WO2017041173A1 - Essai de diagnostic pour une maladie osseuse de paget - Google Patents

Essai de diagnostic pour une maladie osseuse de paget Download PDF

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WO2017041173A1
WO2017041173A1 PCT/CA2016/051059 CA2016051059W WO2017041173A1 WO 2017041173 A1 WO2017041173 A1 WO 2017041173A1 CA 2016051059 W CA2016051059 W CA 2016051059W WO 2017041173 A1 WO2017041173 A1 WO 2017041173A1
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pdb
odds
score
individual
genetic
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Sabrina GUAY-BELANGER
David SIMONYAN
Laëtitia MICHOU
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Universite Laval
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    • 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
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • 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
    • C12Q2600/156—Polymorphic or mutational markers
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/40—Population genetics; Linkage disequilibrium

Definitions

  • the present relates to a method and system for detecting Paget's disease of bone, combining genetic and bone biomarkers in asymptomatic individuals.
  • Paget's disease of bone is characterized by focal abnormal bone remodeling, with increased bone resorption coupled with an increased and disorganized new bone formation, resulting in abnormal bone architecture and weakened bone strength.
  • This disease affects up to 3% of Caucasians over 55 years of age, which makes it the second most frequent metabolic bone disorder after osteoporosis.
  • patients with PDB are asymptomatic.
  • 10 to 30% of patients will develop symptoms and complications, such as bone pain, bone deformities, fractures, deafness, or nerve root compression.
  • the development of an osteosarcoma is the most severe complication, and occurs in less than 1 % of patients with PDB.
  • PDB is transmitted in an autosomal dominant mode of inheritance with incomplete penetrance in about one third of cases.
  • genetic heterogeneity has been demonstrated in familial forms of PDB, only the Sequestosome 1 (SQSTM1) gene at the 5q35 locus has been linked to PDB, with nearly 30 disease-causing mutations identified so far.
  • SQSTM1 Sequestosome 1
  • the at risk genotype in the biological sample is at least one of rs499345 (EPS8L3/CSF- 1) , rs5742915 (PML), rs2458413 (TM7SF4), rs3018362 (RPL17P14), and rs2234968 ( ⁇ /).
  • the individual is characterized as being predisposed to PDB when the genetic score is over a threshold.
  • the threshold is 0.33.
  • the method described herein further comprises the steps of determining the concentration of a biological marker; and calculating a biochemical score to characterize the individual as being predisposed to PDB.
  • the biological marker is at least one of calcium and P1 NP.
  • the concentration of calcium in ⁇ /L and of P1 NP in ng/mL in the biological sample is determined.
  • the individual is characterized as being predisposed to PDB when the biological score is over a threshold.
  • the threshold is 0.07.
  • the method provided herein further comprises a first step of determining the absence of a mutation in the SQSTM1 gene.
  • the biological sample is at least one of a serum sample and a DNA sample.
  • the individual is a human.
  • the method provided herein further comprises a final step of providing a whole-body bone scan of the individual to confirm the presence of PDB.
  • the provided herein further comprises a reaction vessel for receiving the biological sample for determining the presence or absence of the at risk genotypes.
  • the at least one application is further configured for controlling laboratory equipment for determining the concentration of calcium and P1 NP in the biological sample.
  • the at least one application is further configured for controlling laboratory equipment operative for determining the absence of a mutation in the SQSTM1 gene.
  • Fig. 1 illustrates the area under the receiver operating characteristic (ROC) curve (AUC) calculations for the most significant biomarker combinations.
  • ROC receiver operating characteristic
  • Fig. 2 illustrates a schematic representation of the molecular tests proposed for the detection of PDB.
  • the genetic algorithm relies first on a screen in the SQSTM1 gene to search for disease-causing germinal mutations. If negative, the genetic combination developed described herein should be tested.
  • the genetic and biochemical algorithm relies first on a screen in the SQSTM1 gene to search for disease-causing germinal mutations. If negative, the combination integrating both genetic and biochemical markers developed in this study is tested.
