EP1114187A1 - Classification geometrique et hierarchique fondee sur l'expression genetique - Google Patents

Classification geometrique et hierarchique fondee sur l'expression genetique

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Publication number
EP1114187A1
EP1114187A1 EP99969123A EP99969123A EP1114187A1 EP 1114187 A1 EP1114187 A1 EP 1114187A1 EP 99969123 A EP99969123 A EP 99969123A EP 99969123 A EP99969123 A EP 99969123A EP 1114187 A1 EP1114187 A1 EP 1114187A1
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Prior art keywords
cells
subsequence
class
subsequences
pair
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EP99969123A
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German (de)
English (en)
Inventor
Joel S. Bader
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CuraGen Corp
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CuraGen Corp
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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
    • C12Q1/6844—Nucleic acid amplification reactions
    • C12Q1/6853—Nucleic acid amplification reactions using modified primers or templates
    • C12Q1/6855—Ligating adaptors
    • 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

  • This invention relates to representations of the extent of relatedness between cells, cell lines, tissues, organs, or expressed sequences based on a genomic analysis of gene expression using software algorithm based analysis.
  • Colon adenocarcinoma from 40 tumor samples were compared with 22 normal colon tissue samples using Affymetrix DNA chips to which sequences from human cDNAs were bound (Alon et al, Proc. Natl. Acad. Sci. USA 96:6745-6750 (June 1999)).
  • 3,200 full-length human cDNAs and 3,400 ESTs are represented in sets of 25-bp fragments, as well as such sequences containing a single base mismatch in the center of the sequence.
  • the gene expression in both the tumor tissue samples and the normal colon samples was assessed by hybridization. The statistical significance of the correlation between genes was assessed by calculating pairwise correlation coefficients.
  • the clustering of the expressed genes was evaluated using an algorithm based on deterministic-annealing (Rose et al, Phys. Rev. Lett. 65:945-948 (1990); Rose, Proc. IEEE 96: 2210-2239 (1998)) to organize the data in a binary tree. Data are presented as a large two-dimensional color coded array, with genes displayed along one dimension and tissue samples along the other; artificial color values are assigned at each array point to indicate the extent of expression in a third dimension. Clustering analysis reveals patterns in the color distribution within the array which is disrupted when various randomization procedures are applied. The clustering of the genes in the data set reveals groups of genes whose expression is correlated across tissue types. The algorithm separated the tissues into distinct clusters.
  • the invention provides novel methods of geometric and hierarchical classification between at least two classes of data sets.
  • Data sets may represent cells, nucleic acid sequences, polypeptide sequences, or the like.
  • the invention is able to utilize both standard DNA microchip arrays and non-DNA chip technology to provide input information on nucleic acid moieties of the specified classes of cells.
  • the data are then treated in various ways to provide representations of relatedness that are readily interpretable by the human eye.
  • the invention additionally provides novel methods for generating a representation of the correlation between at least two classes of cells, the correlation reflecting any changes in the composition and amount of nucleic acids present between the classes.
  • the cell classes may be from different sources for use in comparing differences between various cell populations. These differences include, but are not limited to, species differences, tissue differences, disease state differences, and drug treatment differences.
  • Computer algorithms analyze input data reflecting differences between chosen cell classes and represent them in a meaningful way. Prior to the present invention, input information was obtained only using DNA-chip technology to analyze the nucleic acids of the cell classes to be compared. Drawbacks to these methods are that identifier sequences need to be already known and isolated, chip technology has size limitations related to the number of the nucleic acids immobilized on the chips, and, once the chips were manufactured, it is virtually impossible to expand nucleic acid parameters.
  • the invention provides the use of
  • GeneCallingTM a non-DNA chip technology, to assay differences between input cell classes.
  • An unexpected result is that GeneCallingTM is able to provide sensitive comparisons between disparate groups above, thereby sidestepping the limitations inherent in the use of DNA chip technology when assaying input nucleic acid population.
  • the invention provides a novel method for generating the extent of relatedness reflecting similarities or differences in the presence and quantitation of the fragments among the classes by calculating a distance that reflects the amplitude of a difference vector.
  • the extent of relatedness is provided by generating a tree structure reflecting the relatedness between any two classes. The branches of the tree structure reflect the difference vectors and are ramified from nodes.
  • the invention also provides a novel method for generating a representation of the correlation between classes of data sets.
  • the correlation is related to a set of orthonormal eigenvectors.
  • the representation is a cluster diagram or a dendrogram, and includes a tree structure reflecting the relatedness of the pathways involved in the biochemical or physiological response to a difference between cells of the two classes.
  • the invention additionally relates to providing geometrical representations of differences between classes of data sets.
  • the geometrical representations encompass, by way of nonlimiting example, principal component analysis and principal factor analysis, as well as reduced dimensional representations derived from them.
  • the geometrical representations are based on differences determined between classes of cells using any method of analyzing for the presence of genes, nucleic acids, or fragments thereof, including nucleic acid microchip arrays and differential display of expressed genes or nucleic acid fragments.
