EP1733050A2 - Procede d'identification de signatures dans des profils d'expression genetique complexes - Google Patents

Procede d'identification de signatures dans des profils d'expression genetique complexes

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Publication number
EP1733050A2
EP1733050A2 EP05716523A EP05716523A EP1733050A2 EP 1733050 A2 EP1733050 A2 EP 1733050A2 EP 05716523 A EP05716523 A EP 05716523A EP 05716523 A EP05716523 A EP 05716523A EP 1733050 A2 EP1733050 A2 EP 1733050A2
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European Patent Office
Prior art keywords
expression profile
gene
complex
protein
cell
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EP05716523A
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German (de)
English (en)
Inventor
Thomas Häupl
Joachim GRÜN
Andreas Radbruch
Gerd-Rüdiger Burmester
Christian Oligene GmbH KAPS
Andreas GRÜTZKAU
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Oligene GmbH
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Oligene GmbH
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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
    • C12Q1/6809—Methods for determination or identification of nucleic acids involving differential detection

Definitions

  • the present invention relates to a method for recognizing signatures in complex gene expression profiles, which comprises the steps of: a) providing a biological sample to be examined, b) providing at least one suitable expression profile, the at least one expression profile being one or more markers includes, which are typical only for the expression profile, c) determining the complex expression profile of the biological probe, d) determining the quantitative cellular composition of the biological sample by means of the expression profiles determined in steps b) and c), e) Calculation of a virtual signal that is expected on the basis of the determined composition of the expression profiles, f) calculation of the difference between the actually measured complex expression profile and the virtual signal, and g) determination of the quantitative composition of the complex expression profile on the basis of the determined old differences.
  • the present invention further relates to the use of the method according to the invention in the diagnosis, prognosis and / or tracking of a disease.
  • corresponding computer systems, computer programs, computer-readable data carrier media and laboratory robots or evaluation devices for molecular detection methods are disclosed.
  • genes at certain times in the cell's life cycle ultimately determines its phenotype.
  • the analysis of gene expression, in particular in the diagnosis and treatment, is of particular importance in the case of diseased and / or degenerated cells and ultimately tissues which are particularly complex, i.e. may have unknown mixtures of expression profiles of different cell types.
  • DNA array studies for example, measure the activity of genes via the expression of the mRNA.
  • Protein expression is also becoming increasingly available in high-throughput processes using appropriate array technologies or mass spectrometry.
  • Epigenetic-related analyzes raise profiles of the DNA methylation status of genes and allow conclusions to be drawn about the hiaactivation or the activatability of genes. These methods suggest far-reaching developments for molecular diagnostics. It is hoped that different molecular profiles can be associated with special clinical features, that molecular features can divide diseases into subgroups, and that interpretation options can be developed that provide prognostic information for therapy and disease progression.
  • pathomechanisms that enable targeted therapeutic influencing could be derived from the molecular profiles or their interpretation at the individual factor level.
  • the samples to be examined carry a wide variety of molecular information. Numerous genes can be associated with a change in expression both with a shift in the cellular composition of the sample (immigration of cells) and an activation of one or more metabolic pathways.
  • Haviv et al (Haviv I, Campbell IG. DNA microarrays for assessing ovarian cancer gene expression. Mol Cell Endocrinol. 2002 May 31; 191 (l): 121-6.) Describe the simultaneous expression analysis of genes within a given population using array technologies. Then the expression of normal and malignant cells can be compared and genes identified that are regulated differently. Vallat et al (Val- lat L, Magdelenat H, Merle-Beral H, Mas favour P, Potocki de Montalk G, Davi F, Kruhoffer M, Sabatier L, Omtoft TF, Delic J. The resistance of B-CLL cells to DNA damage -induced apoptosis defined by DNA microarrays. Blood.
  • BCLL B-cell chronic lymphoid leukemia
  • Vasseli et al (Vasselli JR, Shih JH, lyengar SR, Maranchie J, Riss J, Worrell R, Torres-Cabala C, Tabios R, Mariotti A, Stearman R, Merino M, Walther MM, Simon R, Klausner RD, Linehan W M. P redicting s urvival inp atients w ith m etastatic k idney c ancer by gene-expression profiling in the primary tumor. Proc Natl Acad S ci US A. 2003 Jun 1 0; 100 (12): 6958-63.
  • Favier et al (Favier J, Plouin PF, Corvol P, Gase JM. Angiogenesis and vascular architecture in pheochromocytomas: distinetive traits in malignant tumors. Am J Pathol. 2002 Oct; 161 (4): 1235-46.) Describe the investigation of gene expression profiles in the context of angiogenesis in tumors.