  • Fig. 3 is an exemplary embodiment of a PDB assessment system.
  • Fig. 4 is a detailed view of the PDB assessment system of Fig. 3.
  • Fig. 5a is an exemplary embodiment of an application running on the processor of Fig. 4.
  • Fig. 5b is another exemplary embodiment of an application running on the processor of Fig. 4.
  • Fig. 6a is an exemplary flowchart of a method as performed by the application of Fig. 5a.
  • Fig. 6b is an exemplary embodiment of a method as performed by the application of Fig 5a.
  • the method provided herein uses a molecular test, integrating first a screen for SQSTM1 germinal mutations, followed in non-carriers, either by a genetic markers combination (if only DNA is available for example), or by a genetic and biochemical markers combination (if both DNA and serum are available).
  • the molecular test provided herein allows avoiding systematic bone scans in all patients likely to receive a bone anabolic agent prescription, which may be a cost- effective approach in a medium to long-term period.
  • 35 SNPs previously associated with PDB were genotyped in 305 patients, and 292 healthy controls.
  • serum levels of 14 bone biomarkers were assayed in 51 patients and 151 healthy controls.
  • Bivariate and multivariate logistic regression models with adjustment for age and sex were fitted to search for a combination of SNPs and/or bone biomarkers that could best detect PDB in patient non-carriers of a SQSTM1 mutation.
  • a combination of five genetic markers gave rise to the highest area under the ROC curve (AUC) with 95% confidence interval [95% CI] of 0.731 [0.688; 0.773], which allowed to detect 81 .5% of patients with PDB.
  • AUC area under the ROC curve
  • a combination of two bone biomarkers had an AUC of 0.822 [0.726; 0.918], and was present in 81 .5% of patients with PDB.
  • the combination of the five genetic markers and the two bone biomarkers increased the AUC up to 0.892 [0.833; 0.951 ], and detected 88.5% of patients with PDB.
  • rs499345 (EPS8L3/CSF- 1) , rs5742915 (P L), rs2458413 (TM7SF4), rs3018362 (RPL17P14), rs2234968 (OP77V) and rs62620995 (TM7SF4), at risk genotypes, yielded to most significant results.
  • the AUC for this combination was 0.767 [0.727; 0.807] (p ⁇ 0.0001), but the at risk genetic marker rs62620995 was a rare variant, present in a very small number of individuals (3.9%), which may limit the use of this marker in other populations.
  • Odds of disease Exp [-2.8535 + (sex*1 .2590) + (rs499345*0.5316) +
  • PDB patients with a genetic score > 0.33 had significant higher sALP levels than PDB patients with a genetic score ⁇ 0.33 (3.25 ⁇ 3.35 versus 2.03 ⁇ 1 .42, p ⁇ 0.0001). No other genotype-phenotype association was found. Thus, PDB patients with a genetic score > 0.33 may have a more active disease, based on the sALP levels measurement.
  • a bivariate analysis showed that PDB patients had a statistically significant higher serum level of P1 NP, C-telopeptide, calcium after correction for albumin, total ALP and OPG, and a lower serum level of sclerostin compared to healthy controls.
  • the bone biomarkers with the greatest AUC were calcium, total ALP, and P1 NP: 0.747, 0.739 and 0.721 , respectively.
  • this biomarker alone had a sensitivity of 70.4%, a specificity of 68.0%, and a positive and negative likelihood ratios of 2.2 and 0.4.
  • PPV and NPV were at 28.4% and 92.7%, respectively.
  • Odds of disease Exp [-27.6491 + (sex*0.6241) + (Calcium*0.0106) + (P1 NP*0.0184)]
  • Odds of disease Exp [-30.9036 + (sex*0.5957) + (rs499345*0.4824) +
  • a pure genetic molecular test relying on a two steps algorithm is used: first a screen in the SQSTM1 gene should be performed to search for disease-causing germinal mutations, and if negative, the genetic score based on the combination of the five SNPs developed in this study should be calculated (Fig. 2A).
