Chemistry: molecular biology and microbiology – Measuring or testing process involving enzymes or... – Involving nucleic acid
Reexamination Certificate
1998-10-27
2001-03-20
Brusca, John S. (Department: 1631)
Chemistry: molecular biology and microbiology
Measuring or testing process involving enzymes or...
Involving nucleic acid
C435S069600, C436S518000, C530S333000
Reexamination Certificate
active
06203987
ABSTRACT:
1 FIELD OF THE INVENTION
The field of this invention relates to methods for enhanced detection of biological responses to perturbations. In particular, it relates to methods for analyzing structure in biological expression patterns for the purposes of improving the ability to detect certain specific gene regulations and to classify more accurately the actions of compounds that produce complex patterns of gene regulation in the cell.
2 BACKGROUND OF THE INVENTION
Within the past decade, several technologies have made it possible to monitor the expression level of a large number of transcripts at any one time (see, e.g., Schena et al., 1995, Quantitative monitoring of gene expression patterns with a complementary DNA micro-array,
Science
270:467-470; Lockhart et al., 1996, Expression monitoring by hybridization to high-density oligonucleotide arrays,
Nature Biotechnology
14:1675-1680; Blanchard et al., 1996, Sequence to array: Probing the genome's secrets,
Nature Biotechnology
14, 1649; U.S. Pat. No. 5,569,588, issued Oct. 29, 1996 to Ashby et al. entitled “Methods for Drug Screening”). In organisms for which the complete genome is known, it is possible to analyze the transcripts of all genes within the cell. With other organisms, such as human, for which there is an increasing knowledge of the genome, it is possible to simultaneously monitor large numbers of the genes within the cell.
Such monitoring technologies have been applied to the identification of genes which are up regulated or down regulated in various diseased or physiological states, the analyses of members of signaling cellular states, and the identification of targets for various drugs. See, e.g., Friend and Hartwell, U.S. Provisional Patent Application Ser. No. 60/039,134, filed on Feb. 28, 1997; Stoughton, U.S. patent application Ser. No. 09/099,722, filed on Jun. 19, 1998; Stoughton and Friend, U.S. patent application Ser. No. 09/074,983, filed on filed on May 8, 1998; Friend and Hartwell, U.S. Provisional Application Ser. No. 60/056,109, filed on Aug. 20, 1997; Friend and Hartwell, U.S. application Ser. No. 09/031,216, filed on Feb. 26, 1998; Friend and Stoughton, U.S. Provisional Application Ser. No. 60/084,742 (filed on May 8, 1998), No. 60/090,004 (filed on Jun. 19, 1998) and No. 60/090,046 (filed on Jun. 19, 1998), all incorporated herein by reference for all purposes.
Levels of various constituents of a cell are known to change in response to drug treatments and other perturbations of the cell's biological state. Measurements of a plurality of such “cellular constituents” therefore contain a wealth of information about the effect of perturbations and their effect on the cell's biological state. Such measurements typically comprise measurements of gene expression levels of the type discussed above, but may also include levels of other cellular components such as, but by no means limited to, levels of protein abundances, or protein activity levels. The collection of such measurements is generally referred to as the “profile” of the cell's biological state.
The number of cellular constituents is typically on the order of a hundred thousand for mammalian cells. The profile of a particular cell is therefore typically of high complexity. Any one perturbing agent may cause a small or a large number of cellular constituents to change their abundances or activity levels. Not knowing what to expect in response to any given perturbation will therefore require measuring independently the responses of these about 10
5
constituents if the action of the perturbation is to be completely or at least mostly characterized. The complexity of the biological response data coupled with measurement errors makes such an analysis of biological response data a challenging task.
Current techniques for quantifying profile changes suffer from high rates of measurement error such as false detection, failures to detect, or inaccurate quantitative determinations. Therefore, there is a great demand in the art for methods to enhance the detection of structure in biological expression patterns.
Discussion or citation of a reference herein shall not be construed as an admission that such reference is prior art to the present invention.
3 SUMMARY OF THE INVENTION
This invention provides methods for enhancing detection of structures in the response of biological systems to various perturbations, such as the response to a drug, a drug candidate or an experimental condition designed to probe biological pathways as well as changes in biological systems that correspond to a particular disease or disease state, or to a treatment of a particular disease or disease state. The methods of this invention have extensive applications in the areas of drug discovery, drug therapy monitoring, genetic analysis, and clinical diagnosis. This invention also provides apparatus and computer instructions for performing the enhanced detection of biological response patterns, drug discovery, monitoring of drug therapies, genetic analysis, and clinical diagnosis.
One aspect of the invention provides methods for classifying cellular constituents (measurable biological variables, such as gene transcripts and protein activities) into groups based upon the co-variation among those cellular constituents. Each of the groups has cellular constituents that co-vary in response to perturbations. Those groups are termed cellular constituent sets.
In some specific embodiments, genes are grouped according to the degree of co-variation of their transcription, presumably co-regulation. Groups of genes that have co-varying transcripts are termed genesets. Cluster analysis or other statistical classification methods are used to analyze the co-variation of transcription of genes in response to a variety of perturbations. In one specific embodiment, clustering algorithms are applied to expression profiles (e.g, a collection of transcription rates of a number of genes) obtained under a variety of cellular perturbations to construct a “similarity tree” or “clustering tree” which relates cellular constituents by the amount of co-regulation exhibited. Genesets are defined on the branches of a clustering tree by cutting across the clustering tree at different levels in the branching hierarchy. In some embodiments, the cutting level is chosen based upon the number of distinct response pathways expected for the genes measured. In some other embodiments, the tree is divided into as many branches as they are truly distinct in terms of minimal distance value between the individual branches. In some preferred embodiments, objective statistical tests are employed to define truly distinct branches. One exemplary embodiment of such a statistic test employs Monte Carlo randomization of the perturbation index for each gene's responses across all perturbations tested. In some preferred embodiments, the cut off level is set so that branching is significant at the 95% confidence level. In preferred embodiments, clusters with one or two genes are discarded. In some other embodiments, however, small clusters with one or two genes are included in genesets.
As the diversity of perturbations in the clustering set becomes very large, the genesets which are clearly distinguishable get smaller and more numerous. However, it is a discovery of the inventors that even over very large experiment sets, there is a number of genesets that retain their coherence. These genesets are termed irreducible genesets. In some embodiments of the invention, a large number of diverse perturbations are applied to obtain these irreducible genesets.
Statistically derived genesets may be refined using regulatory sequence information to confirm members that are co-regulated, or to identify more tightly co-regulated subgroups. In such embodiments, genesets may be defined by their response pattern to individual biological experimental perturbations such as specific mutations, or specific growth conditions, or specific compounds. The statistically derived genesets may be further refined based upon biologica
Friend Stephen H.
Stoughton Roland
Brusca John S.
Pennie & Edmonds LLP
Rosetta Inpharmatics, Inc.
Siu Stephen
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