Patent Application
20240288448
The present invention relates to cardiovascular disease, and particularly, although not exclusively, to cardiovascular disease biomarkers and their use in methods of diagnosis and prognosis. The invention also extends to diagnostic and prognostic kits utilising the biomarkers of the invention for diagnosing or prognosing cardiovascular disease.
Due to the complexity of cardiovascular disease (CVD), the ability of single biomarkers to predict cardiovascular outcome (CVO) risk is limited (De Lemos et al 2017), though many genetic loci and protein biomarkers (BMKs) have been discovered. Combining genetic information with other types of omics data should have more predictive power to detect sub-groups of the population and the correspondent molecular signatures related to the clinical outcomes (Vilne et al., 2018). This should point to molecular signatures for CV risk and show their potential to stratify the population in relation to disease progression. A better understanding of the drivers of T2D-associated cardiovascular disease will help to define sub-populations that may differ with regard to disease progression.
The inventors developed and tested a workflow to identify substructure of a trial population by molecular signatures of protein and genetic biomarkers. They discovered signatures defining sub-populations that progressed differently towards CVD, suggesting that these signatures reflect different stages of disease progression. They identified combinations of biomarkers, reflecting differences in CVD progression, will lead to strategies to optimize clinical trials plans, drug efficacy, and so to optimize treatment.
Accordingly, in a first aspect of the invention, there is provided a method of determining, diagnosing and/or prognosing an individual's risk of suffering from cardiovascular disease, the method comprising;
Advantageously, the method of the invention enables the identification of individuals who are at risk from suffering from a CVD event, and thus enables early intervention to prevent, or reduce the risk of, the individual from suffering from a CVD event. In particular, detection of each biomarker in isolation enables the identification of individuals who are at risk from suffering from a CVD event, and detection of multiple biomarkers, i.e. the biomarker signature provides a particularly effective means of enabling early intervention to prevent, or reduce the risk of, the individual from suffering from a CVD event.
The skilled person would understand that the term prognosis may relate to predicting the rate of progression or improvement and/or the duration of cardiovascular disease with individual suffering from cardiovascular disease.
The method may be performed in vivo, in vitro or ex vivo. Preferably, the method is performed in vitro or ex vivo. Most preferably, the method is performed in vitro.
The expression level may relate to the level or concentration of a biomarker polynucleotide sequence. The polynucleotide sequence may be DNA or RNA.
The DNA may be genomic DNA. The RNA may be mRNA.
The amount of biomarker may relate to the concentration of biomarker polypeptide sequence.
The biomarker activity may relate to the activity of the biomarker protein, preferably activity in relation to interacting biomarkers.
Categorical Biomarker(s):
LLQQ=2→inactivation (−1)
LLQQ=from 3 to 4→no activation (0)
LLQQ=from 5 to higher levels→activation (+1)
For the quantitative biomarker(s) we used the tertiles of the distribution.
Accordingly, the expression level, amount, and/or activity of the biomarker may be detected by: sequencing methods (e.g., Sanger, Next Generation Sequencing, RNA-SEQ), hybridization-based methods, including those employed in biochip arrays, mass spectrometry (e.g., laser desorption/ionization mass spectrometry), fluorescence (e.g., sandwich immunoassay), surface plasmon resonance, ellipsometry and atomic force microscopy. Expression levels of markers (e.g., polynucleotides, polypeptides, or other analytes) may be compared by procedures well known in the art, such as RT-PCR, Northern blotting, Western blotting, flow cytometry, immunocytochemistry, binding to magnetic and/or antibody-coated beads, in situ hybridization, fluorescence in situ hybridization (FISH), flow chamber adhesion assay, ELISA, microarray analysis, or colorimetric assays. Methods may further include one or more of electrospray ionization mass spectrometry (ESI-MS), ESI-MS/MS, ESI-MS/(MS)n, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS), desorption/ionization on silicon (DIOS), secondary ion mass spectrometry (SFMS), quadrupole time-of-flight (Q-TOF), atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS/MS, APCI-(MS)n, atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS/MS, and APPI-(MS)n, quadrupole mass spectrometry, fourier transform mass spectrometry (FTMS), and ion trap mass spectrometry, where n is an integer greater than zero. Thus, the method of detection may relate to a probe that is capable of hybridizing to the biomarker DNA or RNA sequence. The term “probe” may defined to be an oligonucleotide. A probe can be single stranded at the time of hybridization to a target. Probes include but are not limited to primers, i.e., oligonucleotides that can be used to prime a reaction, for example at least in a PCR reaction.
Preferably, a decrease in expression, amount and/or activity of TNF-1, GSTA1, NT-proBNP, RORA and/or TNC, when compared to the reference, is indicative of an individual having a higher risk of suffering from cardiovascular disease or a negative prognosis.
Preferably, an increase in expression, amount and/or activity of GHR, A2M, IGFBP2, APOB, SEPP1, TFF3, IL6 and/or CHI3L1, when compared to the reference, is indicative of an individual having a higher risk of suffering from cardiovascular disease or a negative prognosis.
Preferably, a decrease in expression, amount and/or activity of MET, GDF15, CCL22, TNFRSF11, ANGPT2 and/or ReIA NF-KB, when compared to the reference, is indicative of an individual having a lower risk of suffering from cardiovascular disease or a positive prognosis.
Preferably, step a) comprises detecting, in a sample obtained from the individual, the expression levels, amount and/or activities of at least 3 of the biomarkers selected from the group consisting of: TNF-1; GSTA1; NT-proBNP; RORA; TNC; GHR; A2M; IGFBP2; APOB; SEPP1; TFF3; IL6; CHI3L1; MET; GDF15; CCL22; TNFRSF11; ANGPT2; and ReIA NF-KB. However, step a) may comprise detecting expression levels, amount and/or activities of at least 4 biomarkers or at least 5 biomarkers.
Step a) may comprise detecting expression levels, amount and/or activities of at least 6 biomarkers or at least 7 biomarkers. Alternatively, step a) may comprise detecting expression levels, amount and/or activities of at least 8 biomarkers or at least 9 biomarkers. In another embodiment, step a) may comprise detecting expression levels, amount and/or activities of at least 10 biomarkers, at least 11 biomarkers, at least 12 biomarkers, at least 13 biomarkers, at least 14 biomarkers or at least 15 biomarkers. In another embodiment, step a) may comprise detecting expression levels, amount and/or activities of at least 16 biomarkers, at least 17 biomarkers or at least 18 biomarkers.
Preferably, step a) comprises detecting, in a sample obtained from the individual, the expression levels, amount and/or activities of the biomarkers: TNF-□; GSTA1; NT-proBNP; RORA; TNC; GHR; A2M; IGFBP2; APOB; SEPP1; TFF3; IL6; CHI3L1; MET; GDF15; CCL22; TNFRSF11; ANGPT2 and ReIA NF-KB.
The cardiovascular disease may be selected from the group consisting of: cardiovascular death; myocardial infarction; stroke; and heart failure.
In one embodiment, RORA is provided by gene bank locus ID: HGNC: 10258; Entrez Gene: 6095; and/or Ensembl: ENSG00000069667. The protein sequence may be represented by the GeneBank ID P35398-2, which is provided herein as SEQ ID No: 1, as follows:
Accordingly, preferably RORA comprises or consists of an amino acid sequence substantially as set out in SEQ ID NO: 1, or a fragment or variant thereof.