  • This genetic algorithm had a sensitivity of 83.6%, a specificity of 51 .0%, a PPV of 64.1 % and a NPV of 74.9%. Positive and negative likelihood ratios were at 1 .7 and 0.3, respectively.
  • this genetic and biochemical algorithm had a sensitivity of 93.9%, a specificity of 54.0%, positive and negative likelihood ratios of 2.0 and 0.1 , and a PPV and NPV of 40.0% and 96.4%, respectively.
  • This genetic combination relying on SNPs identified in GWAS and replicated in the French-Canadian population, allowed the correct classification of 81 .5% of patients with PDB in non-carriers of any SQSTM1 mutation, and in 77.8% of patients with PDB coming from the multiethnic population of New York city area, using a cut-off point of 0.33 for the genetic score.
  • a combination of bone biomarkers, including calcium and P1 NP, and adjusted for sex had an AUC of 0.822 [0.726; 0.918], and a sensitivity and a specificity of 81 .5% and 48.7%, respectively.
  • This bone biomarker combination identified correctly 81 .5% of patients with PDB in French and French-Canadian populations, all non-carriers of a SQSTM1 mutation and using a cut-off point of 0.07 for the biochemical score. Then, the combination of both genetic and biochemical markers adjusted for sex increased the AUC to 0.892 [0.833; 0.951 ] and, using a cut-off point of 0.05 for the combined score, this combination identified correctly 23 patients (88.5%) with PDB. In order to cover the monogenic component of PDB (with the presence of germinal SQSTM1 mutations) and the multifactorial aspect of the disease, two molecular tests relying on a two steps algorithm were used.
  • the genetic algorithm identified 255 (83.6%) of patients with PDB, and had a sensitivity of 83.6% and a specificity of 51 .0%.
  • the algorithm integrating both genetic and biochemical markers identified correctly 46 (93.9%) patients with PDB, and had a sensitivity of 93.9% and a specificity of 54.0%.
  • the criteria used to diagnose PDB were: 1) an increase in total sALP level and/or 2) a typical aspect of PDB on the bone X-rays and/or 3) an abnormal whole-body bone scan.
  • Patients with PDB originated from two different countries: Canada (French-Canadian from a 120km area around Quebec City) and France (Angers, Paris, Saint-Etienne areas). They had either a familial form of PDB, with only one affected per family being included, or they were unrelated affected individuals.
  • Genotyping of SNPs relied on two different methods: Sanger sequencing or Sequenom MassARRAY SNP Multiplex Technology.
  • the SNPs genotyped by the Sanger sequencing method were first amplified using a polymerase chain reaction (PCR). Amplification products were purified and sequenced with Big Dye Deoxy Terminator v 3.1 Cycle (Applied Biosystems) on an ABI 3730x1 sequencer, and the DNA sequences obtained were analyzed with Staden package version 1.6.
  • PCR polymerase chain reaction
  • iPLEX reaction multiplexed primer-based extension reaction
  • each SNP allele was detected on the MassARRAY Compact MALDI-TOF mass spectrometer. The results were analyzed with MassARRAY Typer 4 software. In order to verify the allele calls, 3.7% of samples were randomly duplicated in the plate, and yielded to a consistency of 100%.
  • Each SNP was genotyped in 287 French-Canadian patients with PDB, 18 French patients with PDB, and 292 healthy controls from the French-Canadian population. Finally, five selected SNPs were genotyped in 70 unrelated patients with PDB, from the New- York city area population.
  • Hardy-Weinberg equilibrium was checked by a conformity chi-square test in controls. Linkage disequilibrium was calculated by the use of Haploview software. Since there was one to five missing genotypes in 10 SNPs, and one SNP (rs3829923) had 14 (2.35%) missing, these genotypic data were imputed using the expectation- maximization algorithm of multiple imputation procedure, which includes all genetic markers. Genetic markers were dichotomized according to the presence or absence of the at risk allele within the genotype, and patients carrying a germinal or a post-zygotic SQSTM1 mutation were removed from these analyses.