  • Pession et al (Pession A, Libri V, Sartini R, Conforti R, Magrini E, Bernardi L, Fronza R, Olivotto E, P rete A, T onelli R, P aolucci G. R eal-time R T-PCR o ft yrosine h ydroxylase to detect bone marrow involvement in advanced neuroblastoma. Oncol Rep. 2003 Mar-Apr; 10 (2): 357-62.) describe TH mRNA expression as a specific tumor marker and its analysis in different tissues.
  • Changes in the gene expression profile can be caused by shifts in the cellular composition of the sample (immigration of cells) as well as activations of one or more genes.
  • Changes in cellular composition occur with every inflammation and are therefore not specific to a particular disease.
  • activations of single or multiple genes can be typical or even specific for a certain disease process.
  • both changes, that of the cellular composition and that of the regulation of genes are reflected in the hybridization without current bioinformatic analysis methods being able to assign the two possible causes. The interpretation of the array data is therefore very limited.
  • this problem is of a general nature and also applies to profiles of protein expression and protein modification or epigenetic profiles (i.e. different methylation profiles of DNA from different cell types or complex samples).
  • the method is intended to enable the fast and high-throughput analysis of complex expression profiles without the need for special purification steps.
  • a method for the quantitative determination and qualitative characterization of a complex expression profile in a biological sample comprising the steps of a) providing a biological sample to be examined, b) providing at least one suitable expression profile, the at least one expression profile comprising one or more markers which are exclusively typical of the expression profile, c) determining the complex expression profile of the biological sample, and d ) Determining the quantitative cellular composition of the biological sample using the expression profiles determined in steps b) and c).
  • the method according to the invention for the quantitative determination and qualitative characterization of a complex expression profile in a biological sample comprises the further steps of e) calculating a virtual signal which is expected on the basis of the determined composition of the expression profiles, f) calculating the difference from the actual one measured complex expression profile and the virtual signal, and g) determining the quantitative composition of the complex expression profile based on the determined differences.
  • the present invention shows a method which contributes to the breakdown of complex information from array analyzes. According to the invention, this method is divided into several steps.
  • the following profiles are first required for the separation of the influences: a) an expression profile which represents, for example, the normal state, b) further defined or specific Expression profile files which e.g. characterize defined influences or states of a cell or cell population, and c) the investigating complex expression profile of the biological test, for example the disease state.
  • the typical “expression profiles” or “profiles” of defined influences and / or states are also referred to below as “signatures” or “fingerprints”.
  • signatures for the different cell types are required, for example for monocytes, for T cells, for granulocytes, etc.
  • a so-called “functional” and / or “characterizing” signature as is produced by a specific cytokine effect, can also be compared , represent a signature in the sense of the present invention.
  • Marker genes must be defined for each influence that is to be recognized and separated from other molecular information. These allow a quantitative assessment of the share of a signature in the overall profile.
  • marker genes for monocytes, T cells or granulocytes are identified. These reflect the proportion of the respective cell population in a mixed sample.
  • other measurement methods such as e.g. B. the differential blood count or a FACS analysis can be used.
  • a virtual signal can be calculated that is expected based on the composition.
  • the difference between the actually measured signal and the expected signal shows whether the differences can only be explained by the mixture of the different populations (influences) (no difference) or an activation (positive difference) or a suppression (negative difference) of the gene activity Has.
  • the profiles can be virtually broken down into sub-components.
  • RNA expression profile RNA expression profile
  • protein expression profile secretion profile
  • DNA methylation profile DNA methylation profile
  • metabolite profile a complex expression profile in a biological sample
  • a method according to the invention for the quantitative determination and qualitative characterization of a complex expression profile in a biological sample is further preferred, the determination of an expression profile using a molecular detection method, such as, for. B. a genarray, protein array, peptide array and / or PCR array or the creation of a differential blood count or a FACS analysis.
  • a molecular detection method such as, for. B. a genarray, protein array, peptide array and / or PCR array or the creation of a differential blood count or a FACS analysis.
  • the present invention is therefore not limited to nucleic acid arrays.
  • expression profiles from gel analyzes eg 2D
  • mass spectrometry eg., mass spectrometry and / or enzymatic digestion (nuclease or protease pattern) can also be used.
  • the expression profiles determined in step b) of the method above being selected from the group of expression profiles which characterize functional influences or states, such as e.g. Expression profiles that characterize the activity of certain messenger substances, signal transduction or gene regulation.