In one embodiment, RORA is encoded by a nucleotide sequence which is provided herein as SEQ ID No: 2, as follows:
Accordingly, preferably RORA comprises or consists of a nucleotide sequence substantially as set out in SEQ ID NO: 2, or a fragment or variant thereof.
→In one embodiment, GHR is provided by gene bank locus ID: HGNC: 4263; Entrez Gene: 2690; and/or Ensembl: ENSG00000112964. The protein sequence is represented by the GeneBank ID P10912, which is provided herein as SEQ ID No: 4, as follows:
Accordingly, preferably GHR comprises or consists of an amino acid sequence substantially as set out in SEQ ID NO: 4, or a fragment or variant thereof.
In one embodiment, GHR is encoded by a nucleotide sequence, which is provided herein as SEQ ID No: 5, as follows:
Accordingly, preferably GHR comprises or consists of a nucleotide sequence substantially as set out in SEQ ID NO: 5, or a fragment or variant thereof.
In one embodiment, TNF-□ is provided by gene bank locus ID: HGNC: 11892; Entrez Gene: 7124; and/or Ensembl: ENSG00000232810. The protein sequence may be represented by the GeneBank ID P01375, which is provided herein as SEQ ID No: 6, as follows:
Accordingly, preferably TNF-□ comprises or consists of an amino acid sequence as substantially as set out in SEQ ID NO: 6, or a fragment or variant thereof.
In one embodiment TNF-□ is encoded by a nucleotide sequence which is provided herein as SEQ ID No: 7, as follows:
Accordingly, preferably TNF-□ comprises or consists of a nucleotide sequence as substantially as set out in SEQ ID NO: 7, or a fragment or variant thereof.
In one embodiment, GSTA1 is provided by gene bank locus ID: HGNC: 4626; Entrez Gene: 2938; Ensembl: ENSG00000243955; OMIM: 138359; and/or UniProtKB: P08263. The protein sequence may be represented by the GeneBank ID: ENST00000334575.6, which is provided herein as SEQ ID No: 8, as follows:
Accordingly, preferably GSTA1 comprises or consists of an amino acid sequence substantially as set out in SEQ ID NO: 8, or a fragment or variant thereof.
In one embodiment GSTA1 is encoded by a nucleotide sequence which is provided herein as SEQ ID No: 9, as follows:
Accordingly, preferably GSTA1 comprises or consists of a nucleotide sequence as substantially as set out in SEQ ID NO: 9, or a fragment or variant thereof.
In one embodiment, NT-proBNP is provided by gene bank locus ID: HGNC: 7940; Entrez Gene: 4879; Ensembl: ENSG00000120937; OMIM: 600295; and/or UniProtKB: P16860. The protein sequence may be represented by the GeneBank ID P16860, which is provided herein as SEQ ID No: 10, as follows:
Accordingly, preferably NT-proBNP comprises or consists of an amino acid sequence substantially as set out in SEQ ID NO: 10, or a fragment or variant thereof.
In one embodiment NT-proBNP is encoded by a nucleotide sequence which is provided herein as SEQ ID No: 11, as follows:
Accordingly, preferably NT-proBNP comprises or consists of a nucleotide sequence substantially as set out in SEQ ID NO: 11, or a fragment or variant thereof.
In one embodiment, TNC is provided by gene bank locus ID: HGNC: 5318; Entrez Gene: 3371; Ensembl: ENSG00000041982; OMIM: 187380; and/or UniProtKB: P24821. The protein sequence may be represented by the GeneBank ID P24821, which is provided herein as SEQ ID No: 12, as follows:
Accordingly, preferably TNC comprises or consists of an amino acid sequence substantially as set out in SEQ ID NO: 12, or a fragment or variant thereof.
In one embodiment TNC is encoded by a nucleotide sequence which is provided herein as SEQ ID No: 13, as follows:
Accordingly, preferably TNC comprises or consists of a nucleotide sequence substantially as set out in SEQ ID NO: 13, or a fragment or variant thereof.
In one embodiment, A2M is provided by gene bank locus ID: HGNC: 7; Entrez Gene: 2; Ensembl: ENSG00000175899; OMIM: 103950; and/or UniProtKB: P01023. The protein sequence may be represented by the GeneBank ID P01023, which is provided herein as SEQ ID No: 14, as follows:
Accordingly, preferably A2M comprises or consists of an amino acid sequence substantially as set out in SEQ ID NO: 14, or a fragment or variant thereof.
In one embodiment A2M is encoded by a nucleotide sequence which is provided herein as SEQ ID No: 15, as follows:
Accordingly, preferably A2M comprises or consists of a nucleotide sequence substantially as set out in SEQ ID NO: 15, or a fragment or variant thereof.
In one embodiment, IGFBP2 is provided by gene bank locus ID HGNC: 5471; Entrez Gene: 3485; Ensembl: ENSG00000115457; OMIM: 146731 and/or UniProtKB: P18065.
The protein sequence may be represented by the GeneBank ID P18065, which is provided herein as SEQ ID No: 16, as follows:
Accordingly, preferably IGFBP2 comprises or consists of an amino acid sequence substantially as set out in SEQ ID NO: 16 or a fragment or variant thereof.
In one embodiment IGFBP2 is encoded by a nucleotide sequence which is provided herein as SEQ ID No: 17, as follows:
Accordingly, preferably IGFBP2 comprises or consists of a nucleotide sequence substantially as set out in SEQ ID NO: 17, or a fragment or variant thereof.
In one embodiment, APOB is provided by gene bank locus ID: HGNC: 603; Entrez Gene: 338; Ensembl: ENSG00000084674; OMIM: 107730; and/or UniProtKB: P04114. The protein sequence may be represented by the GeneBank ID P04114, which is provided herein as SEQ ID No: 18, as follows:
Accordingly, preferably APOB comprises or consists of an amino acid sequence substantially as set out in SEQ ID NO: 18, or a fragment or variant thereof.
In one embodiment APOB is encoded by a nucleotide sequence which is provided herein as SEQ ID No: 19, as follows:
Accordingly, preferably APOB comprises or consists of a nucleotide sequence substantially as set out in SEQ ID NO: 19, or a fragment or variant thereof.
In one embodiment, SEPP1 is provided by gene bank locus ID: HGNC: 10751; Entrez Gene: 6414; Ensembl: ENSG00000250722; OMIM: 601484; and/or UniProtKB: P49908. The protein sequence may be represented by the GeneBank ID P49908, which is provided herein as SEQ ID No: 20, as follows:
Accordingly, preferably SEPP1 comprises or consists of an amino acid sequence substantially as set out in SEQ ID NO: 20, or a fragment or variant thereof.
In one embodiment SEPP1 is encoded by a nucleotide sequence which is provided herein as SEQ ID No: 21 as follows:
Accordingly, preferably SEPP1 comprises or consists of a nucleotide sequence substantially as set out in SEQ ID NO: 21, or a fragment or variant thereof.
In one embodiment, TFF3 is provided by gene bank locus ID: HGNC: 11757; Entrez Gene: 7033; Ensembl: ENSG00000160180; OMIM: 600633; and/or UniProtKB: Q07654. The protein sequence may be represented by the GeneBank ID 007654, which is provided herein as SEQ ID No: 22, as follows:
Accordingly, preferably TFF3 comprises or consists of an amino acid sequence substantially as set out in SEQ ID NO: 22, or a fragment or variant thereof.