  • MDR multiple dimensionality reduction
  • the goodness of fit for the last models was verified using the Hosmer- Lemeshow test.
  • a classification table was generated in order to establish a cut-off point for the predicted probability of PDB based on estimated sensitivity and specificity. Then, using the logistic regression estimates of each parameter, the predicted probability of PDB was calculated for each individual, and if this predicted probability was equal or greater than the established cut-off point, the individual was considered as having a positive test. In order to facilitate the results interpretation, this predicted probability was referred as a genetic score.
  • the area under the receiving operating characteristics (ROC) curve and 95% CI were estimated for all of these models using DeLong et al.'s approach available in SAS (DeLong et al., 1988, Biometrics, 44: 837- 845).
  • Intrinsic characteristics including sensitivity, specificity, positive and negative likelihood ratios, as well as extrinsic characteristics, including positive and negative predicting values (PPV and NPV) with 95%CI were also calculated. Then, analyses were performed with 243 patients with PDB, of whom 231 were French-Canadians and 12 were French, and 292 healthy controls.
  • SNPs Single nucleotide polymorphisms
  • Logistic regression analysis adjusted for age and sex, showed that a combination of six genetic markers, consisting in rs499345 (EPS8L3/CSF- 1) , rs5742915 (P L), rs2458413 (TM7SF4), rs3018362 (RPL17P14), rs2234968 (OPTN) and rs62620995 (TM7SF4) at risk genotypes, yielded to most significant results.
  • the AUC for this combination was 0.767 [0.727; 0.807] (p ⁇ 0.0001), but the at risk genetic marker rs62620995 was a rare variant, present in a very small number of individuals (3.9%), which may limit the use of this marker in other populations.
  • SNPs single nucleotide polymorphisms
  • OR odds ratio
  • 95%CI 95% confidence interval
  • AUC area under the receiver operating characteristic (ROC) curve
  • the genetic algorithm consists in germinal SQSTM1 mutations test followed by the genetic combination.
  • the genetic and biochemical algorithm consists in germinal SQSTM1 mutations test followed by the combination integrating both genetic and biochemical markers
  • PDB patients with a genetic score > 0.33 had significant higher sALP levels than PDB patients with a genetic score ⁇ 0.33 (3.25 ⁇ 3.35 versus 2.03 ⁇ 1 .42, p ⁇ 0.0001) (Table 5).
  • biomarkers associated with bone metabolism were assayed in serum using commercial Roche Diagnostics immunoassay kits (Hoffman's division LaRoche Ltee; Laval, Canada), according to the manufacturer's protocol: procollagen type 1 amino-terminal propeptide (P1 NP), 25-OH vitamin D, interleukin-6 (IL-6), parathyroid hormone (PTH), C-telopeptide, N-mid osteocalcin, calcium, albumin, total ALP and high-sensitivity C-reactive protein (hsCRP). All these immunoassays were performed using the Cobas E170 or the Cobas c31 1 system.
  • receptor activator of nuclear factor kappa-B ligand and osteoprotegerin (OPG) serum levels were measured using commercially available ELISA kits from Neobiolab (Cambridge, MA, USA), and serum levels of anti-measles virus IgG were measured using the ELISA kit from IBL International (Hamburg, Germany), according to the manufacturer's protocols.
  • sclerostin serum levels were measured using an ELISA protocol. Briefly, the plates were coated with 4 ⁇ g/mL of human sclerostin monoclonal antibody (R&D, Minneapolis, USA) overnight at 4°C. Then, all wells were blocked for two hours with a 0.1 M Tris solution containing 8% of milk.
  • the stop solution (R&D, Minneapolis, USA) was added, and the absorbance, determined as the optical density at 450 nm, was measured.