  • these can characterize the expression of certain molecular processes, e.g. of apoptosis, cell division, cell differentiation, tissue development, inflammation, infection, tumorigenesis, metastasis, vascular formation, invasion, destruction, regeneration, autoimmune reaction, immune compatibility, wound healing, allergy, poisoning and / or sepsis.
  • these can also characterize the expression of certain clinical conditions, e.g.
  • the choice of expression profiles depends on the origin of the biological sample to be examined, as well as its composition and / or expected composition. If necessary, the profiles must be defined and appropriately determined in the process for the measurement, or can be found in public expression databases.
  • a method according to the invention for the quantitative determination and qualitative characterization of a complex expression profile in a biological sample is still further preferred, the SLR value of a marker gene using the formula
  • Marker genes must be defined for each influence that is to be recognized and separated from other molecular hiformations. These allow a quantitative assessment of the share of a signature in the overall profile. For the detection of different cellular compositions, e.g. Marker genes for monocytes, T cells or granulocytes identified. These reflect the proportion of the respective cell population in a mixed sample.
  • a method according to the invention for the quantitative determination and qualitative characterization of a complex expression profile in a biological sample is preferred, the marker being selected from the markers given in Table 2 below.
  • these markers are only examples of the cell types specified there and can easily be determined accordingly for other tissues by means of the teaching disclosed here.
  • a method according to the invention for the quantitative determination and qualitative characterization of a complex expression profile in a biological sample comprising the exemplary qualitative and / or quantitative recognition of expression profiles of a T cell, monocyte and / or granulocyte expression profile.
  • Another aspect of the present invention relates to a method for the quantitative determination and qualitative characterization of a complex expression profile in a biological sample, wherein the determination of the quantitative composition of the complex expression profile based on the determined differences further comprises the identification of a previously unknown expression profile.
  • the comparison between two complex samples initially provides differential gene expression, which can be caused by differences in cellular composition as well as by gene regulation.
  • the first step is to break down the cellular composition. This is done using signatures that characterize different cell types. By using standard signatures for tissue and individual cell types, an expected profile is calculated that only takes normal gene expression into account. The difference between this virtual profile and the actually measured profile results in the genes that are either changed by further, not yet considered cell types or by regulation. Functional changes in gene expression can therefore be expected in this difference.
  • a further aspect of the present invention thus relates to a procedure for the quantitative determination and qualitative characterization of a complex expression profile in a biological sample, the determination of the quantitative composition of the complex expression profile based on the differences determined also the identification of molecular candidates for the diagnostic, prognostic and / or includes therapeutic use.
  • a still further aspect of the present invention relates to a molecular candidate or target structure for diagnostic, prognostic and / or therapeutic use, identified by means of the method according to the invention.
  • One mole is preferred Kular candidate for diagnostic, prognostic and / or therapeutic use, which has a sequence listed in one of Tables 5 to 8.
  • the molecular candidates of the invention can, for example, a) to characterize the inflammatory cell infiltration into an inflamed tissue with genes from Table 5 differentiating from gene activation by inflammation, b) to characterize the gene activation in an inflamed tissue with genes from Table 6 differentiating from the Cell infiltration, c) to characterize the gene activation or the inflammatory cell infiltration into an inflamed tissue via the calculated proportion of activation or infiltration of the genes in Table 7 and / or d) to characterize subgroups of inflammatory gene activation with genes from Tables 6, 7 and / or 8.
  • a further aspect of the present invention then relates to these candidates and / or target structures as “tools” for the diagnosis, molecular definition and therapy development of diseases, in particular chronic inflammatory joint diseases, and other inflammatory, infectious or tumorous diseases in humans.
  • the sequences of individual genes can thereby , a selection of genes or all genes which are mentioned in Tables 5 to 8 and their encoded proteins are used.
  • These tools according to the invention can also include gene sequences which are identical in sequence to the genes mentioned in Tables 5 to 8 or to their genes are encoded proteins or have at least 80% sequence identity in the protein-coding sections
  • corresponding (DNA or RNA or amino acid) sequence sections or partial sequences are included, which in their sequence have a sequence identity of at least 80% to the corresponding sections possess the genes mentioned.
  • the tools according to the invention can be used in many aspects of the prognosis, therapy and / or diagnosis of diseases.