  • the invention also provides display means for displaying the representation of the extent of relatedness, the correlation, and the geometrical representations of differences between classes of data sets, as well as the representations themselves.
  • Figure 1 is a schematic flow diagram illustrating the principal steps involved in generating the various representations of the invention starting from a set of subsequence-selected fragments found for the samples.
  • Figure 2 is a schematic flow diagram illustrating the primary steps involved in carrying out a principal component analysis.
  • Figure 3 illustrates hierarchical clustering of four drugs with sterile water as an outgroup.
  • Figure 4 is a graphical projection of drug treatments and controls onto principal factors.
  • the present invention relates to methods for preparing representations of the relatedness between cells of any two or more different classes of cells.
  • the classes broadly encompass cells arising in animal and plant organisms, the cells further being normal cells or cells in a diseased state, including tumor cells. They further include cells that have been treated with a putative pharmaceutical agent.
  • the representations are obtained using experimental data that provide size and sequence information on nucleic acid fragments derived from each of the cellular sources. The fragments may be prepared from the nucleic acid content of the cells in each class in any of several ways.
  • the present invention also relates to methods for preparing representations of the relatedness in terms of co-expression between the nucleic acid fragments so produced.
  • the invention further relates to the representations provided by these methods, and to display means on which such representations are displayed.
  • the methods for preparing the fragments such as the use of restriction endonucleases or the application of amplification primers, are chosen to provide subsequence information relating to the ends of the resulting fragments, while size determination provides the length of the fragment.
  • the size and subsequence results can optionally be scanned against databases providing known nucleic acid sequences in order to provide the identity of one or more candidate fragments of known complete nucleic acid sequences having the correct length and terminal subsequences (U. S. Patent No. 5,871,697; Shimkets et al. 1999 Nature Biotechnology 17:798-803).
  • the invention additionally relates to providing geometrical representations of differences between classes of cells.
  • the geometrical representations encompass, by way of nonlimiting example, principal component analysis and principal factor analysis, as well as reduced dimensional representations derived from them.
  • the geometrical representations are based on differences determined between classes of cells using any method of analyzing for the presence of genes, nucleic acids, or fragments thereof, including nucleic acid microchip arrays and differential display of expressed genes or nucleic acid fragments.
  • sample relates to a particular experimental state for which all the variables being studied in a project are held fixed.
  • a variable refers to a particular cell type; if a variable is the subsequence pairs employed in the project, a “sample” refers to a particular subsequence pair; or if a variable is a set of putative pharmaceutical agents, a “sample” refers to a particular agent from the set.
  • “representation” relates to any graphical, visual, or equivalent non-verbal display that provides an image of the results obtained according to the methods of the present invention. More specifically, a “representation” of the invention is obtained by transforming the quantitative results gathered by experiments underlying the invention. Examples of such data include, by way of non-limiting example, differential gene expression across classes of cell, and/or across a set of putative therapeutic agents, and/or equivalent types of experimental parameter.
  • a representation of the invention is generated by algorithms executed in a computer and is suitable for display on a display means, such as a display screen or monitor, employed in the operation of the computer.
  • the representation is also suitable for storing in a storage module or data archive of such a computer. It is still further suitable for printing from the computer onto a medium such as paper or equivalent physical medium, and for recording it onto a portable storage medium, including, for example, magnetic media, CD ROMs and equivalent storage media.
  • display means includes any of the objects and media identified above in this paragraph, as well as equivalent apparatuses and objects suitable for displaying the results of computational processes for visual inspection.
  • extent of relatedness is a characterization according to methods of the present invention of a degree of similarity or a degree of non-similarity between any two members of the same type of element; in particularly important embodiments, the type of element may be classes of cells.
  • a "putative pharmaceutical agent” relates to a chemical compound or a composition comprising at least one chemical compound which is a candidate for being a therapeutic agent. Any such therapeutic agent may be used in treating a mammal suffering from a disease or a pathology. In treating the mammal with the therapeutic agent it is intended to attenuate the symptoms and/or the underlying causes of the disease or the pathology, to ameliorate the symptoms and/or the underlying causes, and/or to contribute to a cure of the disease or the pathology.
  • Non-limiting examples of a putative pharmaceutical agent include an agent drawn from a chemical compound library; an isolate from a natural source; a compound synthesized specifically as a putative agent; or a substance derived or obtained using the practices of genetic engineering and recombinant nucleic acid technology such as a recombinant protein, a fragment of a recombinant protein, a recombinant polypeptide, a fragment of a recombinant polypeptide, a recombinant peptide, or a nucleic acid including, for example an oligonucleotide intended as an antisense agent, and a recombinant gene intended for administration as a gene therapeutic agent.
  • a "fragment" of a nucleic acid relates to a contiguous portion originating from the genomic or cDNA-derived nucleic acid from a class of cells.
  • the contiguous portion includes at or near each end a target subsequence defined according to the operational procedures disclosed herein, and includes all nucleotides in the sequence of the fragment bounded by the two target subsequences.
  • the target subsequences are identified, for example, by contacting the nucleic acid from the cells with a specific pair of restriction endonucleases, or with a specific pair of oligonucleotide primers, and in equivalent ways.