  • the intra-assay coefficient of variation (CV) was 10.1 %, and the inter-assay CV was 7.3%.
  • CV intra-assay coefficient of variation
  • Each of these biomarkers were measured in serum samples from 36 French-Canadian patients with PDB, 15 French patients with PDB and 151 healthy controls from the French-Canadian population. Serum samples were collected at the inclusion visit, aliquoted, and frozen immediately at -80 until analyses. Serum samples were not available in the New York city area population.
  • AUC area under the ROC curve
  • 95% CI were also calculated for each bone biomarker separately, and then in combination.
  • Intrinsic and extrinsic characteristics were also calculated for the bone biomarkers combinations.
  • a classification table was generated. Then, the estimates of each parameter given by the logistic regression analysis were used to calculate the predicted probability of PDB for each individual. In this model, the predicted probability was referred as a biochemical score. Analyses were then performed with 27 PDB-affected patients (16 French-Canadians and 1 1 French), and 151 healthy controls. We further combined the genetic and biochemical markers in the same logistic model to test if it increased the diagnostic detection. The predicted probability of a positive score for PDB detection for this model was calculated as previously described, and was referred as a combined score in this study.
  • genotype-phenotype associations included 26 patients with PDB (16 French-Canadians and 10 French), and 151 healthy controls.
  • genotype- phenotype associations we compared PDB patients with a genetic score equal or greater than the established cut-off point to PDB patients with a genetic score inferior than this cut-off point, for the following items: familial history of PDB, age at diagnosis, total sALP levels, number of affected bones, and Reniers' index. Analyses relied on Student t test for continuous variables, and 2 of Fisher's exact tests when appropriate for nominal values.
  • Genotype-phenotype association analyses included patients with PDB from the three populations available to this study. Statistical analyses were performed using SAS 9.4, MDR v 3.0.2, and IBM SPSS Statistics 21 . Intrinsic and extrinsic characteristics for each molecular test were calculated using SAS and an online platform. For all analyses, a p value of ⁇ 0.05 was considered statistically significant.
  • the bone biomarkers with the greatest AUC were calcium, total ALP, and P1 NP: 0.747, 0.739 and 0.721 , respectively.
  • P1 NP and total ALP were considered as gold standards.
  • this biomarker alone had a sensitivity of 70.4%, a specificity of 68.0%, and a positive and negative likelihood ratios of 2.2 and 0.4.
  • PPV and NPV were at 28.4% and 92.7%, respectively.
  • this biomarker had a sensitivity of 77.8%, a specificity of 51.0%, a positive and a negative likelihood ratios of 1.6 and 0.4, and a PPV and NPV of 22.1 % and 92.8%, respectively (see Table 3).
  • Logistic regression analysis, adjusted for age and sex, showed that a combination of five bone biomarkers, including calcium, P1 NP, N-mid osteocalcin, OPG and sclerostin, had the highest AUC: 0.887 [0.821 ; 0.953] (p ⁇ 0.0001).
  • the screening for germinal SQSTM1 mutations had a sensitivity of 15.4%, a specificity of 100%, a PPV of 100%, a NPV of 53.1 %, and a negative likelihood ratio of 0.9 (see Table 3).
  • the PDB assessment system 302 may be accessible remotely from any one of a plurality of devices 304 over connections 306.
  • the devices 304 may comprise any device, such as a personal computer, a tablet, a smart phone, or the like, which is configured to communicate over the connections 306.
  • the PDB assessment system 302 may itself be provided directly on one of the devices 304, either as a downloaded software application, a firmware application, or a combination thereof.
  • cloud computing may also be used such that the system 302 is provided partially or entirely in the cloud.
  • connections 306 may be provided to allow the PDB assessment system 302 to communicate with the devices 304.
  • the connections 306 may comprise wire-based technology, such as electrical wires or cables, and/or optical fibers.
  • the connections 306 may also be wireless, such as RF, infrared, Wi-Fi, Bluetooth, and others.
