Leland Stanford Junior University Isolation of Different Extracellular Vesicle (EV) Subpopulations

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2176665B1 (en) 2007-08-16 2016-03-02 The Royal Institution for the Advancement of Learning / McGill University Tumor cell-derived microvesicles
EP2542696B1 (en) 2010-03-01 2016-09-28 Caris Life Sciences Switzerland Holdings GmbH Biomarkers for theranostics
EP2718721A1 (en) 2011-06-07 2014-04-16 Caris Life Sciences Luxembourg Holdings, S.a.r.l. Circulating biomarkers for cancer
US20140186293A1 (en) * 2012-12-31 2014-07-03 University Of Louisville Research Foundation, Inc. Immunoglobulin-bound extracellular vesicles and uses thereof
US20180045728A1 (en) * 2013-12-04 2018-02-15 Board Of Regents, The University Of Texas System Analysis of genomic dna, rna, and proteins in exosomes for diagnosis and theranosis
WO2020163591A1 (en) * 2019-02-06 2020-08-13 Beth Israel Deaconess Medical Center, Inc. Compositions and methods for characterizing pancreatic ductal adenocarcinoma
WO2022046576A1 (en) 2020-08-22 2022-03-03 The Broad Institute, Inc. Pancreatic ductal adenocarcinoma signatures and uses thereof
US20220118452A1 (en) * 2020-10-20 2022-04-21 The Board Of Trustees Of The Leland Stanford Junior University Isolation of Different Extracellular Vesicle (EV) Subpopulations

Non-Patent Citations (70)

* Cited by examiner, † Cited by third party
Title
ALLIN, K. H.B. G. NORDESTGAARD: "Elevated c-reactive protein in the diagnosis, prognosis, and cause of cancer", CRIT REV CLIN LAB SCI, vol. 48, 2011, pages 155 - 70, Retrieved from the Internet <URL:https://www.ncbi.nlm.nih.gov/pubmed/22035340>
AZIZIAN, A.F. RUHLMANNT. KRAUSEM. BERNHARDTP. JOA. KONIGM. KLEISSA. LEHAM. GHADIMIJ. GAEDCKE: "Cal9-9 for detecting recurrence of pancreatic cancer", SCI REP, vol. 10, 2020, pages 1332, Retrieved from the Internet <URL:https://www.ncbi.nlm.nih.gov/pubmed/31992753>
BERNARD, V., D. U. KIM, F. A. SAN LUCAS, J. CASTILLO, K. ALLENSON, F. C. MULU, B. M. STEPHENS, J. HUANG, A. SEMAAN, P. A. GUERRERO: "Circulating nucleic acids are associated with outcomes of patients with pancreatic cancer", GASTROENTEROLOGY, vol. 156, 2019, pages 108 - 18
BOULAY, B. R.M. PAREPALLY: "Managing malignant biliary obstruction in pancreas cancer: Choosing the appropriate strategy", WORLD J GASTROENTEROL, vol. 20, 2014, pages 9345 - 53
BUZAS, E. I.B. GYORGYG. NAGYA. FALUSS. GAY: "Emerging role of extracellular vesicles in inflammatory diseases", NAT REV RHEUMATOL, vol. 10, 2014, pages 356 - 64, XP055418387, Retrieved from the Internet <URL:https://www.ncbi.nlm.nih.gov/pubmed/24535546> DOI: 10.1038/nrrheum.2014.19
BUZAS, E. I.E. A. TOTHB. W. SODARK. E. SZABO-TAYLOR: "Molecular interactions at the surface of extracellular vesicles", SEMIN IMMUNOPATHOL, vol. 40, 2018, pages 453 - 64, XP036621174, DOI: 10.1007/s00281-018-0682-0
CAPELLO, M.J. V. VYKOUKALH. KATAYAMAL. E. BANTISH. WANGD. L. KUNDNANIC. AGUILAR-BONAVIDESM. AGUILARS. C. TRIPATHID. S. DHILLON ET : "Exosomes harbor b cell targets in pancreatic adenocarcinoma and exert decoy function against complement-mediated cytotoxicity", NAT COMMUN, vol. 10, 2019, pages 254
