WO2015164772A1 - Protéines associées au facteur de croissance analogue à l'insuline (igf) en circulation pour la détection du cancer du poumon - Google Patents

Protéines associées au facteur de croissance analogue à l'insuline (igf) en circulation pour la détection du cancer du poumon Download PDF

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WO2015164772A1
WO2015164772A1 PCT/US2015/027562 US2015027562W WO2015164772A1 WO 2015164772 A1 WO2015164772 A1 WO 2015164772A1 US 2015027562 W US2015027562 W US 2015027562W WO 2015164772 A1 WO2015164772 A1 WO 2015164772A1
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igfbp
panel
biomarkers
lung cancer
igf
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Jeffrey A. Borgia
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Rush University Medical Center
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    • 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
    • G01N33/543—Immunoassay; Biospecific binding assay; Materials therefor with an insoluble carrier for immobilising immunochemicals
    • G01N33/54366—Apparatus specially adapted for solid-phase testing
    • G01N33/54373—Apparatus specially adapted for solid-phase testing involving physiochemical end-point determination, e.g. wave-guides, FETS, gratings
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/112—Disease subtyping, staging or classification
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/60—Complex ways of combining multiple protein biomarkers for diagnosis

Definitions

  • the present invention relates to a method and a kit for assigning clinical significance to indeterminate lung nodules and for a primary screen in assessing high risk subjects, and in particular to a method and a kit for assigning clinical significance to indeterminate lung nodules or for a primary screen using a biomarker panel for detecting lung cancer.
  • Non-small cell lung cancer has the highest prevalence of all malignancies worldwide and remains the most common cause of cancer-related mortality.
  • lung cancer represented 12.9% of newly diagnosed cancer with 1.8 million cases and 19.4% of cancer related deaths with 1.59 million cases.
  • IGF-I insulin-like growth factor I
  • IGF- 1 R insulin-like growth factor 1 receptor
  • IGFBPs Insulin-like growth factor binding proteins
  • IGFBP-3 Insulin-like growth factor binding proteins
  • IGFBP-3 levels were inversely associated with lung cancer, noting a precipitous drop during tumoriogenesis.
  • uPA urokinase-type plasminogen activator
  • IGFBP-5 has been linked with TGF- ⁇ induced epithelial-to-mesenchymal invasion of breast cancer cultures. A decrease in the IGFBP-5 allows for unregulated TGF- ⁇ action and increased cell migration.
  • Biomarkers useful for distinguishing stage I NSCLC from benign disease are identified herein and applied to the refinement of a multianalyte classification algorithm. Previous work in this area demonstrated the prognostic ability of IGFBP-5 and IGFBP-7 to predict disease recurrence and outcomes in patients undergoing an anatomic resection for NSCLC. [13] IGF-related factors identified herein are shown to distinguish early-stage NSCLC from cases of benign disease in high-risk individuals with positive radiography.
  • biomarker panel assigning risk for the presence of lung cancer as a primary screen, possibly to indicate who should have further diagnostics (like LDCT imaging or biopsy) performed.
  • a method and a kit for assessing risk of lung cancer versus benign disease in a subject include obtaining a biological sample from the subject and determining a measurement for a panel of biomarkers in the biological sample.
  • the panel includes at least one biomarker selected from the group consisting of IGFBP-1 , IGFBP-2, IGFBP-3, IGFBP-4, IGFBP-5, IGFBP-6, IGFBP-7, IGF-1 , and IGF-2 and at least one biomarker selected from the group consisting of IL-6, IL-1 ra, IL-10, SDF- ⁇ ⁇ + ⁇ , TNF-a, M IP- l a, slL-2Ra, CA-125, Eotaxin, OPN, sEGFR, and sE-Selectin.
  • the method further includes comparing the measurement to a reference profile for the panel of biomarkers, sorting the patient into a group and determining whether the subject is at risk for lung cancer based on the group.