  • the information used in the present invention is obtained from experiments providing the results of differential gene expression wherein the difference relates to an experimental state and a reference state.
  • a reference state refers to a normal, or an unperturbed, or a non-pathological class of cells.
  • An experimental state may relate to a certain set of conditions applied to one class of cells, and the corresponding reference state then relates to the same set of conditions applied to a second class of cells.
  • An experimental state may also relate to a class of cells in the presence of one or more putative therapeutic agents, in which case the reference state relates to the same class of cells in the absence of any putative therapeutic agent.
  • An experimental state may furthermore be obtained from a class of cells that is of interest in a particular set of circumstances.
  • Types of cell encompassed within the present invention include, by way of non-limiting example, endothelial cells, mesothelial cells, and epithelial cells.
  • Tissues and organs included within the present invention may be, by way of non-limiting example, lung, heart, skeletal muscle, smooth muscle, brain, central nervous system, peripheral nervous system, stomach, liver, kidney, reproductive tissues and organs, skin, and bone.
  • Cancerous cells include, by way of non-limiting example, cells from prostate cancer, breast cancer, colon cancer, lung cancer, lymphatic or hematopoietic cancers, and also include cells obtained from tissue biopsies or from cell lines in the National Cancer Institute human tumor cell line panel.
  • the cells subjected to analysis in the present invention may also originate from plants, yeast, fungi, and other taxonomic groupings.
  • the methods of evaluating the extent of relatedness between classes of cells for example, between a first class of cells and a second class of cells, are founded on evaluating the extent of relatedness of the expression of particular genes between the cells of the two classes.
  • similarities and differences in the susceptibility of the nucleic acid present in the cells to digestion by specific pairs of restriction endonucleases are determined, according to the methods of the present invention, by procedures that are disclosed in detail in co-owned U. S. Patent No. 5,871,697 to Rothberg et al, and in Shimkets et al. 1999 (Nature Biotechnology 17:798-803), both of which are incorporated herein by reference in their entirety.
  • the nucleic acid content of the cells is subjected to restriction endonuclease ("RE") digestion by specific pairs of endonucleases.
  • RE restriction endonuclease
  • Each member of the RE pair is chosen to optimize the likelihood that a restriction fragment resulting from the nuclease digestion will be a unique fragment.
  • the restriction nuclease digestion is carried out on cDNA prepared from the cells of the class in the given experimental state. This implementation leads to emphasis on genes that are expressed in the experimental state, many of which may be characteristic of the given experimental state and be more poorly expressed, or not expressed at all significantly, in a different experimental state.
  • a large number of specific pairs of nucleases may be employed.
  • expression of a gene may be repressed in a characteristic way in a given experimental state and be expressed at a higher level, such as at a constitutive level, in a different experimental state.
  • several pairs of restriction nucleases that may be employed in implementing the present invention are disclosed in U. S. Patent No. 5,871 ,697.
  • the extent of relatedness may be obtained by amplification fragment length polymo ⁇ hism analysis ("AFLP").
  • AFLP amplification fragment length polymo ⁇ hism analysis
  • amplification of the nucleic acid content of the class of cells being examined is subjected to a primer-dependent amplification procedure in which any of a set of primer pairs is used to initiate amplification.
  • Amplification procedures are described in considerable detail in, for example, Innis et al, PCR PROTOCOLS, A GUIDE TO METHODS AND APPLICATIONS, Academic Press, New York (1989), and Innis et al, PCR STRATEGIES, Academic Press, New York (1995).
  • the primers of each primer pair are different from each other, and reflect different subsequences that are the object of the amplification process.
  • Amplification may proceed by any procedure, including polymerase chain reaction, known in the field of molecular biology.
  • AFLP polymerase chain reaction
  • the length of an amplicon found in a given experimental state differs from the length found in a different experimental state. This may arise, for example, if the given experimental state arises from a mutation that occurs in a subsequence recognized by a primer used in the amplification reaction. It may also arise from a deletion from, or an insertion into, the nucleic acid of the cells in that state.
  • the gene expression levels are determined experimentally. This can be done, in a preferred embodiment, by following the general protocols of differential expression using restriction endonucleases (U.S. Patent No. 5,871 ,697). For each pair of restriction enzymes and each biological sample, a pool of fluorescently-labeled DNA fragments is generated. Electrophoresis is then performed to separate these fragments based on size, and an intensity, designated as I srt (x), where s labels the sample, i.e., the cell class; r labels the restriction enzyme pair, i.e., the gene fragment; t labels the trial, and x is the length of the fragment as determined by electrophoresis, is detected.
  • I srt x
  • the length x may be either a continuous index or a convenient discretization.
  • the resolution of the electropherogram may be set to a discretization of 0.1 nucleotide ("nt"). Commonly three independent trials are performed. A mean signal I sr (x) is then obtained by averaging over the n t trials,
  • I sr (x) (l/n t ) ⁇ , I s ⁇ (x) ⁇ , (1 )
  • Step 3 is repeated for succeedingly smaller values of the intensity difference. If the length x that marks the current largest difference is within a distance w from the length of a previously identified difference, the current difference is skipped and the next smaller difference is considered.