  • Preferred uses are high-throughput methods in protein expression analysis (high-resolution, two-dimensional protein gel electrophoresis, MALDI techniques), high-throughput methods in protein spotting technology (protein arrays) for screening autoantibodies as a diagnostic tool for inflammatory diseases Joint diseases and other inflammatory, infectious or tumorous diseases in humans, high-throughput methods in the protein spotting technique (protein arrays) for the screening of autoreactive T cells as a diagnostic tool for inflammatory joint diseases and other inflammatory, infectious or tumorous diseases in humans, non-high-throughput errors in the protein spotting technique for the screening of autoreactive T cells as a diagnostic tool for inflammatory joint diseases and other inflammatory, infectious or tumorous diseases in humans or for the production of antibodies (also humanized or human), which are specific for the proteins or partial sequences of the tools mentioned, which are listed in Tables 5 to 8 or for analysis in animal experiments or for diagnosis in animals with
  • the tools according to the invention can be used to decide the therapy and / or to monitor the course / control the therapy of inflammatory joint diseases and / or other inflammatory, infectious or tumorous diseases in humans using the genes, DNA sequences or proteins or peptides derived therefrom and / or for development of therapy concepts that directly or indirectly influence the expression of the said genes or gene sequences, the expression of the said proteins or partial protein sequences or the direct or indirect influence of autoreactive T cells directed against the mentioned P roteins or P rotein partial sequences, b v be used, or to use the named genes and sequences and their regulatory mechanisms using the design and use of interpretation algorithms in order to identify or predict therapeutic concepts, effects, optimizations or disease prognoses.
  • the tools according to the invention can influence the biological action of the proteins derived from the gene sequences mentioned, the immediate molecular control loops into which the said genes and proteins derived therefrom are integrated, and for the development of biologically active drugs (biologicals) using genes , Gene sequences, regulation of genes or gene sequences, or using proteins, protein sequences, fusion proteins or using antibodies or autoreactive T cells as mentioned above.
  • Another aspect of the present invention relates to an array as a molecular tool, consisting of different antibodies or molecules with comparable protein-specific binding behavior, for the detection of all or a selection of the proteins derived from the genes of Tables 5 to 8 or all or a selection of these proteins serve.
  • This array can also be available as a kit, e.g. along with conventional ingredients and instructions for use.
  • Another aspect of the present invention finally relates to the use of a molecular candidate according to the invention for screening for pharmacologically active substances, in particular binding partners.
  • Appropriate methods are well known in the art including, among others, the following publications: Abagyan R, Totrov M. High-throughput docking for lead generation. Curr Opin Chem Biol. 2001 Aug; 5 (4): 375-82. Review. Bertrand M, Jackson P, Walther B. Rapid assessment of drug metabohsm in the drug discovery process. Eur J Pharm Be. 2000 Oct; ll Suppl 2: S61-72. Review. Panchagnula R, Thomas NS. Biopharmaceutics and pharmaeokinetics in drug research. Int J Pharm.
  • Another aspect of the present invention relates to a method for diagnosing, predicting and / or tracking a disease, comprising a method as mentioned above.
  • the corresponding linkage of the expression profile data with the diagnosis, prognosis and / or tracking of a disease is known to the person skilled in the art from and can be adapted accordingly to the respective conditions (see e.g. Simon R. Using DNA microarrays for diagnostic and prognostic prediction. Expert Rev Mol Diagn. 2003 Sep; 3 (5): 587-95. Review .; Franklin WA, Carbone DP. Molecular staging and pharmacogenomics. Clinical implications: from lab to patients and back. Lung Cancer. 2003 Aug; 41 Suppl 1: S 147-54. Review. Kalow W. Pharmacogenetics and personalized medicine. Fundam Clin Pharmacol. 2002 Oct ; 16 (5): 337-42. Review; Jain KK. Personalized medicine. Curr Opin Mol Ther. 2002 Dec; 4 (6): 548-58. Review.).
  • a computer system in the sense of the present invention can consist of one or more individual computers, which can be networked with one another centrally or decentrally.
  • a still further aspect of the present invention relates to a computer program comprising a programming code to carry out the steps of the process according to the invention which is carried out on a computer.
  • a still further aspect of the present invention finally relates to a computer-readable data carrier medium, comprising a computer program according to the invention in the form of a computer-readable program code.
  • a still further aspect of the present invention relates to a laboratory robot or evaluation device for molecular detection methods (for example a computerized CCD camera evaluation system), comprising a computer system according to the invention and / or a computer program according to the invention.
  • a laboratory robot or evaluation device for molecular detection methods for example a computerized CCD camera evaluation system
  • a computer system according to the invention and / or a computer program according to the invention.
  • Corresponding devices are well known to the person skilled in the art and can easily be adapted to the present invention.