  • Figure 1 A-1 D illustrates representative 'Box and Whisker' plots indicating distribution of biomarker levels in subjects with benign nodules versus malignant nodules. Shown are IGFBP-3 (Panel A), IGFBP-5 (Panel B), IGF-1 (Panel C) and IGF-II (Panel D).
  • the present invention will utilize a panel of biomarkers measured in a biological sample obtained from a subject to identify subjects having lung cancer or to assess clinical significance to CT-detected indeterminate lung nodules.
  • the panel of biomarkers measured may be used to identify subjects having NSCLC or benign disease, possibly as a probability score or assignment of 'risk'.
  • biomarker refers to any biological compound that can be measured as an indicator of the physiological status of a biological system.
  • a biomarker may comprise an amino acid sequence, a nucleic acid sequence and fragments thereof.
  • Exemplary biomarkers include, but are not limited to cytokines, chemokines, growth and angiogenic factors, metastasis related molecules, cancer antigens, apoptosis related proteins, proteases, adhesion molecules, cell signaling molecules and hormones.
  • Measurement means assessing the presence, absence, quantity or amount (which can be an effective amount) of a given substance within a sample, including the derivation of qualitative or quantitative concentration levels of such substances, or otherwise evaluating the values or categorization of a subject's clinical parameters.
  • the term “Measuring” or “measurement” means assessing the presence, absence, quantity or amount (which can be an effective amount) of a given substance within a sample, including the derivation of qualitative or quantitative concentration levels of such substances, or otherwise evaluating the values or categorization of a subject's clinical parameters.
  • detecting or “detection” may be used and is understood to cover all measuring or measurement as described herein.
  • sample refers to a sample of biological fluid, tissue, or cells, in a healthy and/or pathological state obtained from a subject.
  • samples include, but are not limited to, blood, bronchial lavage fluid, sputum, saliva, urine, amniotic fluid, lymph fluid, tissue or fine needle biopsy samples, peritoneal fluid, cerebrospinal fluid, nipple aspirates, and includes supernatant from cell lysates, lysed cells, cellular extracts, and nuclear extracts.
  • the whole blood sample is further processed into serum or plasma samples.
  • the sample includes blood spotting tests.
  • subject refers to a mammal, preferably a human.
  • Biomarkers that may be used include but are not limited to cytokines, chemokines, growth and angiogenic factors, metastasis related molecules, cancer antigens, apoptosis related proteins, proteases, adhesion molecules, cell signaling molecules and hormones.
  • the biomarkers may be proteins that are circulating in the subject that may be detected from a fluid sample obtained from the subject.
  • the biomarker panel may include 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13 14, 15, 16, 17 or 18 biomarkers.
  • the biomarker panel may include include ten or fewer biomarkers.
  • the biomarker panel may include 2, 3, 6 or 7 biomarkers.
  • the biomarker panel may be optimized from a candidate pool of biomarkers.
  • the biomarker panel may be optimized for determining whether a subject has a specific disease.
  • the biomarker panel may be optimized to determine whether the subject has lung cancer and in some embodiments, the lung cancer may be NSCLC.