  • Step 4 is repeated until there are no more differences to consider.
  • Another method involves finding differences that meet a statistical criterion.
  • a particular example of such a method involves the computational steps of: 1. defining a set of sample classes and assigning each sample to a particular class c;
  • a distance D ss may be defined as the distance in vector space between pairs of samples s and s'.
  • a variety of methods for calculating D ss are available. Some examples, which are intended as being nonlimiting, are provided below.
  • D ss . [ ⁇ d (I sd - I s d ) ] .
  • D ss . [ ⁇ d (I sd - l s . d ) 2 1 ⁇ d 2 ] 05 where ⁇ d is defined in step 1 of scaled correlation function above.
  • D ss . as a pairwise Pearson distance:
  • Hierarchical Clustering The distances can be used to perform hierarchical clustering of the samples. A general algorithm for clustering is described below.
  • Each sample s is assigned its own initial cluster c.
  • Step 2 is repeated until only a single cluster remains.
  • Unweighted pair group method using arithmetic averages also known as average linkage:
  • the distance between clusters c and c' is ( ⁇ ss . D ss ) / (n c n c ) where s ranges over all samples in WO 00/15851 . 13 - PCT/US99/21525 __
  • cluster c s' ranges over all samples in cluster c'
  • n c is the number of samples in cluster c
  • n c is the number of samples in cluster c'.
  • sample s is represented as the point (g s hear g s2 , ..., g sk ). Samples that are close in the k- dimensional space have similar expression profiles and may be considered to be related.
  • C is the correlation matrix
  • H is the centering matrix with diagonal elements given by 1 - (1/n) and off-diagonal elements - (1/n), where n is the number of items being correlated.
  • the k* principal component is then the k th eigenvector of B normalized to unit length and ordered by decreasing eigenvalue ⁇ k , and the k th principal factor is obtained by scaling the eigenvector by ⁇ k l/2 .
  • the projection of sample s onto the k' h principal factor is the element of the factor for row s.
  • the components or factors are ordered from 1 (corresponding to the most informative) to n (corresponding to the least informative).
  • the samples can be represented in a small-dimensional geometric space.
  • the amount of information retained in the representation can be related to the eigenvalues of the components that are used (See Mardia, Kent, and Bibby).
  • a centered inner product matrix B appropriate for principal component or prinicpal factor analysis can also be obtained from any distance matrix D ss .
  • N, -l/2 (D s ,) 2 . (4)
  • the first k factor loadings and an orthonormal rotation matrix G are selected.
  • the j* coordinate of sample s in the rotated space is ⁇ , h SJ .
  • the rotation matrix G may be optimized according to standard criteria. See, for example, Mardia, Kent, and Bibby, Ch. 9.6 on Varimax rotation, supra.
  • the rotated axes represent factors that influence the observed measurements for the samples.
  • the information from the principal factors can be used to help filter the experimental noise from the correlation functions. For example, it is possible to select a cut-off principal factor j ⁇ n, then compute distances and correlations between samples based on their representation in the j-dimensional principal factor space.
  • the experimental results represent the sample-dependent and selection-dependent intensities obtained in an experiment, arrayed in a measurement matrix.
  • the difference bands having various, defined, nucleotide lengths are arrayed as the columns of the matrix; they are obtained in various experiments that are selected using different members of the sets of subsequence pairs.
  • the samples represent the classes of cells, or cells treated with a set of putative pharmaceutical agents, or analogous sample sets, and are arrayed as the rows.
  • the values arrayed in the measurement matrix may then be subjected to correlation analysis to provide either direct sample correlations or correlations of differences.
  • the measurement matrix can also be subjected to a calculation providing a vectoral distance between samples; such a sample distance may also be obtained from the sample correlation result.
  • the distance vector can further be subjected to a linkage analysis to provide hierarchical clustering of the samples.
  • the correlated samples may be subjected to principal component analysis providing the principal factors contributing to a state or to a difference.
  • FIG. 2 A nonlimiting example of the way in which a principal component analysis may be carried out, using methods described herein, is presented in Figure 2.
  • the correlation matrix or the centered inner product matrix described above is subjected to appropriate operations to provide the principal components and the principal factors, based on their eigenvalues and eigenvectors.
  • a reduction in the number of dimensions employed in the number of eigenstates may provide a filtering effect, reducing the noise in the vector distances calculated.
  • the representations provided in the present invention find use in various applications of genomics in the biological and medical fields. Extents of relatedness and correlations provide rapid overviews of enzymatic reactions, metabolic pathways, and physiological effects that become distinguished when comparing states.
  • a pathological state is compared with a normal state, for example in a mammal, and especially in a human
  • the display of distinguished pathways is instructive in the development of therapeutic approaches and/or therapeutic agents for the treatment of the pathological state.
  • a putative pharmaceutical agent is compared to a state that omits the agent, or when one such agent is compared with another, important information is provided relating to the metabolic reactions induced by or undergone by the agent or agents, leading to optimal choice of such agents. This information may also provide leads to the development of novel pharmaceutical agents. If the genome being studied is a plant genome, such as the genome of an important crop plant, analogous principles apply.