In one embodiment TFF3 is encoded by a nucleotide sequence which is provided herein as SEQ ID No: 23, as follows:
Accordingly, preferably TFF3 comprises or consists of a nucleotide sequence substantially as set out in SEQ ID NO: 23, or a fragment or variant thereof.
In one embodiment, IL6 is provided by gene bank locus ID; HGNC: 6018; Entrez Gene: 3569; Ensembl: ENSG00000136244; OMIM: 147620; and/or UniProtKB: P05231. The protein sequence may be represented by the GeneBank ID P05231, which is provided herein as SEQ ID No: 24, as follows:
Accordingly, preferably IL6 comprises or consists of an amino acid sequence substantially as set out in SEQ ID NO: 24 or a fragment or variant thereof.
In one embodiment IL6 is encoded by a nucleotide sequence which is provided herein as SEQ ID No: 25, as follows:
Accordingly, preferably IL6 comprises or consists of a nucleotide sequence substantially as set out in SEQ ID NO: 25, or a fragment or variant thereof.
In one embodiment, CHI3L1 is provided by gene bank locus ID; HGNC: 1932; Entrez Gene: 1116; Ensembl: ENSG00000133048; OMIM: 601525; and/or UniProtKB: P36222. The protein sequence may be represented by the GeneBank ID P36222, which is provided herein as SEQ ID No: 26 as follows:
Accordingly, preferably CHI3L1 comprises or consist of an amino acid sequence substantially as set out in SEQ ID NO: 26, or a fragment or variant thereof.
In one embodiment CHI3L1 is encoded by a nucleotide sequence which is provided herein as SEQ ID No: 27, as follows:
Accordingly, preferably CHI3L1 comprises or consists of a nucleotide sequence substantially as set out in SEQ ID NO: 27, or a fragment or variant thereof.
In one embodiment, MET is provided by gene bank locus ID: HGNC: 7029; Entrez Gene: 4233; Ensembl: ENSG00000105976; OMIM: 164860; and/or UniProtKB: P08581. The protein sequence may be represented by the GeneBank ID P08581, which is provided herein as SEQ ID No: 28, as follows:
Accordingly, preferably MET comprises or consist of an amino acid sequence substantially as set out in SEQ ID NO: 28, or a fragment or variant thereof.
In one embodiment MET is encoded by a nucleotide sequence which is provided herein as SEQ ID No: 29, as follows:
Accordingly, preferably MET comprises or consist of a nucleotide sequence substantially as set out in SEQ ID NO: 29, or a fragment or variant thereof.
In one embodiment, GDF15 is provided by gene bank locus ID: HGNC: 30142; Entrez Gene: 9518; Ensembl: ENSG00000130513; OMIM: 605312; and/or UniProtKB: Q99988. The protein sequence may be represented by the GeneBank ID Q99988, which is provided herein as SEQ ID No: 30, as follows:
Accordingly, preferably GDF15 comprises or consists of an amino acid sequence substantially as set out in SEQ ID NO: 30, or a fragment or variant thereof.
In one embodiment GDF15 is encoded by a nucleotide sequence which is provided herein as SEQ ID No: 31, as follows:
Accordingly, preferably GDF15 comprises or consists of a nucleotide sequence substantially as set out in SEQ ID NO: 31, or a fragment or variant thereof.
In one embodiment, CCL22 is provided by gene bank locus ID: HGNC: 10621; Entrez Gene: 6367; Ensembl: ENSG00000102962; OMIM: 602957; and/or UniProtKB: 000626. The protein sequence may be represented by the GeneBank ID 000626, which is provided herein as SEQ ID No: 32, as follows:
Accordingly, preferably CCL22 comprises or consists of an amino acid sequence substantially as set out in SEQ ID NO: 32, or a fragment or variant thereof.
In one embodiment CCL22 is encoded by a nucleotide sequence which is provided herein as SEQ ID No: 33, as follows:
Accordingly, preferably CCL22 comprises or consists of a nucleotide sequence substantially as set out in SEQ ID NO: 33 or a fragment or variant thereof.
In one embodiment, TNFRSF11 is provided by gene bank locus ID: HGNC: 11909; Entrez Gene: 4982; Ensembl: ENSG00000164761; OMIM: 602643; and/or UniProtKB: 000300. The protein sequence may be represented by the GeneBank ID 000300, which is provided herein as SEQ ID No: 34, as follows:
Accordingly, preferably TNFRSF11 comprises or consists of an amino acid sequence substantially as set out in SEQ ID NO: 34, or a fragment or variant thereof.
In one embodiment TNFRSF11 is be encoded by a nucleotide sequence which is provided herein as SEQ ID No: 35, as follows:
Accordingly, preferably TNFRSF11 comprises or consists of a nucleotide sequence substantially as set out in SEQ ID NO: 35, or a fragment or variant thereof.
In one embodiment, ANGPT2 is provided by gene bank locus ID is HGNC: 485; Entrez Gene: 285; Ensembl: ENSG00000091879; OMIM: 601922; and/or UniProtKB: 015123. The protein sequence may be represented by the GeneBank ID 015123, which is provided herein as SEQ ID No: 36, as follows:
Accordingly, preferably ANGPT2 comprises or consists of an amino acid sequence substantially as set out in SEQ ID NO: 36, or a fragment or variant thereof.
In one embodiment ANGPT2 is encoded by a nucleotide sequence which is provided herein as SEQ ID No: 37, as follows:
Accordingly, preferably ANGPT2 comprises or consists of a nucleotide sequence substantially as set out in SEQ ID NO: 37, or a fragment or variant thereof.
In one embodiment, ReIA NF-KB is provided by gene bank locus ID: HGNC: 9955; Entrez Gene: 5970; Ensembl: ENSG00000173039; OMIM: 164014; and/or UniProtKB: Q04206. The protein sequence may be represented by the GeneBank ID Q04206, which is provided herein as SEQ ID No: 38, as follows:
Accordingly, preferably ReIA NF-KB comprises or consists of an amino acid sequence substantially as set out in SEQ ID NO: 38, or a fragment or variant thereof.
In one embodiment ReIA NF-KB is be encoded by a nucleotide sequence which is provided herein as SEQ ID No: 39, as follows:
Accordingly, preferably ReIA NF-KB comprises or consists of a nucleotide sequence substantially as set out in SEQ ID NO: 39, or a fragment or variant thereof.
In one embodiment, the biomarker is RORA, and a decrease in the expression, amount and/or activity of RORA when compared to a reference is indicative of an individual having a higher risk of suffering from cardiovascular disease.
In one embodiment, the biomarker is GHR, and an increase in the expression, amount and/or activity of GHR when compared to a reference is indicative of an individual having a higher risk of suffering from cardiovascular disease.
Preferably, the sample comprises a biological sample. The sample may be any material that is obtainable from the subject from which protein, RNA and/or DNA is obtainable. Furthermore, the sample may be blood, plasma, serum, spinal fluid, urine, sweat, saliva, tears, breast aspirate, prostate fluid, seminal fluid, vaginal fluid, stool, cervical scraping, cytes, amniotic fluid, intraocular fluid, mucous, moisture in breath, animal tissue, cell lysates, tumour tissue, hair, skin, buccal scrapings, lymph, interstitial fluid, nails, bone marrow, cartilage, prions, bone powder, ear wax, or combinations thereof.