  • the biomarker panel may be optimized for differentiating between NSCLC from benign disease using a candidate biomarker panel starting with eighteen candidate biomarkers selected from the group including Insulin-like growth factor binding proteins 1 , 2, 3, 4, 5, 6, 7 (IGFBP-1 , -2, -3, -4, -5, -6, -7), Insulin-like Growth Factor-1 and II (IGF-I, -II), Interleukin 6 (IL-6), Interleukin 1 receptor antagonist (IL-1 ra), Interleukin 10, (IL-10), Stromal cell-derived factor 1 a + b (SDF- ⁇ ⁇ + ⁇ ), Tumor necrosis factor alpha (TNF-a), Macrophage inflammatory protein 1 alpha (MIP-1 a), Soluble interleukin 2 receptor antagonist (slL-2Ra), Cancer antigen 125 (CA-125), Eotaxin, Osteopontin (OPN), Soluble epidermal growth factor receptor (sEGFR), Soluble endothelial
  • the panel may include one or more biomarkers selected from the group including Insulin-like growth factor binding proteins 1 , 2, 3, 4, 5, 6, 7 (IGFBP-1 , -2, -3, -4, -5, -6, -7), Insulin-like Growth Factor-1 and II (IGF-I, -II) and one or more biomarkers selected from the group including Interleukin 6 (IL-6), Interleukin 1 receptor antagonist (IL-1 ra), Interleukin 10, (IL-10), Stromal cell-derived factor 1 a + b (SDF- ⁇ ⁇ + ⁇ ), Tumor necrosis factor alpha (TNF-a), Macrophage inflammatory protein 1 alpha (MIP-1 a), Soluble interleukin 2 receptor antagonist (slL-2Ra), Cancer antigen 125 (CA-125), Eotaxin, Osteopontin (OPN), Soluble epidermal growth factor receptor (sEGFR), Soluble endothelial selectin
  • Biomarker Panel Measurement generally relates to a quantitative measurement of an expression product, which is typically a protein or
  • the measurement of a biomarker panel may relate to a quantitative or qualitative measurement of nucleic acids, such as DNA or RNA.
  • the measurement of the biomarker panel of the subject detects differences in expression in subjects having lung cancer compared to subjects that are free from cancer.
  • the expression levels of each individual biomarker may be higher or lower in the subjects having lung cancer compared to subjects that are free from cancer.
  • a panel of a plurality of biomarkers provides an improved predictive value relative to a single biomarker.
  • Expression of the biomarkers may be measured using any method known to one skilled in the art. Methods for measuring protein expression include, but are not limited to Western blot, immunoprecipitation,
  • ELISA Enzyme-linked immunosorbent assay
  • RIA Radio Immuno Assay
  • RIA Radio Receptor assay
  • proteomics methods such as mass spectrometry
  • quantitative immunostaining methods Methods for measuring nucleic acid expression or levels may be any techniques known to one skilled in the art. Expression levels from the panel of biomarkers are measured in the subject and compared to the levels of the panel of biomarkers obtained from a cohort of subjects described below.
  • immunoassays may be used to determine the expression levels of the panel of biomarker.
  • Luminex Corp. Austin, TX.
  • the Luminex system uses
  • microspheres in a ninety-six well microplate. Each microsphere is dyed with red and infrared fluorophores at a range of independently varied concentrations of dye, creating unique absorbance signatures for each set of microspheres. Each of the microspheres is derivatized with antibodies having binding affinity for a particular type of molecular species. The subject sample is applied to a set of microspheres having different absorbance signatures, each carrying antibodies specific for a particular antigen. The antibodies on the beads then bind to the antigens present in the subject's sample. A secondary antibody may be applied in this system, followed by a streptavidin conjugated fluorophore reporter.
  • the beads, with their bound antigen and reporter are then sampled by an instrument.
  • a detection chamber is used to detect the unique absorbance signatures and reporter fluorescence intensity, thereby identifying to which set of analytes each microsphere belongs, thus identifying each biomarker tested, and producing a quantitative fluorescent signal from the reporter.
  • the fluorescence intensity of the observed signal is proportional to the quantity of antigen bound to the antibodies on the particular bead. Thus, it is possible to calculate the quantity of a particular biomarker in a sample.
  • a kit may be provided with reagents to measure at least two of the panel of biomarkers.
  • the panel of biomarkers to be measured with the kit may include two or more biomarkers from the group including Insulinlike growth factor binding proteins 1 , 2, 3, 4, 5, 6, 7 (IGFBP-1 , -2, -3, -4, -5, -6, - 7), Insulin-like Growth Factor-1 and II (IGF— I, -II) Interleukin 6 (IL-6), Interleukin 1 receptor antagonist (IL-1 ra), Interleukin 10, (IL-10), Stromal cell-derived factor 1 a + b (SDF- ⁇ ⁇ + ⁇ ), Tumor necrosis factor alpha (TNF-a), Macrophage inflammatory protein 1 alpha (MIP-1 a), Soluble interleukin 2 receptor antagonist (slL-2Ra), Cancer antigen 125 (CA-125), Eotaxin, Osteopontin (OPN), Soluble epidermal growth
  • the kit may include reagents to measure a panel of biomarkers that includes two, three, four, five, six, seven or more biomarkers combined together to measure a subject's biomarker panel.