  • Nucleic acid assays The present invention provides a method for generating a representation of the extent of relatedness between at least two classes of cells.
  • the cells in each class are chosen from among cells of a given cell type, cells from a given tissue, and cells from a given organ.
  • Generation of nucleic acids from the cell samples of choice may be as described in the GeneCallingTM methodology. See U.S. Patent No. 5,871 ,697.
  • the method includes the steps of: (a) defining a plurality of pairs of nucleotide subsequences, each pair consisting of a first subsequence and a second subsequence; (b) isolating the nucleic acid of each class of cells and assaying for the presence of a nucleic acid fragment with the first subsequence at one end and the second subsequence at another end and having a length separated by the first and second subsequences, and quantitating the extent to which each fragment is present; and (c) determining the extent of relatedness reflecting similarities or differences in the presence and quantitation of the fragments among the classes using software algorithm programs known in the art.
  • One important embodiment of this method i.e., determining the presence of the fragments and quantitating the amounts present, as described in step (b) above, is carried out by a process that includes the steps as follow.
  • samples of the nucleic acid from the cells of each class are digested with a plurality of specific pairs of restriction endonucleases ("REs").
  • Each sample is treated by one RE pair, where one RE of the pair targets the first subsequence described in step (a) above, and the second RE of the pair targets the second subsequence, with each digestion providing specific restriction fragments.
  • Each adapter DNA molecule comprises: (/) a shorter strand, preferably having no 5' terminal phosphate, consisting of a first and second portion, the first portion being a region at the 5' end that is complementary to the overhang produced by one of the REs of the given pair and a second portion hybridizable to the opposite longer strand of the adaptor, and ( / ' ) a longer strand, preferably having no 5' terminal phosphate, WO 00/15851 - 17 - PCT/US99/21 _
  • the longer strand is optionally labeled with fluorochrome 208, although any DNA labeling system that preferably allows multiple labels to be simultaneously distinguished is usable in this invention. See, e.g., Ausubel, et al. CURRENT PROTOCOLS IN MOLECULAR BIOLOGY, John Wiley & Sons, New York, NY, 1993.
  • output signals from each ligated fragment are detected for each sample population so treated.
  • Each ligated fragment generates output signals that characterize (a) the presence of the given subsequences corresponding to the RE pair used in a particular run, (b) the length between the two subsequences corresponding to the two REs employed in a given run, and (c) the quantitation of the relative amounts present of each fragment so generated in a given run.
  • a nucleotide sequence database may be searched for sequences that are predicted to produce, or alternatively, not produce, the one or more output signals generated by the nucleic acid from the cells of each class, given the parameters described above.
  • the analysis methods comprise, first, selecting a database of DNA sequences representative of the DNA sample to be analyzed, second, using this database and a description of the experiment to derive the pattern of simulated signals that would be generated, contained in a database of simulated signals, that will be produced by DNA fragments generated in the experiment, and third, for any particular detected signal, using the pattern or database of simulated signals to predict the sequences in the original sample likely to cause this signal.
  • a sequence from a searched database is predicted to produce the one or more output signals when that sequence has both (a) the same length between occurrences of target nucleotide subsequences as is represented by the one or more output signals, and (b) the same target nucleotide sub-sequences that are represented by said one or more output signals, or target nucleotide subsequences that are members of the same sets of target nucleotide sub-sequences represented by the one or more output signals.
  • a first analysis method is selecting a database of DNA sequences representative of the sample to be analyzed.
  • the DNA sequences to be analyzed will be derived from a tissue sample, typically a human sample examined for diagnostic or research pu ⁇ oses.
  • database selection begins with one or more publicly available databases which comprehensively record all observed DNA sequences.
  • databases are GenBank from the National Center for Biotechnology Information (Bethesda, Md.), the EMBL Data Library at the European Bioinformatics Institute (Hinxton Hall, UK) and databases from the National Center for Genome Research (Santa Fe, N.Mex.).
  • GenBank GenBank from the National Center for Biotechnology Information (Bethesda, Md.)
  • the EMBL Data Library at the European Bioinformatics Institute (Hinxton Hall, UK)
  • databases from the National Center for Genome Research (Santa Fe, N.Mex.).
  • any database containing entries for the sequences likely to be present in such a sample to be analyzed is usable in the further steps of
  • a second analysis method uses the previously selected database of sequences likely to be present in a sample and a description of an intended experiment to derive a pattern of the signals which will be produced by DNA fragments generated in the experiment.
  • This pattern can be stored in a computer implementation in any convenient manner. In the following, without limitation, it is described as being stored as a table of information. This table may be stored as individual records or by using a database system, such as any conventionally available relational database. Alternatively, the pattern may simply be stored as the image of the in-memory structures which represent the pattern.
  • a second important embodiment of this method i.e., determining the presence of the fragments and their quantitation, as described in step (b) above, is carried out by a process that includes the steps as follow.
  • a pair of oligonucleotide primers are provided, the pair consisting of a first primer and a second primer, wherein the first primer is complementary to the first subsequence and the second primer is complementary to the second subsequence.