  • Connections 306 may therefore comprise a network, such as the Internet, the Public Switch Telephone Network (PSTN), a cellular network, or others known to those skilled in the art. Communication over the network may occur using any known communication protocols that enable devices within a computer network to exchange information.
  • PSTN Public Switch Telephone Network
  • One or more databases 308 may be integrated directly into the PDB assessment system 302 or any one of the devices 304, or may be provided separately therefrom (as illustrated). In the case of a remote access to the databases 308, access may occur via connections 306 taking the form of any type of network, as indicated above.
  • the various databases 308 described herein may be provided as collections of data or information organized for rapid search and retrieval by a computer.
  • the databases 308 may be structured to facilitate storage, retrieval, modification, and deletion of data in conjunction with various data-processing operations.
  • the databases 308 may be any organization of data on a data storage medium, such as one or more servers.
  • the databases 308 illustratively have stored therein patient data, such as test results indicating the presence/absence of a mutation in the SQSTM1 gene, presence/absence of genetic markers and/or biochemical concentrations.
  • patient data such as test results indicating the presence/absence of a mutation in the SQSTM1 gene, presence/absence of genetic markers and/or biochemical concentrations.
  • genetic markers comprise rs499345 (EPS8L3/CSF-1), rs5742915 (PML), rs2458413 (TM7SF4), rs3018362 (RPL17P14), and rs2234968 (OPTN).
  • biochemical concentrations comprise the concentration of calcium in ⁇ /L and of P1 NP in ng/mL.
  • the databases may also have stored therein results of genetic scores and/or biochemical scores, and characterizing data regarding a predisposition to PDB. Additional data, such as bone scans of individuals, may also be provided in the databases 308.
  • the PDB assessment system 302 illustratively comprises one or more server(s) 400.
  • server(s) 400 For example, a series of servers corresponding to a web server, an application server, and a database server may be used. These servers are all represented by server 400 in Fig. 4.
  • the server 400 may be accessed by a user, such as a lab worker or a clinician, using one of the devices 304, or directly on the system 302 via a graphical user interface.
  • the server 400 may comprise, amongst other things, a plurality of applications 406a ... 406n running on a processor 404 coupled to a memory 402. It should be understood that while the applications 406a ... 406n presented herein are illustrated and described as separate entities, they may be combined or separated in a variety of ways.
  • the memory 402 accessible by the processor 404 may receive and store data.
  • the memory 402 may correspond to the databases 308 or it may be in addition to databases 308. All of the data provided on databases 308 may thus be stored in memory 402.
  • the memory 402 may be a main memory, such as a high speed Random Access Memory (RAM), or an auxiliary storage unit, such as a hard disk, a floppy disk, or a magnetic tape drive.
  • the memory 402 may be any other type of memory, such as a Read-Only Memory (ROM), or optical storage media such as a videodisc and a compact disc.
  • the processor 404 may access the memory 402 to retrieve data.
  • the processor 404 may be any device that can perform operations on data.
  • Examples are a central processing unit (CPU), a front-end processor, a microprocessor, and a network processor.
  • the applications 406a ... 406n are coupled to the processor 404 and configured to perform various tasks.
  • An output may be transmitted to the devices 304.
  • Fig. 5a is an exemplary embodiment of an application 406a running on the processor 404.
  • Fig. 6a is an exemplary embodiment of a method as performed by the application 406a.
  • the application 406a illustratively comprises an analysis module 502, a scoring module 504, and a characterizing module 506.
  • Biological samples from individuals under test may be received at one or more laboratory equipment, as per step 602.
  • the analysis module 502 may be configured to control equipment such as those used to extract DNA from biological material, amplify the DNA, and detect or measure the presence/absence of genotypes, as per step 604.
  • the analysis module 502 may be configured to control a thermocycler to perform PCR or real-time PCR.