  • Figure 1 a dilution experiment to estimate the concentration of unregulated marker genes
  • FIG. 2 the curve profile in limit areas at a low and high concentration of the marker
  • Figure 3 the various relationship sizes that are assumed for calculations
  • Figure 5 the hierarchical cluster analysis using the genes from Table 5
  • Figure 6 the hierarchical cluster analysis using the information from the
  • Figure 7 A) Hierarchical cluster analysis using the genes from Table 6.
  • Figure 8 the hierarchical cluster analysis with the genes of Table 7
  • FIG. 9 A) the hierarchical cluster analysis with the genes of Table 8. B) the illustration of the differences by means of PCA of the experiments which result from the use of the genes from Table 8.
  • a cell type (influence to be measured) can be completely missing in the control sample. It is only in the changed (diseased) state that different cells (or influences) relevant to the disease are found in the sample.
  • a mixture of different cell types (or influences) can already exist in the normal situation.
  • the blood is composed of different cells, which are also subject to variations in the normal state. These variations can be very pronounced in diseases. They are not disease-specific, but may possibly disguise the gene regulations that are typical of a disease.
  • Different cell types can be distinguished by cell surface markers. Comparable, one can also expect different characteristics from gene expression analyzes, which are characteristic of individual cell types and allow a quantitative assessment.
  • Gene expression profiles of tissues and purified cells were compared. Genes are selected that are only present in one cell population or one tissue, but not in the others. These are candidates for estimating the proportion of this population in a sample with mixed cell types.
  • the cell populations and tissues given in Table 1 were compared.
  • the selection criteria for the first stage of gene selection were that
  • the genes shown in Table 2 were identified. These genes are not suitable for all samples. For example, some of these genes can no longer be detected at low cell concentrations and then lead to a quantitative underestimation of the influence. Therefore, further restriction criteria are necessary, which have to be adapted to the complex samples to be examined. •
  • the marker genes must provide sufficient signals and differences in the complex sample to be examined if an infiltration / part of the overall profile has been proven (eg by determining the differential blood count). • In comparison to the control, there must be no regulation of these genes in the sample to be examined. • The genes must not be artificially induced or suppressed in the signature profile compared to the sample examined.
  • the strength of the hybridization and thus the increase in the signal will follow an individual dynamic with the increase in the concentration for each sequence. This is determined by the sequence of the probe, but also by the hybridization conditions, the duration of the hybridization and the stringency conditions of the subsequent washing steps.
  • Figure 3 illustrates the various relationship sizes that are assumed for calculations.
  • Equations 1 to 3 and the considerations for FIG. 2 result in the following unknown quantities that are required for the calculation:
  • the lower detection limit S min is chosen as the fixed point for determining the straight line in the coordinate system.
  • the detection limit can theoretically be determined by dilution experiments for each gene.
  • a faulty hybridization with sequences that do not completely match can be measured for the assessment.
  • Affymetrix technology uses this perfect match / mismatch technology and uses this to calculate a probability of whether the measured signal of a gene is present or absent ("present" or "absent").
  • a limit of detection can only be estimated for genes that are only classified as "present”.
  • the median or mean of all detection limits defined for the third group can serve as a measure.
  • the signal level S min as the limit of the transition from "absent” to "present” was also determined individually from the 123 measurements for each gene. First, the lowest “present” signals and highest “absent” signals were determined. The median of all values lying between these limits was defined as the limit S min . If there was no overlap, the highest “absent” value was set as S min . For all genes that had no “absent” determinations, the median of all S m / ⁇ limit values was set as a uniform S min (68.6). Alternatively, another form of estimation such as the mean or a weighted mean could serve.
  • the estimation of the dynamic range can be estimated from the measured signal values of a large number of different experiments with different samples as follows:
  • S j can be defined as the largest measured value in a series of experiments regardless of the gene as the upper limit of the measurement spectrum.
  • S 0 can be defined as the lowest reliably measured value of this series of experiments regardless of the gene.
  • the value from equation 4 was determined in the example shown here as a theoretical measure for the maximum dynamic range of the signals. The exact sizes for both scales are not decisive for the desired relative calculations.
  • the signal units are arbitrarily defined for each array platform.
  • the concentration units can also be determined arbitrarily.
  • the relative relationships between the signals and concentrations as well as the determination of the detection limit are decisive.
  • the same relationship must apply to a gene for all different cell types and samples in order to carry out calculations between the different samples and signatures. Applying similar proportions to the relationship between concentration and signal With all different genes, the proportion of a signature can be transferred from one gene to another.
  • the convention here is that an order of magnitude comparable to the signal range is assigned to the concentration range.