  • the nucleotide sequence between the first subsequence and the second subsequence are amplified using the oligonucleotide primers to prime the amplification, thereby providing an amplicon characterized by the subsequence pair, a length between the two subsequences corresponding to the two primers employed in each pair and a quantitation of the extent to which each amplicon is present.
  • output signals are generated as above for each amplicon, each output signal characterizing (a) the subsequences of the pairs of primers, (b) the length, and (c) the quantitation.
  • a nucleotide sequence database may be searched for sequences that are predicted to produce, or alternatively, not produce, the one or more output signals generated by the nucleic acid from the cells of each class, given the parameters described above. Analysis methods are as described above.
  • This invention can be applied, for example and not by way of limitation, to in vitro cell populations or cell lines, to in vivo animal models of disease or other processes, to human samples, to purified cell populations perhaps drawn from actual wild-type occurrences, and to tissue samples containing mixed cell populations.
  • the cell or tissue sources can advantageously be a plant, a single celled animal, a multicellular animal, a bacterium, a virus, a fungus, or a yeast, etc.
  • the animal can advantageously be laboratory animals used in research, such as mice engineered or bred to have certain genomes or disease conditions or tendencies.
  • Cells used in the invention may be obtained from a mammal, preferably a human, having or suspected of having a diseased condition.
  • the diseased condition is a malignancy.
  • the in vitro cell populations or cell lines can be exposed to various exogenous factors to determine the effect of such factors on gene expression.
  • the exogenous factor is a putative pharmaceutical agent.
  • Cells so contacted with a putative pharmaceutical agent are treated with an amount of the agent sufficient to effect a change in the state of those cells or with an amount of the agent less than or equal to a predetermined upper limit of dosing concentration, prior to their being assayed. Measures of relatedness and extent of correlation may be made between cells so contacted with putative pharmaceutical agent and, for example, cells not so contacted.
  • the present invention provides a representation of the extent of relatedness between a first class of cells and a second class of cells.
  • the cells in each class are chosen from among cells of a given cell type, cells from a given tissue, and cells from a given organ, as described above.
  • the extent of relatedness reflects similarities or differences in the presence of pairs of nucleotide subsequences, each pair consisting of a first subsequence and a second subsequence, in a nucleotide length separating the first and second subsequences of the pair and in a quantitation of the extent to which each pair having the determined length is in the classes of cells.
  • Input information of the fragments to be analyzed are obtained by methods of nucleic acid analysis and quantitation as described in the NUCLEIC ACID ASSAYS section above.
  • the measure of relatedness is provided by calculating a distance that reflects the amplitude of a difference vector.
  • a difference vector is defined as a difference between a first vector and a second vector.
  • the first vector reflects information derived from the quantitation for each subsequence pair obtained for the first class of cells
  • the second vector reflects the analogous information derived from the second class.
  • the different elements of each vector relate to data obtained using different subsequence pairs.
  • the extent of relatedness is related to a distance. This distance reflects the amplitude of a difference vector that is a difference between a first vector which reflects information derived from the quantitation for each subsequence pair obtained for the first class and a second vector which reflects the corresponding information obtained for the second class.
  • the different elements of each vector relate to data obtained using different subsequence pairs.
  • the representation includes a tree structure reflecting the extent of relatedness is provided by generating a tree structure reflecting the relatedness between any two classes. The branches of the tree structure reflect the difference vectors and are ramified from nodes.
  • the representation is obtained employing the methods of the invention, including the methods that have been summarized in the paragraphs immediately above.
  • the cells in at least one class are obtained as described in the NUCLEIC ACID ANALYSIS section above. Correlation analysis methodology
  • the invention also provides a method for generating a representation of the correlation between a first class of cells and a second class of cells.
  • the correlation reflects a change in the nature and amount of nucleic acids present in the classes.
  • the cells in each class are chosen from among cells of a given cell type, cells from a given tissue, and cells from a given organ.
  • the method of nucleic acid analysis and quantitation are as describe in the NUCLEIC ACID ASSAYS section above.
  • the correlation between the cells of the first class and cells of the second class are correlated, and a representation of the correlation is prepared.
  • the quantitation of the fragments in the invention corresponding to the RE pair used in a given run and the length of each fragment so generated; thereby providing a quantitative measure of the extent to which the nucleic acid present in the cells in each class contains fragments having the specific subsequence pairs and the nucleotide length between the pairs.
  • the correlation is related to a set of orthonormal eigenvectors, as described in the DISTANCES section above.
  • the elements of the basis set upon which the eigenvectors are constructed reflect particular biochemical or physiological pathways correlated between the cells of the two classes
  • Each of these eigenvectors is associated with an eigenvalue that is an integer greater than zero.
  • the coefficients of the basis set elements in each eigenvector whose eigenvalue is less than or equal to this upper limit reflects the contribution of the corresponding pathway to the biochemical or physiological differences correlated between the cells of the first class and the cells of the second class.
  • the representation is a cluster diagram or a dendrogram, and includes a tree structure reflecting the relatedness of the pathways involved in the biochemical or physiological response to a difference between cells of the two classes.