CARIOLI, G.M. MALVEZZIP. BERTUCCIOP. BOFFETTAF. LEVIC. LA VECCHIAE. NEGRI: "European cancer mortality predictions for the year 2021 with focus on pancreatic and female lung cancer", ANN ONCOL, vol. 32, 2021, pages 478 - 87, XP009532772, DOI: 10.1016/j.annonc.2021.01.006
CARVALHO, A. S.H. BAETAA. F. A. HENRIQUESM. EJTEHADIFARE. M. TRANFIELDA. L. SOUSAA. FARINHOB. C. SILVAJ. CABECADASP. GAMEIRO ET AL: "Proteomic landscape of extracellular vesicles for diffuse large b-cell lymphoma subtyping", INT J MOL SCI, vol. 22, 2021
CARVALHO, A. S.H. RIBEIROP. VOABILD. PENQUEO. N. JENSENH. MOLINAR. MATTHIESEN: "Global mass spectrometry and transcriptomics array based drug profiling provides novel insight into glucosamine induced endoplasmic reticulum stress", MOL CELL PROTEOMICS, vol. 13, 2014, pages 3294 - 307
CLOUTIER, N., S. TAN, L. H. BOUDREAU, C. CRAMB, R. SUBBAIAH, L. LAHEY, A. ALBERT, R. SHNAYDER, R. GOBEZIE, P. A. NIGROVIC: "The exposure of autoantigens by microparticles underlies the formation of potent inflammatory components: The microparticle-associated immune complexes", EMBO MOL MED, vol. 5, 2013, pages 235 - 49, Retrieved from the Internet <URL:https://www.ncbi.nlm.nih.gov/pubmed/23165896>
CONROY, T.F. DESSEIGNEM. YCHOUO. BOUCHER. GUIMBAUDY. BECOUARNA. ADENISJ. L. RAOULS. GOURGOU-BOURGADEC. DE LA FOUCHARDIÈRE ET AL.: "Folfirinox versus gemcitabine for metastatic pancreatic cancer", N ENGL J MED, vol. 364, 2011, pages 1817 - 25, XP002730536, DOI: 10.1056/NEJMoa1011923
COSTA-SILVA, B.N. M. AIELLOA. J. OCEANS. SINGHH. ZHANGBASANT K. THAKURA. BECKERA. HOSHINOM. T. MARKH. MOLINA ET AL.: "Pancreatic cancer exosomes initiate pre-metastatic niche formation in the liver", NATURE CELL BIOLOGY, vol. 17, 2015, pages 816 - 26, Retrieved from the Internet <URL:https://doi.org/10.1038/ncb3169>
COUTO, N.S. CAJAJ. MAIAM. C. STRANO MORAESB. COSTA-SILVA: "Exosomes as emerging players in cancer biology", BIOCHIMIE, vol. 155, 2018, pages 2 - 10
COX, J.M. MANN: "Maxquant enables high peptide identification rates, individualized p.P.B.-range mass accuracies and proteome-wide protein quantification", NAT BIOTECHNOL, vol. 26, 2008, pages 1367 - 72, XP055527588, DOI: 10.1038/nbt.1511
CUI, M.J. HUANGS. ZHANGQ. LIUQ. LIAOX. QIU: "Immunoglobulin expression in cancer cells and its critical roles in tumorigenesis", FRONT IMMUNOL, vol. 12, 2021, pages 613530
CUI, M.L. YOUB. ZHENGX. HUANGQ. LIUJ. HUANGB. PANX. QIUQ. LIAOY. ZHAO: "High expression of cancer-derived glycosylated immunoglobulin g predicts poor prognosis in pancreatic ductal adenocarcinoma", J CANCER, vol. 11, 2020, pages 2213 - 21
CVJETKOVIC, A.S. C. JANGB. KONECNAJ. L. HOOGC. SIHLBOMC. LASSERJ. LOTVALL: "Detailed analysis of protein topology of extracellular vesicles-evidence of unconventional membrane protein orientation", SCI REP, vol. 6, 2016, pages 36338, XP055537484, DOI: 10.1038/srep36338
DAAMEN, L. A.V. P. GROOTH. D. HEERKENSM. P. W. INTVENH. C. VAN SANTVOORTI. Q. MOLENAAR: "Systematic review on the role of serum tumor markers in the detection of recurrent pancreatic cancer", HPB, vol. 20, 2018, pages 297 - 304