  • the extreme states M 1 and M 2 shown in FIG. 4 result for the relationship between signal and concentration. They show the two limit areas of how the relationship between concentration and signal can flow into the model depending on the detection limit.
  • Mo shows the course under optimal conditions. In this ideal case there will be a linear relationship to the minimum concentration K minI even with very low signals S minI .
  • the analysis of hybridization provides a relatively high entry signal S minG , via which the presence of a gene is only reliably displayed and from which a linear relationship can be assumed.
  • Model M 1 assumes that background activity does not significantly affect the detection limit K minI of a gene. Only the detection range of the signal is reduced and thus the dynamics of the signal increase are reduced.
  • Model M 2 assumes that low concentrations remain hidden due to the high background and that a gene can only be detected from a higher concentration K minM2 .
  • FIG. 4 illustrates the effects on the concentration determinations K ProbeM1 or K ProbeM2 depending on the choice of the model M t or M 2 .
  • the signal value S min is calculated individually for each gene and assigned a minimum concentration K min .
  • K min ⁇ K must apply.
  • K min 1 has been assigned here.
  • the highest measured signal value S is assigned to K 1 .
  • a concentration of K 1 - 2 14 ' 7 that was comparable to the signal measurement range was assigned.
  • the slope of the straight line results individually from Equation 1 for each gene as follows:
  • K mjnI 1 and thus K minM2 is significantly larger than K mM .
  • RNA concentration for a marker gene in a measured sample can be derived from equations 7 and 8 as follows:
  • a marker gene for a certain cell type was defined in such a way that it cannot be found in other cell or tissue types, or is found to be negligible. This results in the following calculation:
  • Affymetrix technology occupies a special position. This platform uses several different oligonucleotides per gene and associated "mis-match" oligonucleotides. Signals can also be generated here for immediate further calculation den (e.g. via the robust multiarray analysis; RMA). Both signal determination and comparisons can also be carried out using special algorithms that include both perfect match and mis-match information. The results of the comparison calculation are also given as a signal log ratio (SJR) and can be integrated into the calculations performed here. A reference population can also be used as a norm in this way. This is illustrated in Figure 3. This reference is called control. For the example of synovial tissue analysis, this is normal tissue (see also Table 1). The following relationships result for the calculation of the infiltration:
  • Equation 14 can be applied to several genes that are suitable for estimating the proportions of a cell type in a cell mixture (see Tables 2 and 3). The one with- The value from the proportions calculated for each gene provides a measure of the proportion of the cell type in the sample to be examined.
  • an expected mixed profile can be calculated from the profiles for each cell type.
  • the initial situation for the synovial tissue is that the normal tissue contains no immune cells. This corresponds to the control tissue mentioned above. Infiltration in the event of disease can be calculated using the marker genes of various cell populations as shown above (equations 11 and 14, respectively). The proportions of the respective cell types and normal tissue add up to 100%.
  • concentration K Zelljyp can be determined using equation 12 for each expressed in a cell type gene.
  • Synovial tissue is determined via signal S control of the gene in question according to equation 8.
  • CLR Concentration Log Ratio
  • the calculations are carried out directly with the determined and coordinated signals.
  • a reference can be made to a control tissue that does not contain the various cell types, such as the normal synovial tissue.
  • the concentration K control is thus calculated from equations 10 and 13.
  • the proportions of the individual cell types are estimated according to equation 11 from the concentrations of the marker genes or the SLRs according to equation 14.
  • the proportion of residual populations that are not available as individual profiles is missing. This can be combined into a separate virtual "residual population". The share results as follows: n
  • the proportion of the remaining population can be negligible and the calculated expected concentration from the signatures and their proportions can exceed the actually measured value, i.e. n
  • K rest 0.5
  • the calculated concentrations K rest of the residual population are averaged from all normal donors. This creates a virtual signature for the remaining population of the normal donor, comparable to the measured signatures of the different cell types. This provides all the prerequisites for calculating the normal situation based on the existing cell signatures and a virtual normal residual profile.
  • the expected signals are calculated from equation 16.
  • the regulated genes which cannot be traced back to the known signatures, result either from the SLRs according to equation 17 or the CLRs according to equation 18.
  • the breakdown into individual components is carried out step by step.
  • the comparison between two complex samples initially provides differential gene expression, which can be caused by differences in cellular composition as well as by gene regulation.