Preferably, however, the sample comprises blood, urine or tissue.
In one embodiment, the sample comprises a blood sample. The blood may be venous or arterial blood. Blood samples may be assayed immediately.
Alternatively, the blood sample may be stored at low temperatures, for example in a fridge or even frozen before the method is conducted. Detection may be carried out on whole blood. Preferably, however, the blood sample comprises blood serum. Preferably, the blood sample comprises blood plasma.
The blood may be further processed before the method is performed. For instance, an anticoagulant, such as citrate (such as sodium citrate), hirudin, heparin, PPACK, or sodium fluoride may be added. Thus, the sample collection container may contain an anticoagulant in order to prevent the blood sample from clotting. Alternatively, the blood sample may be centrifuged or filtered to prepare a plasma or serum fraction, which may be used for analysis. Hence, it is preferred that the method is performed in a blood plasma or a blood serum sample. It is preferred that the expression level, amount and/or activity of the biomarker is measured in vitro from a blood serum sample or a plasma sample taken from the individual.
The invention also provides for a kit for determining, diagnosing and/or prognosing CVD risk.
Accordingly, in a second aspect, there is provided a kit for determining, diagnosing and/or prognosing the risk of an individual suffering from cardiovascular disease, the kit comprising:
The cardiovascular disease, the biomarker, detection and the sample may be as defined in the first aspect.
Preferably, a decrease in expression, amount and/or activity of TNF-1, GSTA1, NT-proBNP, RORA and/or TNC, when compared to the reference, is indicative of an individual having a higher risk of suffering from cardiovascular disease or a negative prognosis.
Preferably, an increase in expression, amount and/or activity of GHR, A2M, IGFBP2, APOB, SEPP1, TFF3, IL6 and/or CHI3L1, when compared to the reference, is indicative of an individual having a higher risk of suffering from cardiovascular disease or a negative prognosis.
Preferably, a decrease in expression, amount and/or activity of MET, GDF15, CCL22, TNFRSF11, ANGPT2 and/or ReIA NF-KB, when compared to the reference, is indicative of an individual having a lower risk of suffering from cardiovascular disease or a positive prognosis.
The expression levels, amount and/or activities of the biomarkers may be as defined in the first aspect.
The kit may comprise detection means for detecting the expression levels, amount and/or activities of at least 3 biomarkers or at least 4 biomarkers. The kit may comprise detection means for detecting the expression levels, amount and/or activities of at least 5 biomarkers. The kit may comprise detection means for detecting the expression levels, amount and/or activities of at least 6 biomarkers or at least 7 biomarkers. Alternatively, the kit may comprise detection means for detecting the expression levels, amount and/or activities of at least 8 biomarkers or at least 9 biomarkers. In another embodiment, the kit may comprise detection means for detecting the expression levels, amount and/or activities of at least 10 biomarkers, at least 11 biomarkers, at least 12 biomarkers, at least 13 biomarkers, at least 14 biomarkers or at least 15 biomarkers. In another embodiment, the kit may comprise detection means for detecting the expression levels, amount and/or activities of at least 16 biomarkers, at least 17 biomarkers or at least 18 biomarkers.
Preferably, the kit of the second aspect may be for determining, diagnosing and prognosing the risk of an individual suffering from cardiovascular disease, the kit comprising:
The detection means may detect the expression level, for example the level or concentration, of a biomarker polynucleotide sequence, for example DNA or RNA. The DNA may be genomic DNA. The RNA may be mRNA. Alternatively, the detection means may detect polypeptide concentration and/or activity of the biomarker.
Accordingly, the detection means may include: sequencing methods (e.g., Sanger, Next Generation Sequencing, RNA-SEQ), hybridization-based methods, including those employed in biochip arrays, mass spectrometry (e.g., laser desorption/ionization mass spectrometry), fluorescence (e.g., sandwich immunoassay), surface plasmon resonance, ellipsometry and atomic force microscopy. Expression levels of markers (e.g., polynucleotides, polypeptides, or other analytes) may be compared by procedures well known in the art, such as RT-PCR, Northern blotting, Western blotting, flow cytometry, immunocytochemistry, binding to magnetic and/or antibody-coated beads, in situ hybridization, fluorescence in situ hybridization (FISH), flow chamber adhesion assay, ELISA, microarray analysis, or colorimetric assays. Methods may further include one or more of electrospray ionization mass spectrometry (ESI-MS), ESI-MS/MS, ESI-MS/(MS)n, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS), desorption/ionization on silicon (DIOS), secondary ion mass spectrometry (SFMS), quadrupole time-of-flight (Q-TOF), atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS/MS, APCI-(MS)n, atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS/MS, and APPI-(MS)n, quadrupole mass spectrometry, fourier transform mass spectrometry (FTMS), and ion trap mass spectrometry, where n is an integer greater than zero.
Preferably, the kit comprises detection means for detecting RORA present in a sample from a test subject, wherein a decrease in the expression, amount and/or activity of RORA indicative of an individual having a higher risk of suffering from cardiovascular disease.
Preferably, the kit comprises detection means for detecting GHR present in a sample from a test subject, wherein an increase in the expression, amount and/or activity of GHR indicative of an individual having a higher risk of suffering from cardiovascular disease.
Using the methods described herein, the inventors have been able to identify SNPs within RORA and GHR that may be used in diagnosis and prognosis, and in particular gene variants that are associated with CVD risk.
Accordingly, in a third aspect of the invention, there is provided a method of determining, diagnosing and/or prognosing an individual's risk of suffering from cardiovascular disease, the method comprising detecting, in a sample obtained from a individual, a single nucleotide polymorphism (SNP) in the RORA gene, wherein the presence of the SNP is indicative of an individual having an increased risk of suffering from cardiovascular disease.
Preferably, RORA, the sample, detection and the cardiovascular disease is as defined in the first aspect.
The method may be performed in vivo, in vitro or ex vivo. Preferably, the method is performed in vitro or ex vivo. Most preferably, the method is performed in vitro.
Preferably, the SNP is present in a region of chromosome 15, preferably at nucleic acid position 60542728 of the reference sequence NC_000015.10.
Preferably, the SNP comprises a substitution of Adenine (A) to Guanine (G) or Adenine (A) to Cytosine (C).
Preferably, the SNP comprises a substitution of Adenine (A) to Guanine (G) Thus, preferably, the SNP may be referred to by the sequence variant GRCh38.p12 chr 15; NC 000015.10:g.60542728A>G.
Preferably, the SNP comprises a substitution of Adenine (A) to Cytosine (C).
Thus, preferably, the SNP may be referred to by the sequence variant GRCh38.p12 chr 15; NC 000015.10:g.60542728A>C.
Preferably, the SNP may be Reference SNP cluster ID: rs73420079.
Thus, in one embodiment, SNP is present in the sequence represented by Reference SNP cluster ID rs73420079, referred to herein as SEQ ID No: 3, as follows:
Where “V” represent the SNP position. Accordingly, in one embodiment, SNP may comprise or consist of the sequence as substantially set out in SEQ ID No: 3, or a fragment or variant thereof.