DAS, B.S. SENAPATI: "Functional and mechanistic studies reveal magea3 as a pro-survival factor in pancreatic cancer cells", JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH, vol. 38, 2019, pages 294, Retrieved from the Internet <URL:https://doi.org/10.1186/s13046-019-1272-2>
DAWSON, S. J.D. W. TSUIM. MURTAZAH. BIGGSO. M. RUEDAS. F. CHINM. J. DUNNINGD. GALET. FORSHEWB. MAHLER-ARAUJO ET AL.: "Analysis of circulating tumor DNA to monitor metastatic breast cancer", N ENGL J MED, vol. 368, 2013, pages 1199 - 209, XP055494258, DOI: 10.1056/NEJMoa1213261
DEFRANCO, A. L.: "Structure and function of the b cell antigen receptor", ANNU REV CELL BIOL, vol. 9, 1993, pages 377 - 410
DONATI, M. B.R. LORENZET: "Coagulation factors and tumor cell biology: The role of tissue factor", PATHOPHYSIOL HAEMOST THROMB, vol. 33, 2003, pages 22 - 5, Retrieved from the Internet <URL:https://www.ncbi.nlm.nih.gov/pubmed/12954995>
EISENHAUER, E. A.P. THERASSEJ. BOGAERTSL. H. SCHWARTZD. SARGENTR. FORDJ. DANCEYS. ARBUCKS. GWYTHERM. MOONEY ET AL.: "New response evaluation criteria in solid tumours: Revised recist guideline (version 1.1", EUR J CANCER, vol. 45, 2009, pages 228 - 47, XP025841550, DOI: 10.1016/j.ejca.2008.10.026
FORTIN, P. R.N. CLOUTIERV. BISSONNETTEE. AGHDASSIL. EDERD. SIMONYANN. LAFLAMMEE. BOILARD: "Distinct subtypes of microparticle-containing immune complexes are associated with disease activity, damage, and carotid intima-media thickness in systemic lupus erythematosus", J RHEUMATOL, vol. 43, 2016, pages 2019 - 25, Retrieved from the Internet <URL:https://www.ncbi.nlm.nih.gov/pubmed/27585687>
FUJIE, T.M. MORIH. UEOK. SUGIMACHIT. AKIYOSHI: "Expression of mage and bage genes in japanese breast cancers", ANNALS OF ONCOLOGY, vol. 8, 1997, pages 369 - 72, Retrieved from the Internet <URL:https://doi.org/10.1023/A:1008255630202>
GRANSMARK, E.N. BAGENHOLM BYLINH. BLOMSTRANDM. FREDRIKSONE. AVALL-LUNDQVISTN. O. ELANDER: "Real world evidence on second-line palliative chemotherapy in advanced pancreatic cancer", FRONTIERS IN ONCOLOGY, vol. 10, 2020
HOFMANN, L.S. LUDWIGP. J. SCHULERT. K. HOFFMANNC. BRUNNERM. N. THEODORAKI: "The potential of cdl6 on plasma-derived exosomes as a liquid biomarker in head and neck cancer", INT J MOL SCI, vol. 21, 2020
HOLMBERG, A.A. BLOMSTERGRENO. NORDM. LUKACSJ. LUNDEBERGM. UHLEN: "The biotin-streptavidin interaction can be reversibly broken using water at elevated temperatures", ELECTROPHORESIS, vol. 26, 2005, pages 501 - 10, XP055011839, DOI: 10.1002/elps.200410070
HOSHINO, A.B. COSTA-SILVAT.-L. SHENG. RODRIGUESA. HASHIMOTOM. TESIC MARKH. MOLINAS. KOHSAKAA. DI GIANNATALES. CEDER ET AL.: "Tumour exosome integrins determine organotropic metastasis", NATURE, vol. 527, 2015, pages 329 - 35, XP055448126, DOI: 10.1038/nature15756
HOSHINO, A.H. S. KIML. BOJMARK. E. GYANM. CIOFFIJ. HERNANDEZC. P. ZAMBIRINISG. RODRIGUESH. MOLINAS. HEISSEL ET AL.: "Extracellular vesicle and particle biomarkers define multiple human cancers", CELL, vol. 182, 2020, pages 1044 - 61
HOU, J.X. LIK.-P. XIE: "Coupled liquid biopsy and bioinformatics for pancreatic cancer early detection and precision prognostication", MOLECULAR CANCER, vol. 20, 2021, pages 34