  • the first step is to break down the cellular composition. This is done using signatures that characterize different cell types. By using standard signatures for tissue and individual cell types, an expected profile is calculated that only takes normal gene expression into account. The difference between this virtual profile and the actually measured profile results in the genes that are either changed by further, not yet considered cell types or by regulation. Functional changes in gene expression can therefore be expected in this difference. An assignment to a specific cell type is initially not possible. However, these genes result from the functional change in the cells involved.
  • the above method was applied to the analysis of a total of 10 different samples from patients with rheumatoid arthritis (RA), 10 patients with osteoarthritis (OA) and 10 normal synovial tissues.
  • the genes marked with 1 in Table 2 under selection were used to estimate the proportions of CD4 + T cells, monocytes and granulocytes in the synovial tissue from the RA and OA patients.
  • the proportional distribution for RA and OA given in Table 4 was obtained.
  • the gene entries found under the 1st condition are shown in Table 5 below. They represent a gene pool that can be used in chronic inflammatory joint disease such as rheumatoid arthritis as a diagnostic for the extent of infiltration, in particular of T cells, monocytes or granulocytes. These genes alone can already be criteria for the diagnosis of inflammatory joint diseases. For osteoarthritis there was a comparatively significantly lower infiltration (Figure 5, hierarchical cluster analysis with the genes of Table 5 between RA, OA and normal tissue). Also for a division into subgroups of different RAP patients there are infiltration differences, which can be identified both from this selection of genes and from the comparison of the infiltration components using the marker genes (FIG. 6). The signals of these genes can be used for the diagnostic examinations without prior calculation, since they are mainly caused by infiltration.
  • the gene entries found under the second condition are shown in Table 6 below. They represent a gene pool that can be used as a diagnostic agent for the characteristic type of gene regulation. Here differences between individual RA patients can be identified and subdivisions made possible. This includes classifications according to the type of arthritis, stage of the disease, disease prediction, allocation to an optimal form of therapy, assessment or monitoring of the response rate to a specific therapy. This results in new markers or marker groups which, as molecular features, can be correlated with various clinical features or expected feature developments and therefore have diagnostic importance. These signals could also be used diagnostically directly without prior calculation of the infiltration or activation, since they have arisen primarily through activation. Nevertheless, the calculation of the part of the signal generated by the gene activation can also improve the interpretation.
  • FIG. 7 A subdivision into subgroups is shown in FIG. 7.
  • the gene entries identified under the third condition are shown in Table 7. They also represent a diagnostically important gene pool, which, however, must first be converted into signals that differentiate the regulation or Reflect infiltration component (solution of equation 16 according to S 'p wbe ) -
  • the regulation-related signal component for the genes which are summarized by the 2nd or 3rd condition, was determined.
  • the proportion due to infiltration could also be investigated in an analogous manner.
  • a hierarchical cluster analysis was carried out. The result is shown in FIG. 8.
  • a t-test analysis was carried out on the calculated signals from all genes from conditions 2 and 3 applied. This led to the gene entries shown in Table 8, which allow a differentiation.
  • FIG. 9 shows the cluster analysis and associated principle component analysis.
  • the genes were expressed in all examined monocyte populations at least 4-fold higher compared to other cell types or non-infiltrated tissues.
  • CAPG Hs.82422 capping protein (actin filament), gelsolin-like 0 126.8 202295_s_ CTSH Hs.114931 cathepsin H 0 76.3 at
  • cytochrome b -245 beta polypeptide (chronic - S - CYBB Hs.88974 58.6 at granulomatous disease) HLA- major histocompatibility complex, class II, "
  • solute carrier family 7 cationic amino acid "- S - SLC7A7 Hs. 194693 193.1 at transporter, y + system), member 7
  • CD86 antigen CD28 antigen 1 igand 2, B 7-2 Q - CD86 Hs.27954 112.6 at antigen
  • CD36 antigen (collagen type I receptor, 1 S - CD36 Hs. 443120 116.85 at thrombospondin receptor)
  • tumor necrosis factor (ligand) superfamily X -TNFSF13 Hs.54673 54.9 at member 13
  • CD86 antigen CD28 antigen ligand 2, B 7-20 170,35 at antigen
  • the genes were expressed in all examined T-cell populations at least 8-fold higher compared to other cell types or non-infiltrated tissues.
  • amyloid beta (A4) precursor protein-binding «APBA2 Hs. 26468 26 at family A, member 2 (XI 1-like)
  • CD6 Hs.436949 CD6 antigen 0 149.4 zeta-chain (TCR) associated protein kinase n
  • the genes were expressed in all examined neutrophil granulocyte population populations at least 8-fold higher compared to other cell types or non-infiltrated tissues.