Preferably, the SNP comprises a substitution of nucleic acid position X in SEQ D No: 3.
Thus, preferably RORA comprises a single nucleotide polymorphism (SNP), the presence of which is associated with an individual having an increased risk of suffering from CVD.
Preferably, the method of detecting the presence of the SNP comprises a probe that is capable of hybridizing to the biomarker sequence. Preferably, the probe is capable of hybridizing to SEQ ID No 3 such that the SNP is detected.
In a fourth aspect of the invention, there is provided a method of determining, diagnosing and/or prognosing a individual's risk of suffering from cardiovascular disease, the method comprising detecting, in a sample obtained from an individual, a single nucleotide polymorphism (SNP) in the GHR gene, wherein the presence of the SNP is indicative of an individual having an increased risk of suffering from cardiovascular disease.
Preferably, GHR, the sample, detection and the cardiovascular disease is as defined in the first aspect.
Preferably, detecting the SNP in a subject is indicative of an increased risk of suffering from cardiovascular disease.
The method may be performed in vivo, in vitro or ex vivo. Preferably, the method is performed in vitro or ex vivo. Most preferably, the method is performed in vitro.
Preferably, the SNP is present in a region of chromosome 5, preferably at nucleic acid position 42546623 of the reference sequence NC_000005.10.
Preferably, the SNP comprises a substitution of Guanine (G) to Adenine (A).
Preferably, the SNP may be Reference SNP cluster ID rs4314405.
Thus, in one embodiment, SNP is present in the sequence represented by Reference SNP cluster ID: rs73420079, referred to herein as SEQ ID No: 40, as follows:
Where “V” represent the SNP position. Accordingly, in one embodiment, SNP may comprise or consist of the sequence as substantially set out in SEQ ID No: 40, or a fragment or variant thereof.
Preferably, the SNP comprises a substitution of nucleic acid position X in SEQ ID No: 3.
Thus, preferably GHR comprises a single nucleotide polymorphism (SNP), the presence of which is associated with an individual having an increased risk of suffering from CVD.→
In a fifth aspect, there is provided GHR and/or RORA, for use in diagnosis or prognosis.
Preferably, RORA and GHR may comprise a SNP as defined in the third and fourth aspects. The cardiovascular disease may be as defined in the first aspect.
In a sixth aspect, there is provided GHR and/or RORA, for use in diagnosing or prognosing an individual's risk of suffering from cardiovascular disease.
Preferably, RORA and GHR may comprise a SNP as defined in the third and fourth aspects. The cardiovascular disease may be as defined in the first aspect.
Preferably, the method of detecting the presence of the SNP comprises a probe that is capable of hybridizing to the biomarker sequence. Preferably, the probe is capable of hybridizing to SEQ ID No 40 such that the SNP is detected.
In a seventh aspect, there is provided a kit for determining, diagnosing and/or prognosing an individual's risk of suffering from cardiovascular disease, the kit comprising a detection means for detecting, in a sample obtained from a test subject, a single nucleotide polymorphism (SNP) in the RORA gene and/or GHR gene, wherein the presence of the SNP is used to determine, diagnose and/or prognose that an individual has a higher risk of suffering from cardiovascular disease.
The RORA gene, the GHR gene, the sample, detection and the cardiovascular disease may be as defined in the first aspect.
The detection means may be as defined in the second aspect. The single nucleotide polymorphism (SNP) in the RORA gene and/or GHR gene may be as defined in the third and fourth aspects.
In an eighth aspect, there is provided a method of treating an individual having a higher risk of suffering from cardiovascular disease, the method comprising:—
Preferably, the biomarkers, detection of the biomarkers, the cardiovascular disease, the expression levels, amount and/or activities of the biomarkers and the sample are as defined in the first aspect.
Preferably, the method of treatment comprises analysing and comparing the expression levels, amount and/or activities of 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18 or more of the biomarkers: TNF-□; GSTA1; NT-proBNP; RORA; TNC; GHR; A2M; IGFBP2; APOB; SEPP1; TFF3; IL6; CHI3L1; MET; GDF15; CCL22; TNFRSF11; ANGPT2 and ReIA NF-KB.
The method of treatment may comprise analysing and comparing the expression levels, amount and/or activities of at least 6 biomarkers or at least 7 biomarkers. Alternatively, the method of treatment may comprise analysing and comparing the expression levels, amount and/or activities of at least 8 biomarkers or at least 9 biomarkers. In another embodiment, the method of treatment may comprise analysing and comparing the expression levels, amount and/or activities of at least 10 biomarkers, at least 11 biomarkers, at least 12 biomarkers, at least 13 biomarkers, at least 14 biomarkers or at least 15 biomarkers. In another embodiment, the method of treatment may comprise analysing and comparing the expression levels, amount and/or activities of at least 16 biomarkers, at least 17 biomarkers or at least 18 biomarkers.
Preferably, the method of treatment comprises analysing and comparing the expression levels, amount and/or activities of the biomarkers: TNF-□; GSTA1; NT-proBNP; RORA; TNC; GHR; A2M; IGFBP2; APOB; SEPP1; TFF3; IL6; CHI3L1; MET; GDF15; CCL22; TNFRSF11; ANGPT2 and ReIA NF-KB
A clinician would be able to make a decision as to the preferred course of treatment required, for example, the type and dosage of the therapeutic agent according to the eighth and ninth aspects to be administered.
Suitable therapeutic agents may include: statins, including the statins selected from the group consisting of: atorvastatin; simvastatin; rosuvastatin; and pravastatin, beta blockers, blood thinning agents including the blood thinning agents selected from the group consisting of: low-dose aspirin; clopidogrel; rivaroxaban; ticagrelor and prasugrel, nitrates, angiotensin-converting enzyme (ACE) inhibitors, angiotensin II receptor antagonists, calcium channel blockers, including the calcium channel blockers selected from the group consisting of: amlodipine; verapamil and diltiazem and/or diuretics.
Treatment may include enacting lifestyle changes.
In an ninth aspect, there is also provided a method of treating an individual having a higher risk of suffering from cardiovascular disease, the method comprising:—
Preferably, the biomarkers, detection of the biomarkers, the cardiovascular disease, the expression levels, amount and/or activities of the biomarkers and the sample are as defined in the first aspect.
Preferably, the single nucleotide polymorphism (SNP) RORA and/or GHR is as defined in the third and/or fourth aspect.
A clinician would be able to make a decision as to the preferred course of treatment required, for example, the type and dosage of the therapeutic agent according to the eighth and ninth aspects to be administered.
Suitable therapeutic agents may include: statins, including the statins selected from the group consisting of: atorvastatin; simvastatin; rosuvastatin; and pravastatin, beta blockers, blood thinning agents including the blood thinning agents selected from the group consisting of: low-dose aspirin; clopidogrel; rivaroxaban; ticagrelor and prasugrel, nitrates, angiotensin-converting enzyme (ACE) inhibitors, angiotensin II receptor antagonists, calcium channel blockers, including the calcium channel blockers selected from the group consisting of: amlodipine; verapamil and diltiazem and/or diuretics.
Treatment may include enacting lifestyle changes.