KAHLERT, C.S. A. MELOA. PROTOPOPOVJ. TANGS. SETHM. KOCHJ. ZHANGJ. WEITZL. CHINA. FUTREAL ET AL.: "Identification of double-stranded genomic DNA spanning all chromosomes with mutated kras and p53 DNA in the serum exosomes of patients with pancreatic cancer", J BIOL CHEM, vol. 289, 2014, pages 3869 - 75, XP055541056, DOI: 10.1074/jbc.C113.532267
KIM, J.H. A. REBERO. J. HINESK. K. KAZANJIANA. TRANX. YEF. F. AMERSIS. R. MARTINEZS. M. DRYA. J. BILCHIK ET AL.: "The clinical significance of magea3 expression in pancreatic cancer", INT J CANCER, vol. 118, 2006, pages 2269 - 75, XP071283258, Retrieved from the Internet <URL:https://doi.org/10.1002/ijc.21656> DOI: 10.1002/ijc.21656
KOCHANEK, D. M.S. M. GHOUSEM. M. KARBOWNICZEKM. M. MARKIEWSKI: "Complementing cancer metastasis", FRONT IMMUNOL, vol. 9, 2018, pages 1629, Retrieved from the Internet <URL:https://www.ncbi.nlm.nih.gov/pubmed/30061895>
KUBUSCHOK, B.X. XIER. JESNOWSKIK.-D. PREUSSB. F. M. ROMEIKEF. NEUMANNE. REGITZG. PISTORIUSM. SCHILLINGP. SCHEUNEMANN ET AL.: "Expression of cancer testis antigens in pancreatic carcinoma cell lines, pancreatic adenocarcinoma and chronic pancreatitis", INT J CANCER, vol. 109, 2004, pages 568 - 75, XP071282186, DOI: 10.1002/ijc.20006
LEE, Y. T.: "Quantitative change of serum protein and immunoglobulin in patients with solid cancers", J SURG ONCOL, vol. 9, 1977, pages 179 - 87, Retrieved from the Internet <URL:https://www.ncbi.nlm.nih.gov/pubmed/68175>
LI, X.R. NIJ. CHENZ. LIUM. XIAOF. JIANGC. LU: "The presence of ighgl in human pancreatic carcinomas is associated with immune evasion mechanisms", PANCREAS, vol. 40, 2011, pages 753 - 61, Retrieved from the Internet <URL:https://www.ncbi.nlm.nih.gov/pubmed/21654544>
LIANG KAI ET AL: "Nanoplasmonic quantification of tumour-derived extracellular vesicles in plasma microsamples for diagnosis and treatment monitoring", NATURE BIOMEDICAL ENGINEERING, vol. 1, no. 4, 6 February 2017 (2017-02-06), XP093086744, Retrieved from the Internet <URL:https://www.nature.com/articles/s41551-016-0021> DOI: 10.1038/s41551-016-0021 *
LIANG, K.F. LIUJ. FAND. SUNC. LIUC. J. LYOND. W. BERNARDY. LIK. YOKOIM. H. KATZ ET AL.: "Nanoplasmonic quantification of tumour-derived extracellular vesicles in plasma microsamples for diagnosis and treatment monitoring", NATURE BIOMEDICAL ENGINEERING, vol. 1, 2017, pages 0021, Retrieved from the Internet <URL:https://doi.org/10.1038/s41551-016-0021>
LIU, C.S. DENGK. JINY. GONGH. CHENGZ. FANY. QIANQ. HUANGQ. NIG. LUO ET AL.: "Lewis antigen-negative pancreatic cancer: An aggressive subgroup", INT J ONCOL, vol. 56, 2020, pages 900 - 08
MAIA, J.A. H. OTAKEJ. POCASA. S. CARVALHOH. C. BECKA. MAGALHAESR. MATTHIESENM. C. STRANO MORAESB. COSTA-SILVA: "Transcriptome reprogramming of cdl lb(+) bone marrow cells by pancreatic cancer extracellular vesicles", FRONT CELL DEV BIOL, vol. 8, 2020, pages 592518
MAIA, J.S. BATISTAN. COUTOA. C. GREGORIOC. BODOJ. ELZANOWSKAM. C. STRANO MORAESB. COSTA-SILVA: "Employing flow cytometry to extracellular vesicles sample micro volume analysis and quality control", FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY, vol. 8, 2020