  • the kit may be provided with one or more assays provided together in a kit.
  • the kit may include reagents to measure the biomarkers in one assay.
  • the kit may include reagents to measure the biomarkers in more than one assay.
  • Some kits may include a 4-plex assay and a 2-plex assay while other kits may include different combinations of assays to cover all the biomarkers needed to be measured.
  • the kit may also include reagents to measure a biomarker individually and other biomarkers in a 2-, 4-, or 8-plex assay. Any combination of reagents and assay may be combined in a kit to cover all the biomarkers needed.
  • methods determining whether a subject is at risk for lung cancer is based upon the biomarker panel measurement compared to a reference profile that can be made in conjunction with a statistical algorithm used with a computer to implement the statistical algorithm to sort the subject into a group.
  • the statistical algorithm is a learning statistical classifier system.
  • the learning statistical classifier system can be selected from the following list of non-limiting examples, including Random Forest (RF), Classification and Regression Tree (CART), boosted tree, neural network (NN), support vector machine (SVM), general chi-squared automatic interaction detector model, interactive tree, multiadaptive regression spline, machine learning classifier, and combinations thereof.
  • the Random Forest algorithm may be used to identify the panel of biomarkers.
  • the optimal multivariate panel of biomarkers was chosen based on variable selection algorithms performed within the random forests package in R. [29, 30]
  • Liberal inclusion criteria were applied for the individual biomarkers (a Mann-Whitney p value smaller than 0.05 or an area under the ROC curve [AUC] higher than 0.60) to ensure that no biomarker with potential value in a multianalyte panel was prematurely excluded from this selection process based on a weak individual performance.
  • the Random Forest algorithm may be used to identify the panel of biomarkers.
  • the optimal multivariate panel of biomarkers was chosen based on variable selection algorithms performed within the random forests package in R. [29, 30]
  • Liberal inclusion criteria were applied for the individual biomarkers (a Mann-Whitney p value smaller than 0.05 or an area under the ROC curve [AUC] higher than 0.60) to ensure that no biomarker with potential value in a multianalyte panel was prematurely excluded from this selection
  • Forests package selects optimal combinations of biomarkers by growing numerous (1000 in the present study) cross-validated classification trees for each subpanel of biomarkers, with each tree used to predict group membership for each case. These are counted as the tree votes for that group. The forest chooses the group membership having the most votes over all the trees in the forest. Each such tree is grown by cross-validation; where a training set
  • OOB out-of-bag
  • the classification accuracy of the random forest is measured by the averaged error of the OOB predictions across the entire forest; this is termed the OOB error rate.
  • the OOB error thus uses disjoint subsets of the data for model fitting and validation repeatedly. This cross-validation is also used to compute a variable importance for each biomarker included in the Random Forest analysis.
  • the stepwise selection method sequentially searches for optimal subpanel of markers where the marker with the lowest variable importance score from the Random Forest are removed at each step.
  • the subject data set may include about 20, 30, 40, 50, 60, 70, 80, 90, 100, 1 10, 120, 130, 140, 150 or more subjects.
  • the subject data set may be about 100 subjects, about 50 having lung cancer and about 50 having benign disease.
  • the subject data set may be between about 130-140 subjects with about half the data set having lung cancer and about half the training set having benign disease. Other numbers of subjects in a data set may also be used in the training set. Two thirds of the data set randomly selected may be used for training and the remaining one third tests the algorithm and this process is repeated to select the optimum biomarkers generate a reference profile and to determine whether a subject has cancer.