It will be appreciated that the invention extends to any nucleic acid or peptide or variant, derivative or analogue thereof, which comprises substantially the amino acid or nucleic acid sequences of any of the sequences referred to herein, including variants or fragments thereof. The terms “substantially the amino acid/nucleotide/peptide sequence”, “variant” and “fragment”, can be a sequence that has at least 40% sequence identity with the amino acid/nucleotide/peptide sequences of any one of the sequences referred to herein, for example 40% identity with the sequence identified as SEQ ID Nos: 1 to 40 and so on.
Amino acid/polynucleotide/polypeptide sequences with a sequence identity which is greater than 65%, more preferably greater than 70%, even more preferably greater than 75%, and still more preferably greater than 80% sequence identity to any of the sequences referred to are also envisaged. Preferably, the amino acid/polynucleotide/polypeptide sequence has at least 85% identity with any of the sequences referred to, more preferably at least 90% identity, even more preferably at least 92% identity, even more preferably at least 95% identity, even more preferably at least 97% identity, even more preferably at least 98% identity and, most preferably at least 99% identity with any of the sequences referred to herein.
The skilled technician will appreciate how to calculate the percentage identity between two amino acid/polynucleotide/polypeptide sequences. In order to calculate the percentage identity between two amino acid/polynucleotide/polypeptide sequences, an alignment of the two sequences must first be prepared, followed by calculation of the sequence identity value.
The percentage identity for two sequences may take different values depending on:—(i) the method used to align the sequences, for example, ClustalW, BLAST, FASTA, Smith-Waterman (implemented in different programs), or structural alignment from 3D comparison; and (ii) the parameters used by the alignment method, for example, local vs global alignment, the pair-score matrix used (e.g. BLOSUM62, PAM250, Gonnet etc.), and gap-penalty, e.g. functional form and constants.
Having made the alignment, there are many different ways of calculating percentage identity between the two sequences. For example, one may divide the number of identities by: (i) the length of shortest sequence; (ii) the length of alignment; (iii) the mean length of sequence; (iv) the number of non-gap positions; or (v) the number of equivalenced positions excluding overhangs. Furthermore, it will be appreciated that percentage identity is also strongly length dependent. Therefore, the shorter a pair of sequences is, the higher the sequence identity one may expect to occur by chance.
Hence, it will be appreciated that the accurate alignment of protein or DNA sequences is a complex process. The popular multiple alignment program ClustalW (Thompson et al., 1994, Nucleic Acids Research, 22, 4673-4680; Thompson et al., 1997, Nucleic Acids Research, 24, 4876-4882) is a preferred way for generating multiple alignments of proteins or DNA in accordance with the invention. Suitable parameters for ClustalW may be as follows: For DNA alignments: Gap Open Penalty=15.0, Gap Extension Penalty=6.66, and Matrix=Identity. For protein alignments: Gap Open Penalty=10.0, Gap Extension Penalty=0.2, and Matrix=Gonnet. For DNA and Protein alignments: ENDGAP=−1, and GAPDIST=4. Those skilled in the art will be aware that it may be necessary to vary these and other parameters for optimal sequence alignment.
Preferably, calculation of percentage identities between two amino acid/polynucleotide/polypeptide sequences may then be calculated from such an alignment as (N/T)*100, where N is the number of positions at which the sequences share an identical residue, and T is the total number of positions compared including gaps and either including or excluding overhangs. Preferably, overhangs are included in the calculation. Hence, a most preferred method for calculating percentage identity between two sequences comprises (i) preparing a sequence alignment using the ClustalW program using a suitable set of parameters, for example, as set out above; and (ii) inserting the values of N and T into the following formula:—Sequence Identity=(N/T)*100.
Alternative methods for identifying similar sequences will be known to those skilled in the art. For example, a substantially similar nucleotide sequence will be encoded by a sequence which hybridizes to DNA sequences or their complements under stringent conditions. By stringent conditions, the inventors mean the nucleotide hybridises to filter-bound DNA or RNA in 3× sodium chloride/sodium citrate (SSC) at approximately 45° C. followed by at least one wash in 0.2×SSC/0.1% SDS at approximately 20-65° C. Alternatively, a substantially similar polypeptide may differ by at least 1, but less than 5, 10, 20, 50 or 100 amino acids from the sequences shown in, for example, SEQ ID Nos: 1 to 40 and so on.
Due to the degeneracy of the genetic code, it is clear that any nucleic acid sequence described herein could be varied or changed without substantially affecting the sequence of the protein encoded thereby, to provide a functional variant thereof. Suitable nucleotide variants are those having a sequence altered by the substitution of different codons that encode the same amino acid within the sequence, thus producing a silent (synonymous) change. Other suitable variants are those having homologous nucleotide sequences but comprising all, or portions of, sequence, which are altered by the substitution of different codons that encode an amino acid with a side chain of similar biophysical properties to the amino acid it substitutes, to produce a conservative change. For example, small non-polar, hydrophobic amino acids include glycine, alanine, leucine, isoleucine, valine, proline, and methionine. Large non-polar, hydrophobic amino acids include phenylalanine, tryptophan and tyrosine. The polar neutral amino acids include serine, threonine, cysteine, asparagine and glutamine. The positively charged (basic) amino acids include lysine, arginine and histidine. The negatively charged (acidic) amino acids include aspartic acid and glutamic acid. It will therefore be appreciated which amino acids may be replaced with an amino acid having similar biophysical properties, and the skilled technician will know the nucleotide sequences encoding these amino acids.
All of the features described herein (including any accompanying claims, abstract and drawings), and/or all of the steps of any method or process so disclosed, may be combined with any of the above aspects in any combination, except combinations where at least some of such features and/or steps are mutually exclusive.
For a better understanding of the invention, and to show how embodiments of the same may be carried into effect, reference will now be made, by way of example, to the accompanying Figures, in which:—
a-c shows the networks of molecular entities that were prioritized in the overconnectivity analyses. To infer patient specific activity (Workflow
The inventors set out to identify biomarkers, and combinations of biomarkers, associated with CVD progression, with the aim of developing strategies to enable identification of individuals who are at risk from suffering from a CVD event, and thus enable early intervention to prevent, or reduce the risk of, the individual from suffering from a CVD event. Identification of suitable biomarkers and biomarker networks will also optimize clinical trials plans, drug efficacy, and optimize treatment.
The participants of the ORIGIN biomarker sub-study (N=8,401, Supplementary Table S1) were chosen among the randomized patients who were treated by Lantus or placebo. A smaller sub-population of this sample was genotyped (5,078 samples). After quality control (see below), sample size was 4,390 individuals. Only these genotyped subjects entered the analyses described in this protocol for patient stratification (demographic characteristics in Supplementary Table S1).
The quality control for the protein biomarkers measured in the ORIGIN cohort was previously described (Gerstein et al., 2015). Normalization=biomarkers that were not normally distributed were log transformed. Standard biomarker analyses for CVO prediction in this cohort were described elsewhere (Gerstein et al., 2015).
Quantitative BMK measurements were also transformed to categorical variables (−1, 0, +1) based on percentiles of the distributions for each BMK, separately for Caucasian, Latinos and subjects of African origin. The algorithm PARADIGM cannot deal with continuous traits for clustering.