MAROM, E. M., S. SARVIS, J. E. HERNDON, 2ND AND E. F. PATZ, JR.: "T1 lung cancers: Sensitivity of diagnosis with fluorodeoxyglucose pet", RADIOLOGY, vol. 223, 2002, pages 453 - 9
MATTHIESEN, R.G. PRIETOA. AMORIMK. ALORIAA. FULLAONDOA. S. CARVALHOJ. M. ARIZMENDI: "Sir: Deterministic protein inference from peptides assigned to ms data", J PROTEOMICS, vol. 75, 2012, pages 4176 - 83, XP028497098, DOI: 10.1016/j.jprot.2012.05.010
MEI, Z.L. SHIB. WANGJ. YANGZ. XIAOP. DUQ. WANGW. YANG: "Prognostic role of pretreatment blood neutrophil-to-lymphocyte ratio in advanced cancer survivors: A systematic review and meta-analysis of 66 cohort studies", CANCER TREATMENT REVIEWS, vol. 58, 2017, pages 1 - 13, XP085133901, Retrieved from the Internet <URL:https://doi.org/10.1016/j.ctrv.2017.05.005> DOI: 10.1016/j.ctrv.2017.05.005
NUZHAT, Z.V. KINHALS. SHARMAG. E. RICEV. JOSHIC. SALOMON: "Tumour-derived exosomes as a signature of pancreatic cancer - liquid biopsies as indicators of tumour progression", ONCOTARGET, vol. 8, 2017, pages 17279 - 91, XP055845560, DOI: 10.18632/oncotarget.13973
PADOAN, A., M. PLEBANI AND D. BASSO: "Inflammation and pancreatic cancer: Focus on metabolism, cytokines, and immunity", INT J MOL SCI, vol. 20, 2019, pages 676
PEREZ-HERNANDEZ, J.J. REDONR. CORTES: "Extracellular vesicles as therapeutic agents in systemic lupus erythematosus", INT J MOL SCI, vol. 18, 2017
PLEIMAN, C. M.D. D'AMBROSIOJ. C. CAMBIER: "The b-cell antigen receptor complex: Structure and signal transduction", IMMUNOL TODAY, vol. 15, 1994, pages 393 - 9, XP023938296, DOI: 10.1016/0167-5699(94)90267-4
POOJARY, M.P. V. JISHNUS. P. KABEKKODU: "Prognostic value of melanoma-associated antigen-a (mage-a) gene expression in various human cancers: A systematic review and meta-analysis of 7428 patients and 44 studies", MOLECULAR DIAGNOSIS & THERAPY, vol. 24, 2020, pages 537 - 55
RAHIB, L., B. D. SMITH, R. AIZENBERG, A. B. ROSENZWEIG, J. M. FLESHMAN AND L. M. MATRISIAN: "Projecting cancer incidence and deaths to 2030: The unexpected burden of thyroid, liver, and pancreas cancers in the United States", CANCER RESEARCH, vol. 74, 2014, pages 2913 - 21
RECORD, M.K. CARAYONM. POIROTS. SILVENTE-POIROT: "Exosomes as new vesicular lipid transporters involved in cell-cell communication and various pathophys-iologies", BIOCHIM BIOPHYS ACTA, vol. 1841, 2014, pages 108 - 20, Retrieved from the Internet <URL:http://www.ncbi.nlm.nih.gov/pubmed/24140720>
ROBINSON, J. L.A. FEIZIM. UHLENJ. NIELSEN: "A systematic investigation of the malignant functions and diagnostic potential of the cancer secretome", CELL REP, vol. 26, 2019, pages 2622 - 35
ROGNER, U. C.K. WILKEE. STECKB. KORNA. POUSTKA: "The melanoma antigen gene (mage) family is clustered in the chromosomal band xq28", GENOMICS, vol. 29, 1995, pages 725 - 31
SCARA, S.P. BOTTOMR. SCATENA: "Ca 19-9: Biochemical and clinical aspects", ADV EXP MED BIOL, vol. 867, 2015, pages 247 - 60
SCHMITZ-WINNENTHAL, F. H., L. V. GALINDO-ESCOBEDO, D. RIMOLDI, W. GENG, P. ROMERO, M. KOCH, J. WEITZ, R. KREMPIEN, A. G. NIETHAMME: "Potential target antigens for immunotherapy in human pancreatic cancer", CANCER LETT, vol. 252, 2007, pages 290 - 8, XP025319758, DOI: 10.1016/j.canlet.2007.01.003