  • CART Classification and Regression Tree
  • Random Forest variable selection process may then be used by a CART algorithm to model a classification tree for identifying a subject's true (pathologic) preoperative lymph node status. This analysis was performed using the RPART package of the R statistical software suite. [31] Briefly, classification trees determine a set of binary if-then logical (split) conditions that permit accurate classification of (in this case) the subject's nodal status. The CART algorithm discriminates between groups by splitting the range of values measured for each individual biomarker at all of its possible split points. The 'goodness of split criterion' is then used to determine the best split point for each biomarker for predicting nodal status.
  • CART then ranks all of the best splits on each biomarker and selects the best biomarker and its split point for the split at the root node. CART then assigns classes to the two split nodes according to a rule that minimizes misclassification error. This process is continued at each nonterminal child node and at each of the successive stages until all observations are perfectly classified or the sample size within a given node is too small to divide (n ⁇ a user-supplied number; such as 5).
  • the final output of the resulting classification tree is a graphical display of decision criteria for each split, with the resulting predicted group memberships at the terminal nodes. The predicted probabilities of preoperative nodal status from the tree were used to obtain sensitivity and specificity across a range of cut-points for decision rules and the resulting ROC curve.
  • the analysis of the biomarker panel may be used to determine a treatment regime for the subject.
  • the measurement of one or more biomarkers in the panel may be used to determine whether to follow up at a later time point with the subject to repeat the
  • the treatment may be started or modified by administering a drug or changing the drug administered to the subject or to add an additional drug to an existing drug treatment regime, to change the dosage or other changes.
  • other types of treatment regimes may be used such as radiation.
  • the identification of patients at risk of lung cancer using the biomarker level may place the patient in a specific treatment, or an earlier treatment in the overall treatment strategy or identify subjects for further testing before beginning treatment.
  • the panel of biomarkers measured may be used to monitor subjects for post-surgical disease surveillance for early identification of disease recurrence.
  • Enrollment criteria for individuals in the LDCT program include age greater than 50 years or a smoker with greater than 20 pack years.
  • Pretreatment serum or plasma was prepared using standard
  • Binding Protein Panels EMD Millipore, Billerica, MA and included the following assays: insulin-like growth factor-l (IGF-I), insulin-like growth factor-l l (IGF-I I), insulin-like growth factor-binding protein-1 (IGFBP-1 ), IGFBP-2, IGFBP-3, IGFBP-3, IGFBP-4, IGFBP-5, IGFBP-6, IGFBP-7. All assays were performed in a blinded fashion using a 384-well adaptation of the manufacturer's recommended protocols. All data was collected on a Luminex FlexMAP 3D system with concentrations calculated based on 7-point standard curves using a five- parametric fit algorithm in xPONENT v4.0.3 (Luminex Corp., Austin, TX).
  • a primary objective of this study was to evaluate the association of circulating biomarkers of IGF-signaling with the clinical significance of
  • IGF-related biomarkers were evaluated based on ultimate histology of the solitary nodule in the combined cohort.
  • IGFBP-5 was notably decreased (p ⁇ 0.001 ) in malignant cases.
  • Table II Patterns of serum levels of circulating IGF-related molecules.
  • the validation cohort included a similar age distribution as our internal sample, with benign patients younger than those with carcinoma. Nodule sizes were similarly distributed as well, and histology again favored adenocarcinoma (Table III).
  • Table III Demographics for the multianalyte algorithm refinement.
  • IGFBP-7 with Mann-Whitney p-value 0.019 and AUC of 0.651 and IGF-II, with an AUC of 0.619. No other IGF-related factor was found to have significance in this distinct cohort of plasma specimens from Rush University.
  • the Random Forest based variable selection method was applied to the Rush University discovery cohort for classification, as we previously described. [16] Using this method a new 7-analyte classification panel was formulated that is composed of IL-6, IL-10, IL-1 ra, SDF- ⁇ + ⁇ , IGFBP-4, IGFBP-5, and IGF-II (see Table IV below).