  • matrix metalloproteinase 9 (gelatinase B, MMP9 Hs.151738 0 68.6 at 92kDa gelatinase, 92kDa type IV collagenase)
  • 211574 s membrane cofactor protein (CD46, ⁇ MCP Hs.83532 0 192.3 at trophoblast-lymphocyte cross-reactive antigen) coagulation factor II (thrombin) receptor-like 213506_at F2RL1 Hs.154299 0 56.2
  • Affymetrix_ID gene symbol Unigen Name integrin, beta 2 (antigen CD 18 (p95), lym- 202803_s_at ITGB2 Hs.375957 phocyte function-associated antigen 1; macrophage antigen 1 (mac-1) beta subunit) serine (or cysteine) proteinase inhibitor,
  • CD48 antigen B-cell membrane protein
  • GMFG Hs.5210 glia maturation factor, gamma selectin L (lymphocyte adhesion molecule
  • CDW52 antigen 204661_at CDW52 Hs.276770 CDW52 antigen (CAMPATH-1 antigen)
  • 210116_at SH2D1A Hs.151544 (lymphoproliferative syndrome) 210140 at CST7 Hs.143212 cystatin F (leukocystatin) leukocyte immunoglobulin-like receptor,
  • TCRA 211902_x_at - Hs.74647
  • L DLR class
  • CDW52 antigen CDW52 antigen (CAMPATH-1 antigen) 35974 at LRMP Hs.124922 lymphoid-restricted membrane protein
  • genes marked with 1 in the last column represent, in addition to selected representatives, further multiple determinations of immunoglobulin sequences and were therefore not used for the statistical calculations and cluster analysis in the associated figures.
  • Affymetrix_ID Gen Symbol Unigene Name signal transducer and activator of transcrip- 200887 s at STAT1 Hs.21486 tion 1, 91kDa major histocompatibility complex, class II,
  • TNFSF11 Hs.333791 member 11 Fc fragment of IgG, low affinity Ilb, recep ⁇
  • V4-4 216491_x_at - Hs.288711 ble region (V4-4) gene, partial cds Homo sapiens IgH VH gene for immuno-
  • CD14 Hs.75627 CD 14 antigen capping protein (actin filament), gelsolin-
  • CD79A antigen immunoglobulin
  • solute carrier family 16 monocarboxylic
  • Transcriptome the complete set of RNA transcripts that were read from the genome at a given time
  • Proteome The complete set of proteins that was produced and modified after transcription
  • Gene expression signature Profiles induced by a defined condition or associated with a condition e.g. the profile of a certain cell type in the normal state; or the profile induced by a cytokine in a tissue or cell type
  • Marker gene gene that is characteristic of a signature and on the basis of whose strength of expression the proportion of the signature in a complex sample can be determined.
  • molecular profile a pattern of signal strengths from different representatives of a molecular substance class in a given sample.
  • K cell type RNA belonging to the signal S cell type Concentration of a gene A cell type Proportion of a defined cell population in a complex sample from different cell types
  • KRest Concentration of a gene in the rest of the population in the normal state KF correction factor for adapting the signature concentrations to a complex control
  • Ki reg change in the concentration of a gene that arises from regulation in comparison to the normal state

Abstract

L'invention concerne un procédé d'identification de signatures dans des profils d'expression génétique complexes, consistant a) à se munir d'un échantillon biologique à examiner, b) à se munir d'au moins un profil d'expression adapté comportant un ou plusieurs marqueurs, typiques du profil d'expression, c) à déterminer le profil d'expression complexe de l'échantillon biologique, et d) à déterminer la composition cellulaire quantitative de l'échantillon biologique par l'intermédiaire des profils d'expression déterminés aux étapes b) et c). Le procédé selon l'invention peut également consister e) à calculer un signal virtuel prévu sur la base de la composition déterminée des profils d'expression, f) à calculer la différence entre le profil d'expression complexe mesuré réel et le signal virtuel, et g) à déterminer la composition quantitative du profil d'expression complexe sur la base des différences déterminées. L'invention concerne également l'utilisation dudit procédé dans le diagnostic, le pronostic et/ou le suivi d'une maladie. L'invention concerne par ailleurs des systèmes informatiques, des programmes informatiques, des supports de données lisibles par ordinateur, et des robots de laboratoires ou des appareils d'évaluation correspondants, destinés à des procédés d'identification moléculaire.
EP05716523A 2004-04-04 2005-04-04 Procede d'identification de signatures dans des profils d'expression genetique complexes Withdrawn EP1733050A2 (fr)

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