  • the analysis module 502 is configured for controlling lab equipment for the purpose of determining the presence or the absence of at risk genotypes in one or more biological samples, the at risk genotypes being selected from the group consisting of rs499345 (EPS8L3/CSF-1), rs5742915 (PML), rs2458413 (TM7SF4), rs3018362 (RPL17P14), and rs2234968 (OPTN).
  • the analysis module is further configured for determining the concentration of calcium in ⁇ / ⁇ . and of P1 NP in ng/mL in the same or a different biological sample.
  • the analysis module 502 may be configured to store any results from the analysis in the data storage 508, which may correspond to the databases 308 and/or memory 402.
  • the scoring module may be configured for retrieving the results of the analysis from the data storage 508 and calculating a genetic score therefrom, as per step 606.
  • the scoring module 504 is further configured for calculating a biochemical score using biochemical data retrieved from the data stored 508.
  • the scoring module 504 may be configured to store any resulting genetic and/or biochemical score in the data storage 508 or to transmit the scores directly to the characterizing module 506.
  • the characterizing module 506 may be configured to retrieve scores from the data storage 508 and characterizing individuals as being predisposed to PDB as a function of the scores, as per step 608. For example, when the genetic score is of 0.33 or more, an individual may be characterized as having such a predisposition. In another example, an individual may be characterized as having such a predisposition when the biochemical score is of 0.07 or more.
  • the threshold values for characterization of a predisposition may vary.
  • the characterizing module 506 is configured to characterize an individual as having a predisposition to PDB only when both the genetic and the biochemical scores meet the threshold, which may be a value representing a sum of both scores. Alternatively, only the genetic score may be considered and the biochemical score may be used to further confirm the result of the genetic score.
  • the application 406a may further be used to determine the absence of a mutation in the SQSTM1 gene, using for example the analysis module 502.
  • Fig. 6b illustrates an exemplary embodiment of a method including this step. Step 603 of determining the absence of the mutation may be performed using the same or a different biological sample. If the mutation is absent, the method continues to step 604. If the mutation is present, the method ends. Alternatively, the data used by the analysis module 502 has already been screened for the absence of the mutation in the SQSTM 1 gene.
  • the analysis module 502 may be fully automated for control of the lab equipment, or it may be partially automated and operate with the assistance of a lab operator or a technician. Operation may take place via a graphical user interface, as indicated above. It should be understood that the lab equipment may be local or remote to the PDB assessment system 302, and control thereof may occur via connections 306.
  • Fig. 5b illustrates another embodiment for an application 406b running on the processor 404.
  • a scoring module 504 and a characterizing module 506 are provided. Genetic and/or biochemical data for each individual undergoing PDB testing may be provided directly to the scoring module 504 or it may be stored in data storage 508 and accessed by the scoring module 504.
  • a separate application (not shown) running on processor 404 or on a separate processor, may be used to perform step 604 from the exemplary method of figure 6a.

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Abstract

La présente invention concerne un procédé et un système pour détecter une maladie osseuse de Paget, combinant des marqueurs biologiques génétiques et osseux chez des individus asymptomatiques. L'invention concerne également un système pour évaluer la prédisposition à une maladie osseuse de Paget (PDB) chez un individu manquant d'une mutation dans le gène SQSTM1, le système comprenant une mémoire ; un processeur couplé à la mémoire ; et au moins une application stockée dans la mémoire et pouvant être exécutée par le processeur pour recevoir des données indicatives de la présence ou de l'absence d'un génotype à risque dans un échantillon biologique, et calculer un score génétique pour caractériser l'individu comme étant prédisposé à une PDB lorsque le score génétique est au-dessus d'un seuil.
PCT/CA2016/051059 2015-09-10 2016-09-08 Essai de diagnostic pour une maladie osseuse de paget WO2017041173A1 (fr)

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Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130096178A1 (en) * 2010-04-14 2013-04-18 The University Court Of The University Of Edinburgh Genetic markers for paget's disease

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130096178A1 (en) * 2010-04-14 2013-04-18 The University Court Of The University Of Edinburgh Genetic markers for paget's disease

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