Genotyping of the ORIGIN cohort, N=5,078 samples, was performed using Illumina HumanCore Exome DNA Analysis Bead Chip (Illumina Omni2.5). Over 540,000 genetic variants were called, including extensive coverage of coding variants, both common and rare. Single nucleotide polymorphisms (SNPs) were excluded with call rate <0.99, minor allele frequency <0.01, or deviation from Hardy-Weinberg equilibrium (P<1×10-6). Individuals were excluded if their self-reported sex, ethnicity and relatedness were not in concordance with their genetic information. After quality control, sample size was 4,390 individuals and 284,024 SNPs (note: SNPs excluded because of low allele frequency were accurately genotyped for the most part and will be included in future analyses). Genome-wide genotype imputation was performed using Impute v2.3.0. The inventors used the NCBI build 37 of Phase I integrated variant 1000 Genomes Haplotypes (SHAPEIT2) as the reference panel. Imputed SNPs were excluded with imputation certainty score <0.3. Final number of Imputed SNPs was 10,501,330.
Principal components were generated based on whole genome genotyping separately for Caucasian and Latinos. These were used as co-variates in genetic analysis for the genome-wide association study (GWAs) from the ORIGIN study.
Due to population ethnic substructure, subpopulations were defined by ethnic groups (Caucasian and Latinos) and analyzed separately. HWE (P>0.001) for the SNPs entering the analyses was tested as part of the quality control to define if the population subgroups met expected genotypes distributions. Subpopulations were meta-analyzed and association results were visualized in Manhattan and QQ plots (
GWA analyses were conducted separately for Caucasian (N=1,931) and Latinos (N=2,216). Genotypes consisting of both directly typed and imputed SNPs (N=4.9-9
Mio) entered the GWA analyses. To avoid over-inflation of test statistics due to population structure or relatedness, the inventors applied genomic controls for the GWA analysis. Principal components were generated based on whole genome genotyping separately for Caucasian and Latinos. These were used as co-variates in the genome-wide association analyses from the ORIGIN study.
Linear regression (PLINK) for associations with normalization was performed under an additive model, with SNP allele dosage as predictor and with age, gender.
Meta-analysis was performed. Corresponding to Bonferroni adjustment for one million independent tests, the inventors specified a threshold of P for genome-wide significance. The CARDIOGRAM consortium dataset used was the CARDIoGRAMplusC4D 1000 Genomes-based GWAS meta-analysis summary statistics. It comprised GWAS studies of mainly European, South Asian, and East Asian, descents imputed using the 1000 Genomes phase 1 v3 training set with 38 million variants. The study interrogated 9.4 million variants and involved 60,801 CVD cases and 123,504 controls (Nikpey et al., 2015). To assess the number of independent loci associated with CVD, correlated SNPs were grouped using a LD-based result clumping procedure (PLINK, Purcell et al, 2007). This procedure was used for gene mapping of loci (Supplementary Table S2) entering the overconnectivity network analysis. Variants associated with CVD at a p-value below 10-6 with proxies at a p-value below or equal 10-5 in ORIGIN cohort and p-value below 10-7 with proxies at a p-value below equal 10-6 in the CARDIOGRAM cohort were mapped to genes, so that these could be considered in the network analyses. The inventors excluded alleles with a MAF below 1 percent and poor imputation quality (Info below 0.4) from the clumping procedure.
All steps of the workflow (
A disease network was identified using the overconnectivity algorithm, as implemented in the R based Computational Biology for Drug discovery (CDDD) package developed by Clarivate Analytics. The specificity of the network for the disease relies on the disease linked molecules chosen to produce the network (
PARADIGM is a data integration approach based on probabilistic graphical models. It renders a pathway or network as a probabilistic graphical model (PGM), learning its parameters from supplied omics data sets. The model allows inference of true activity score for each node in the pathway given the different omics measurements for the nodes. PARADIGM allows prediction on the level of individual patients and is capable of accommodating such data types as gene/protein expression, copy number changes, metabolomics, direct protein activity assays such as kinase activity measurements. PARADIGM combines multiple genome-scale measurements at the sample level to infer the activities of genes, products and abstract process within a pathway or subnetwork. Edges of original network connect hidden variables of different nodes (e.g. activity hidden variable of node A affects protein or DNA hidden variable of node B, depending on mechanism of A-B link).
Each node is assigned a conditional probability distribution when the model is created. The distribution tells how likely it is to observe a node in particular state given states of its parents in the model Three states are allowed for each node (activated, repressed, unchanged). The distributions for hidden variables are defined at the first step. Distributions for observed variables of each molecular level are learned by EM algorithm using the input data. After model is complete, inference can be made about probabilities of observing hidden nodes in a particular state—either without observed data (prior probability) or taking data into account (posterior probability).
The main output is a matrix of integrated pathway activities (IPAs) A where A_ij represents the inferred activity of entity i in patient sample j. The values in A are signed and are non-zero if the patient data makes the activation or inhibition of the hidden node more likely compared to prior. The A is supposed to be used instead of original data sets for purposes such as patient stratification or association analysis to reveal biological entities with activity associated to clinical traits.
The output is a matrix of activity scores for each node in the network and each sample. The activity score represents signed log likelihood ratio (positive when the node is predicted to be active, negative when node is predicted to be repressed). A pathway is converted into a probabilistic graphical model that includes both hidden states for each node and observed states for the nodes which can correspond to the input data sets. There are two possible modes to assess IPA significance. Both involve permutation—calculation of IPA scores on many randomized samples.
For the ‘within’ permutation, a permuted data sample is created by creating new set of evidence (i.e. states for observed variables at gene expression and gene copy number) by assigning a value of the random node in pathway/subnetwork and random sample to each observed node.
For the ‘any’ permutation, the procedure is the same, but the random node selection step could choose a node from anywhere in the input data (regardless of whether a particular pathway/subnetwork contains such a node).
For both permutation types, iterations permuted samples are created, and the IPA scores for each permuted sample is calculated. The distribution of scores from permuted data is used as a null distribution to estimate the significance of IPA scores in real data set. SCRIPT: Paradigm (Vaske et al., 2010) in R with CBDD package developed by CLARIVATES.
Clusters were produced separately for the discovery (Caucasian; N=1908) and replication (Latinos; N=2146 sub-populations.
Biomarkers to be predicted as key members of the input networks:
TNF-alpha, IL6, ReIA NF-KB Subunit, NT-proBNP (NPPB)
The inventors initiated the patient stratification analyses based on the disease sub-networks identified in the ORIGIN cohort (
To verify the relevance of the identified patient clusters in both the Caucasian and Latino populations (
In the GWAs the inventors conducted with the ORIGIN cohort, the inventors identified a few variants associated or borderline associated to CVD (
These were mostly rare variants (MAF=0.01 in EUR), or did not map to a gene region (Supplementary Table S2). These variants were not present in the CARDIOGRAM dataset, and the inventors could not validate these findings using the Cardiovascular Disease Knowledge Portal (http://broadcvdi.org/home/portalHome). The inventors mapped GWAs results to nearby loci (Supplementary Tables S2) to inform the network analysis, identifying biological interactions of these loci to other CVD biomarkers (Supplementary Table S3). Results from the GWA analyses are shown in Table 1 for loci that were prioritized in the network analyses, all other findings are reported in
The inventors built a CVD network (
The prioritized sub-networks and their molecular directional interactions (
A relation of the clusters to 1st and 2nd co-primary composites of CVO was found in a second step through survival analyses (Kaplan-Maier survival estimates and Log rank analyses to test for equality of survival function) and Cox-regression models adjusted for CVO risk factors (
The distribution of CVO risk factors (Supplementary Table 1 and
Here, the inventors developed a workflow that goes from the identification of a CVD network to the proof of concept that the molecular interactions identified can be used to stratify patients with regard to disease progression. The inventor's computational biology approach has identified the group of biomarkers associated with CVD outcomes and has associated, for the first time, SNPs associated with CVD risk. The inventor's work has identified molecular interactions and shows interdependencies of molecular activities that associate with different stages of CVD progression.