  • This panel differentiated patients with NSCLC from patients with benign disease with a cross-validated accuracy of 90.4%, which is an improvement from the 76.5% of a panel including IL-6, IL-10, I L- 1 ra , slL-2Rc SDF- ⁇ + ⁇ , TNF-a, and MIP-1 [16]
  • Our new panel provided 24 cases of true negatives, 61 cases of true positives, 6 cases of false positives, and 3 cases of false negatives for a calculated sensitivity of 95.3%, specificity of 91.0%, and a negative predictive value of 89%.
  • Emerging LDCT-based screening programs are designed to efficiently identify individuals with early lung cancer with the intent of decreasing the number of late stage lung cancer cases and, thereby, provide potentially curative treatment options. Based on current NLST inclusion criteria for LDCT scanning approximately 7 million individuals would qualify for a LDCT screening program in the US. [5] Of those 7 million, it is estimated 1.6-3.5 million indeterminate nodules would be identified and carry a high false positive rate of 94.5-96.4%. [16, 22] With this increased diagnostic burden of false positive cases, the International Association for the Study of Lung Cancer (IASLC) and the Strategic CT
  • IGF-I/II and IGFBP1 -7 were selected as biomarkers to investigate the theorized sustained proliferative signaling and active metabolism.
  • Cellular metabolism is a complex physiological process that is normally regulated by insulin and its associated proteins and factors (IGF-I/II and
  • IGFBP-1/7 IGFBP-1/7.
  • IGF-1 has been studied and its function implicated in cell growth and apoptosis. [26] The dysregulation of these factors has been implicated in tumorigenesis. [7, 8, 10] IGF-I can increase expression and activity of normally inactive enzymes including urokinase-type plasminogen activator, matrix metalloproteinase-2 (MMP) and MMP-9. [27] Increased enzymatic activity has been linked to local and metastatic spread. Additionally, IGFBP's have been implicated in the epithelial-to-mesenchymal transition (EMT) of tumorigenesis. Specifically, normal levels of IGFBP-5 are linked with decreased migration of tumor cells mediated via TGF-31. [12]
  • EMT epithelial-to-mesenchymal transition
  • the practice of the present invention will employ, unless otherwise indicated, conventional methods for measuring the level of the biomarker within the skill of the art.
  • the techniques may include, but are not limited to, molecular biology and immunology. Such techniques are explained fully in the literature.
  • IGFBP-5 enhances epithelial cell adhesion and protects epithelial cells from TGFbetal-induced mesenchymal invasion. Int J Biochem Cell Biol, 2013. 45(12): p. 2774-85.

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Abstract

L'invention concerne un procédé et un kit pour évaluer le risque de cancer du poumon ou bien de maladie bénigne chez un sujet. Le procédé comprend l'obtention d'un échantillon biologique auprès du sujet et la détermination de mesures pour un panel de marqueurs biologiques dans l'échantillon biologique. Le panel comprend au moins un marqueur biologique choisi dans le groupe constitué d'IGFBP-1, IGFBP-2, IGFBP-3, IGFBP-4, IGFBP-5, IGFBP-6, IGFBP-7, IGF-1 et IGF -2, et au moins un marqueur biologique choisi dans le groupe constitué d'IL-6, IL-1 ra, IL-10, SDF-Ια+ß, TNF-a, MIP-1a, sIL-2ra, CA-125, l'éotaxine, OPN, sEGFR, et la sE-Sélectine. Le procédé comprend en outre la comparaison des mesures à un profil de référence pour le panel de marqueurs biologiques, l'affectation du sujet dans un groupe de stratification et la détermination pour le sujet d'un risque de cancer du poumon en fonction du groupe de stratification.
PCT/US2015/027562 2014-04-25 2015-04-24 Protéines associées au facteur de croissance analogue à l'insuline (igf) en circulation pour la détection du cancer du poumon WO2015164772A1 (fr)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107435062A (zh) * 2016-05-25 2017-12-05 上海伯豪医学检验所有限公司 甄别肺部微小结节良恶性的外周血基因标志物及其用途
WO2019232361A1 (fr) * 2018-05-31 2019-12-05 University Of Florida Research Foundation Traitement personnalisé du cancer du pancréas

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