SEVE, P.I. RAY-COQUARDV. TRILLET-LENOIRM. SAWYERJ. HANSONC. BROUSSOLLES. NEGRIERC. DUMONTETJ. R. MACKEY: "Low serum albumin levels and liver metastasis are powerful prognostic markers for survival in patients with carcinomas of unknown primary site", CANCER, vol. 107, 2006, pages 2698 - 705, Retrieved from the Internet <URL:https://www.ncbi.nlm.nih.gov/pubmed/17063500>
SHANTHA KUMARA, H. M. C.M. J. GRIECOO. L. CABALLEROT. SUA. AHMEDE. RITTERS. GNJATICV. CEKICL. J. OLDA. J. SIMPSON ET AL.: "Mage-a3 is highly expressed in a subset of colorectal cancer patients", CANCER IMMUNITY, vol. 12, 2012, pages 16 - 16
SIEGEL, R. L.K. D. MILLERH. E. FUCHSA. JEMAL: "Cancer statistics, 2021", CA: A CANCER JOURNAL FOR CLINICIANS, vol. 71, 2021, pages 7 - 33
SIENEL, W.C. VARWERKA. LINDERD. KAISERM. TESCHNERM. DELIREG. STAMATISB. PASSLICK: "Melanoma associated antigen (mage)-a3 expression in stages i and ii non-small cell lung cancer: Results of a multi-center study", EUROPEAN JOURNAL OF CARDIO-THORACIC SURGERY, vol. 25, 2004, pages 131 - 34, Retrieved from the Internet <URL:https://doi.org/10.1016/j.ejcts.2003.09.015>
SKOG, J.T. WURDINGERS. VAN RIJND. H. MEIJERL. GAINCHEW. T. CURRYB. S. CARTERA. M. KRICHEVSKYX. O. BREAKEFIELD: "Glioblastoma microvesicles transport rna and proteins that promote tumour growth and provide diagnostic biomarkers", NATURE CELL BIOLOGY, vol. 10, 2008, pages 1470 - 76, XP002633335, Retrieved from the Internet <URL:https://doi.org/10.1038/ncb1800> DOI: 10.1038/ncb1800
SUNG, H.J. FERLAYR. L. SIEGELM. LAVERSANNEI. SOERJOMATARAMA. JEMALF. BRAY: "Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries", CA: A CANCER JOURNAL FOR CLINICIANS, vol. 71, 2021, pages 209 - 49, Retrieved from the Internet <URL:https://doi.org/10.3322/caac.21660>
TAYLOR, D. D.C. GERCEL-TAYLOR: "Microrna signatures of tumor-derived exosomes as diagnostic biomarkers of ovarian cancer", GYNECOL ONCOL, vol. 110, 2008, pages 13 - 21, XP022795052, DOI: 10.1016/j.ygyno.2008.04.033
THAKUR, B. K.H. ZHANGA. BECKERI. MATEIY. HUANGB. COSTA-SILVAY. ZHENGA. HOSHINOH. BRAZIERJ. XIANG ET AL.: "Double-stranded DNA in exosomes: A novel biomarker in cancer detection", CELL RESEARCH, vol. 24, 2014, pages 766 - 69, XP055240739, Retrieved from the Internet <URL:https://doi.org/10.1038/cr.2014.44> DOI: 10.1038/cr.2014.44
TIRKES, T.M. A. HOLLARM. TANNM. D. KOHLIF. AKISIKK. SANDRASEGARAN: "Response criteria in oncologic imaging: Review of traditional and new criteria", RA-DIOGRAPHICS, vol. 33, 2013, pages 1323 - 41, XP055232120, DOI: 10.1148/rg.335125214
VON HOFF, D. D.T. ERVINF. P. ARENAE. G. CHIOREANJ. INFANTEM. MOORET. SEAYS. A. TJULANDINW. W. MAM. N. SALEH ET AL.: "Increased survival in pancreatic cancer with nab-paclitaxel plus gemcitabine", N ENGL J MED, vol. 369, 2013, pages 1691 - 703, XP055250743, DOI: 10.1056/NEJMoa1304369
WALKER, C.E. MOJARESA. DEL RIO HERNANDEZ: "Role of extracellular matrix in development and cancer progression", INT J MOL SCI, vol. 19, 2018