These results may enable the identification of individuals who are at risk from suffering from a CVD event, and thus enables early intervention to prevent, or reduce the risk of, the individual from suffering from a CVD event. In particular, detection of each biomarker in isolation enables the identification of individuals who are at risk from suffering from a CVD event, and detection of multiple biomarkers, i.e. the biomarker signature provides a particularly effective means of enabling early intervention to prevent, or reduce the risk of, the individual from suffering from a CVD event.
This is the first evidence for the reported GHR and RORA variants to be associated with CVD, and Example 2 sets out the method of determining a subject's risk of cardiovascular disease based on detections of SNPs in GHR and RORA genes. Though the genetic evidence of association with CVD is not strong, RORA is known to regulate a number of genes involved in lipid metabolism such as apolipoproteins Al, APOA5, CIII, CYP71 and PPARgamma, possibly working as a receptor for cholesterol or one of its derivatives cite Uniprot). Additionally, overexpression of RORA isoforms suppresses TNFalpha induced expression of adhesion molecules in human umbilical vein endothelial cells, regulating inflammatory response (Migita et al., 2004). The inventor's network analyses shows that RORA and GHR interact with main regulatory hubs of the identified CVD network of BMKs. Consistent with that, their genotypes contributed to calculations of the network activity and to the clustering of patients, which were in turn related to CVD progression.
RORA and GHR connected to the main networks through upstream regulators (TNFalpha and IL6) common to the prioritized molecules. Though, TNFalpha and IL6 were not part of the input information to create the network, the inventors could uncover these as hidden main regulators of the input molecules. Links of TNFalpha and IL6 to CVD are known (Lopez-Candales et al., 2017)—but, the network allows the visualization of the directions of the molecular interactions (
One of the advantages of the Bayesian network analyses over single BMK analyses, is that the activity of BMKs that were not measured or had poor measurement quality in the patient sample (for instance TNFalpha, and IL6 in the ORIGIN trial) can be estimated, in special if these are central to the disease network. This can lead to the discovery of new BMKs of disease progression. TNFalpha and IL6 have been intensively studies in CVD models, but in humans their protein detection is somewhat cumbersome and their levels are also influenced by physical activity (Vijayaraghava et al., 2017).
Here, the inventors could infer their activities based on downstream interacting protein, from which measured levels were fed into the Bayesian model (
NPPB and TFF3 were the top ranking associated BMKs by conventional statistics in the ORIGIN cohort, but did not appear in the network constructed with the CARDIOGRAM gene set. As the overconnectivity algorithm identifies the shortest path connecting genes, the inventors, without wishing to be bound to any particular theory, can conclude that: i) there is an active biological network that connects GWAs associated genes to protein biomarkers that had been associated to CVD, to which TNFalpha plays a central role; ii) though NPPB and TFF3 clearly are relevant biomarkers of CVD, these appear to be more downstream to the cascade of events related to the TNFalpha than the gene set identified in the CARDIOGRAM study. Nevertheless, for the purpose of identifying a disease related network for BMK discovery, protein BMKs should be more relevant than loci associated with CVD, as GWA results often map to genes that do not encode circulating proteins. In the patient stratification analyses, the inventors used mainly circulating proteins (N=13 plus 2 genetic markers), making it feasible for translational application, as tissue specific samples are often difficult or not feasible to obtain in standard clinical practice.
Though the complexity of this computational approach does not make it a straight forward process to be used in the clinic, the obtained patient strata can be further investigated to identify ideal BMKs (including known clinical parameters) combinations and ratios that would represent different stages of disease progression.
Identifying clusters of sub-populations progressing differently towards CVO will be informative to: i) define how the molecular signatures of each cluster translate into combinations of biomarkers with prognostic value, and iii) to define how these markers will respond to treatment in an additional cohort. Thereby, molecular signatures of disease progression should lead to strategies to optimize clinical trials plans, and drug efficacy, and so to optimize treatment.
Supplementary TABLE S1. Study sample characteristics for the total sample, and high versus low CVO risk clusters for the Caucasian and Latino sub-populations.
Supplementary TABLE S2. List of molecular entities used in the overconnectivity analyses. Results from the GWA analyses conducted in ORIGIN and in the CARDIOGRAM consortium were assigned to loci (see Methods) and protein biomarkers to their respective genes.
Supplementary TABLE S3. List of associated loci included in the overconnectivity network analyses. Identified loci at P<10-6, in the ORIGIN cohort, and P<10-8, in the CARDIOGRAM consortium, were entered in the analyses. Attached excel file.
Supplementary TABLE S4. List of sub-networks identified in the overconnectivity analyses with their respective p-values.
Supplementary TABLE S5. Overconnectivity network result table. Nodes and relationships of entities comprising the networks used for the Bayesian statistics based network analyses.
Supplementary TABLE S6. Logistic regression model to identify standard CVO risk factors (age, sex, BMI, HbA1c, c-Peptide, HDL-C, LDL-C, TG, TC, SBP, DBP, smoking status, albuminuria or reported albuminuria) contributing to clusters separation in Caucasian and Latinos.
Having identified two SNPs associated with an individual having an increased risk in cardiovascular disease (CVD), i.e. Reference SNP cluster ID: rs73420079 (SEQ ID No: 3) and Reference SNP cluster ID: rs4314405 (SEQ ID No: 40), the inventors work enables the identification of individuals with an increased risk of CVD using oligonucleotide probes designed to detected the SNPs.
Oligonucleotide probes for detecting the presence of the SNPs can be produced and synthesized by any available oligonucleotide probe design tool, based on the SNP rs number. For example, probes of the SNPs can be sent to Illumina's® Illumina Assay Design Tool for scoring, based on the rs number format, to produce an assay ready probe.
A sample may be isolated from the patient, the sample can be a blood sample. The individual's nucleic acid is isolated from the sample. The isolation may occur by any means convenient to the practitioner. For instance, the isolation may occur by first lysing the cell using detergents, enzymatic digestion or physical disruption. The contaminating material is then removed from the nucleic acids by use of, for example, enzymatic digestion, organic solvent extraction, or chromatographic methods.
The individual's nucleic acid may be purified and/or concentrated by any means, including precipitation with alcohol, centrifugation and/or dialysis. The individual's nucleic acid is then assayed for presence or absence of one or more of the SNPs using the oligonucleotide probes that are capable of hybridizing to a nucleic acid sequence comprising one or more of the SNPs.
The detection of Reference SNP cluster ID: rs73420079 (SEQ ID No: 3) and/or Reference SNP cluster ID: rs4314405 (SEQ ID No: 40) in the sample indicates that the individual is at in increased risk of CVD.
| Number | Date | Country | Kind |
|---|---|---|---|
| 21315031.1 | Mar 2021 | EP | regional |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2022/055925 | 3/8/2022 | WO |