  • a correlation matrix is calculated that provides a distance determination in which the distance reflects the amplitude of a difference vector.
  • This vector is a difference between two vectors each of which reflects information obtained for the response of one of the two classes to the difference between the classes, and wherein the branches of the tree structure reflect the difference vectors and the branches are ramified from nodes.
  • the cells in at least one class obtained as described in the NUCLEIC ACID ANALYSIS section above.
  • the present invention also provides a display means displaying a representation of the extent of relatedness between a first class of cells and a second class of cells.
  • the cells in each class are chosen from among cells of a given cell type, cells from a given tissue, and cells from a given organ, as described above.
  • the extent of relatedness reflects similarities or differences in the presence of pairs of nucleotide subsequences, each pair consisting of a first subsequence and a second subsequence, in a nucleotide length separating the first and second subsequences of the pair and in a quantitation of the extent to which each pair having the determined length is in the classes of cells.
  • the extent of relatedness is related to a distance.
  • This distance reflects the amplitude of a difference vector that is a difference between a first vector which reflects information derived from the quantitation for each subsequence pair obtained for the first class and a second vector which reflects the corresponding information obtained for the second class.
  • the different elements of each vector relate to data obtained using different subsequence pairs.
  • the representation includes a tree structure reflecting the relatedness between any two classes, in which the branches of the tree structure reflect the difference vectors and the branches are ramified from nodes.
  • the display means displaying a representation of the extent of relatedness the representation is obtained employing the methods of the invention, including the methods that have been summarized in the paragraphs immediately above.
  • the present invention additionally provides a display means displaying a representation of the correlation between a first class of cells and a second class of cells.
  • the cells in each class are chosen from among cells of a given cell type, cells from a given tissue, and cells from a given organ, as described above.
  • the correlation reflects differences between the first class and the second class in the presence of a pair of nucleotide subsequences, each pair consisting of a first subsequence and a second subsequence and the nucleotide length separating the first and second subsequences of the pair, and in a quantitation of the extent to which each pair having the determined length is present in the cells.
  • the correlation is related to a set of orthonormal eigenvectors.
  • the elements of the basis set upon which the eigenvectors are constructed reflect particular biochemical or physiological pathways correlated between the cells of the two classes
  • Each of these eigenvectors is associated with an eigenvalue that is an integer greater than zero.
  • the coefficients of the basis set elements in each eigenvector whose eigenvalue is less than or equal to this upper limit reflect the contribution of the corresponding pathway to the biochemical or physiological differences correlated between the cells of the first class and the cells of the second class.
  • the representation is a cluster diagram or a dendrogram, and includes a tree structure reflecting the relatedness of the pathways involved in the biochemical or physiological response to a difference between cells of the two classes.
  • a correlation matrix is calculated that provides a distance determination in which the distance reflects the amplitude of a difference vector.
  • This vector is a difference between two vectors each of which reflects information obtained for the response of one of the two classes to the difference between the classes.
  • the branches of the tree structure reflect the difference vectors and the branches are ramified from nodes.
  • the representation is obtained employing the methods of the invention, including the methods that have been summarized in the paragraphs immediately above.
  • the representation is obtained employing the methods of the invention, including the methods that have been summarized in the paragraphs immediately above.
  • the display means displaying a representation of the correlation the cells in at least one class obtained as described in the NUCLEIC ACID ANALYSIS section above.
  • the techniques described here are also useful for providing representations of nucleic acid fragments or genes.
  • the starting point for the analysis is the matrix I sd described previously, where s labels the sample (or group of samples or distinct types of cells) and d labels a particular measurement of the expression level of a particular gene in that class.
  • I the matrix of I sd described previously, where s labels the sample (or group of samples or distinct types of cells) and d labels a particular measurement of the expression level of a particular gene in that class.
  • s labels the sample (or group of samples or distinct types of cells) and d labels a particular measurement of the expression level of a particular gene in that class.
  • representations based on the rows of I each representing a different sample or group of samples
  • Hierarchical and geometrical representations of nucleic acids based on their relative abundance across a series of cells, can be used to infer genes that are co-ex
  • the data matrix of intensities I can be described more generally as a representation in which each row corresponds to a particular biological sample or group of samples, and each column corresponds to a particular nucleic acid molecule or class of molecules whose quantities are measured in each of the biological states.
  • differential-display methods In addition to the differential-display methods described to provide measurements of nucleic acid quantities, other methods for obtaining measurements of the nucleic acids present in a cell are available. These include restriction fragment length polymorphism, amplification fragment length polymorphism, EST sequencing, serial analysis of gene expression, hybridization to oligonucleotide probes, and other methods known in the art. Other methods, such as quantification by TaqMan or Northern blots, are also used. All of these methods generate data sets that can be analyzed according to the methods described here. The measurements I sd for each biological state and nucleic acid can correspond to absolute concentrations, concentrations relative to a standard (either ratio or numeric difference), or other convenient measures.
  • the methods of the invention includes analysis of populations ranging from 5, 10, 25, 50, 100, 1000, 10,000 or 100,000 or more members.