WAN, X.Y. LEIZ. LIJ. WANGZ. CHENM. MCNUTTD. LINC. ZHAOC. JIANGJ. LI ET AL.: "Pancreatic expression of immunoglobulin g in human pancreatic cancer and associated diabetes", PANCREAS, vol. 44, 2015, pages 1304 - 13
WISNIEWSKI, J. R.A. ZOUGMANN. NAGARAJM. MANN: "Universal sample preparation method for proteome analysis", NAT METHODS, vol. 6, 2009, pages 359 - 62, XP055527538, DOI: 10.1038/nmeth.1322

Similar Documents

Publication Publication Date Title
Hu et al. Salivary proteomics for oral cancer biomarker discovery
Salmond et al. Nanoscale flow cytometry for immunophenotyping and quantitating extracellular vesicles in blood plasma
Sepiashvili et al. Potentially novel candidate biomarkers for head and neck squamous cell carcinoma identified using an integrated cell line-based discovery strategy
Ummanni et al. Prohibitin identified by proteomic analysis of prostate biopsies distinguishes hyperplasia and cancer
CN104204807B (en) Biomarker and application thereof for cancer of the stomach
Dowling et al. 2‐D difference gel electrophoresis of the lung squamous cell carcinoma versus normal sera demonstrates consistent alterations in the levels of ten specific proteins
Melo et al. Glypican1 identifies cancer exosomes and facilitates early detection of cancer
Capello et al. Autoantibodies to Ezrin are an early sign of pancreatic cancer in humans and in genetically engineered mouse models
JP5863074B2 (en) Markers for detecting pancreatic cancer
US20140121127A1 (en) Methods and Compositions for Diagnosis of Ovarian Cancer
Tsai et al. Systematic verification of bladder cancer-associated tissue protein biomarker candidates in clinical urine specimens
Vyhlídalová Kotrbová et al. Proteomic analysis of ascitic extracellular vesicles describes tumour microenvironment and predicts patient survival in ovarian cancer
ES2953011T3 (en) Agrin as a marker of endometrial cancer
Zheng et al. Up‐regulation of HLA‐G expression in cervical premalignant and malignant lesions
US20170322216A1 (en) Pancreatic cancer diagnostic
Wang et al. Different expression of S100A8 in malignant and benign gallbladder diseases
WO2024047441A1 (en) Method for monitoring tumor burden in subjects during therapeutic intervention
US8492104B2 (en) Methods and kits to detect and monitor ovarian cancer and preeclampsia
US20150338412A1 (en) Composition for diagnosis of lung cancer and diagnosis kit for lung cancer
EP2551673B1 (en) Methods for the detection of cancer infiltration of the central nervous system
WO2009034562A2 (en) A method of assessing lung squamous cell carcinoma status in an individual
US20180188255A1 (en) Method for the in vitro diagnosis of pancreatic ductal adenocarcinoma or for determining the predisposition to pancreatic ductal adenocarcinoma
WO2024020173A1 (en) Methods of detecting and treating hepatocellular carcinoma
JP2023531860A (en) Biomarkers for lung cancer detection
Janan et al. Serum adhesion molecule-1 (ICAM-1) as a potential prognostic marker for cholangiocarcinoma patients

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 23761588

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 23761588

Country of ref document: EP

Kind code of ref document: A1