  • mice Male Sprague-Dawley rats (Harlan Sprague Dawley, Inc., Indianapolis, Indiana) of 10-14 weeks of age were gavage-fed and dosed once a day for three days with the following drugs, dissolved in sterile water, at the following levels: phenobarbitol 3.81 mg/kg/day gabapentin 34.29 mg/kg/day vigabatrin 150 mg/kg/day paraldehyde 77.08 mg/kg/day.
  • drugs dissolved in sterile water, at the following levels: phenobarbitol 3.81 mg/kg/day gabapentin 34.29 mg/kg/day vigabatrin 150 mg/kg/day paraldehyde 77.08 mg/kg/day.
  • ED 100 the upper limit of the effective dose for humans
  • ED 100 the upper limit of the effective dose for humans
  • Three rats were used for each drug treatment, and an additional three rats to match each drug were treated with sterile water to serve as a control.
  • Rats were sacrificed 24 hours after the final dose and their brains were harvested. Collection of mRNA, synthesis of cDNA, and differential display protocols were carried out according to methods described in U. S. Patent No. 5,871,697 and Shimkets et al. 1999 (Nature Biotechnology 17:798-803).
  • the intensities I sr (x) for each of the three animals treated with the same drug were combined into a single average where the subscript a labels the drug.
  • the standard deviation s ar (x) was also computed for the measurements from the individual animals treated with the drug.
  • the averages I ar (x) and standard deviations s ar (x) for each drug were compared with the average I cr (x) and standard deviation s cr (x) for the sterile water control treatment. A difference at length x was marked if
  • ABS(ln [I ar (x)/I cr (x)]) > ln(1.5) (5) and if the significance was smaller than 0.15 for a two-tailed t-test with t [I ar (x) - I cr (x)] / [ ⁇ s ar (x) 2 + s cr (x) 2 ⁇ / 2 ] 1/2 (6) and infinite degrees of freedom.
  • the difference intensities marked according to this procedure may then be inspected by eye and visually significant differences may be retained. 3.
  • the intensity I ar (x) I a was determined for each of the drug treatments, whether or not that particular treatment has a difference compared to the control.
  • the final data matrix I ad has 8 rows: 1 row for each of the 4 drugs, and 1 row for each of 4 replicates of the water control data.
  • the matrix has as many columns as the number of differences detected in the differential display pattern.
  • the Pearson correlation coefficient C ab between the 8 classes of samples (4 drugs, 4 water controls) was determined using methods provided in the Detailed Description of the Invention. If a data element for a particular difference was missing for a particular treatment, that difference did not contribute to the correlation coefficient.
  • the correlations are shown in the Table 1 below, with the standard deviation within a drug shown as the diagonal elements.
  • Fig. 3 The horizontal distances in Fig. 1 A were proportional to the pairwise Pearson distance between clusters.
  • the correlation matrix C ab also served as the starting point for principal factor analysis.
  • principal components were calculated using the inner product matrix from multidimensional scaling
  • the components are ordered from 1 (most informative) to 8 (least informative).
  • the negative eigenvalues arise from the method used to account for missing data. If missing data had been handled in an alternate manner, for example if a missing element had been set to the average value or if the analysis were restricted to differences for which no data was missing, the eigenvalues would all be non-negative.
  • Fig. 4 the treatments are displayed by projection onto principal factors.
  • Factor 1 discriminates between drugs, where it has a negative value, and controls, where it has a positive value.
  • Factor 2 discriminates between the drug treatments.

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Abstract

L'invention concerne une technique permettant de créer une représentation du degré de parenté entre au moins deux classes de cellules. L'invention a trait également à une technique permettant de créer une représentation de la corrélation entre une première et seconde classes de cellules. La corrélation représente un changement dans la nature et la quantité d'acides nucléiques présents dans les classes. Ces techniques consistent à sélectionner les cellules de chaque classe parmi des cellules d'un type de cellule donnée, des cellules d'un tissu donné, et des cellules d'un organe donné. Ces techniques permettent d'établir des similitudes ou des différences entre les classes en définissant plusieurs paires de sous-séquences de nucléotides, chaque paire comprenant une première et seconde sous-séquences. Elles consistent également à déterminer dans l'acide nucléique de chaque classe de cellules, la présence d'un fragment comportant la première sous-séquence à une extrémité et la seconde sous-séquence à une autre extrémité, fragment qui possède une longueur séparant la première et la seconde sous-séquences, et à déterminer dans quelle mesure le fragment est présent. Ces techniques permettent de déterminer le degré de parenté reflétant les similitudes ou les différences parmi les classes. Par ailleurs, l'invention concerne un dispositif d'affichage affichant une représentation du degré de parenté entre les classes de cellules, ainsi qu'une représentation de la corrélation entre la première et seconde classes de cellules. De plus, l'invention porte sur une représentation du degré de parenté entre les classes de cellules, et une représentation de la corrélation entre la première et seconde classes de cellules.
EP99969123A 1998-09-17 1999-09-17 Classification geometrique et hierarchique fondee sur l'expression genetique Withdrawn EP1114187A1 (fr)

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