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Breast cancer prognostics

Abstrict

A method of providing a prognosis of breast cancer is conducted by analyzing the expression of a group of genes. Gene expresson profiles in a variety of medium such as microarrays are included as are kits that contain them.

Claims

I claim:

1. A method of assessing breast cancer status comprising identifying differential modulation of each gene (relative to the expression of the same genes in a normal population) in a combination of genes selected from the group consisting of Seq. ID. No. 1-56.

2. The method of claim 1 wherein there is at least a 2 fold difference in the expression of the modulated genes.

3. The method of claim 1 wherein the genes selected are those which are up-regulated as an indicator of relapse or metastasis.

4. The method of claim 1 wherein the genes selected are those which are down-regulated as an indicator of relapse or metastasis.

5. The method of claim 1 wherein the genes selected comprise all of Seq. ID No 1-56.

6. The method of claim 1 wherein the p-value indicating differential modulation is less than 0.05.

7. The method of claim 1 further comprising a breast marker that is not genetically based.

8. The method of claim 7 wherein the non-genetically based marker is selected from the group consisting of ER and estrogen regulated proteins and peptides.

9. The method of claim 1 wherein the genes include the genes set forth in Table 4.

10. The method of claim 1 wherein the genes include the genes set forth in Table 5.

11. The method of claim 1 wherein the genes include the genes set forth in Table 6.

12. The method of claim 1 wherein the genes include the genes set forth in Table 7.

13. A prognostic portfolio comprising isolated nucleic acid sequences, their complements, or portions thereof of a combination of genes selected from the group consisting of Seq. ID. No. 1-56.

14. The portfolio of claim 13 in a matrix suitable for identifying the differential expression of the genes contained therein.

15. The portfolio of claim 14 wherein said matrix is employed in a microarray.

16. The portfolio of claim 15 wherein said microarray is a cDNA microarray.

17. The portfolio of claim 15 wherein said microarray is an oligonucleotide microarray.

18. A kit for determining the prognosis of a breast cancer patient comprising materials for detecting isolated nucleic acid sequences, their compliments, or portions thereof of a combination of genes selected from the group consisting of Seq. ID. No. 1-56.

19. The kit of claim 18 comprising reagents for conducting a microarray analysis.

20. The kit of claim 18 further comprising a medium through which said nucleic acid sequences, their compliments, or portions thereof are assayed.

21. The kit of claim 18 wherein the genes include the genes set forth in Table 4.

22. The kit of claim 18 wherein the genes include the genes set forth in Table 5.

23. The kit of claim 18 wherein the genes include the genes set forth in Table 6.

24. The method of claim 18 wherein the genes include the genes set forth in Table 7.

25. A method of assessing response to treatment for breast cancer comprising identifying differential modulation of each gene (relative to the expression of the same genes in a normal population) in a combination of genes selected from the group consisting of Seq. ID. No. 1-56.

26. The method of claim 25 wherein the genes include the genes set forth in Table 4.

27. The method of claim 25 wherein the genes include the genes set forth in Table 5.

28. The method of claim 25 wherein the genes include the genes set forth in Table 6.

29. The method of claim 25 wherein the genes include the genes set forth in Table 7.

30. Articles for assessing breast cancer status comprising materials for identifying nucleic acid sequences, their complements, or portions thereof of a combination of genes selected from the group consisting of Seq. ID. No. 1-56.

31. The articles of claim 30 wherein the genes selected are up regulated and are further selected from the group consisting of Seq. ID. No. 1-26 and Seq. ID No. 56.

32. The articles of claim 30 wherein the genes selected are down regulated and are further selected from the group consisting of Seq. ID. No. 27-55.

33. The articles of claim 30 wherein the genes include the genes set forth in Table 4.

34. The articles of claim 30 wherein the genes include the genes set forth in Table 5.

35. The articles of claim 30 wherein the genes include the genes set forth in Table 6.

36. The articles of claim 30 wherein the genes include the genes set forth in Table 7.

Description

BACKGROUND

[0001] This invention relates to prognostics for breast cancer based on the gene expression profiles of biological samples.

[0002] In breast cancers, prognosis is determined primarily by the presence or absence of metastases in draining axillary lymph nodes. However, in approximately one third of women with breast cancer who have negative lymph nodes, the disease recurs and about one third of patients with positive lymph nodes are free of disease ten years after local or regional therapy. Furthermore, an increasing proportion of breast cancers are being diagnosed at an early stage because of increased awareness and wider use of screening modalities. Universal application of systematic therapy to these patients often leads to over-treatment. According to the St Gallen and NIH consensus, 70-80% of the Stage I and II patients would not have developed distant metastases without adjuvant treatment and may potentially suffer from the side effects. These data highlight the need for more sensitive and specific prognostic assays that could significantly reduce the number of patients that receive unnecessary treatment.

[0003] Tumor size and lymphatic or vascular invasion have been found to be of significant prognostic value in several studies. Quantitative pathological features, i.e. nuclear morphology, DNA content and proliferative activity may further demarcate tumors that have a high chance of micrometastases. Known molecular genetic changes that affect patient outcome include Her2/NEU over-expression, DNA amplifications, p53 mutations, ER/PR status, uPA and PAI expression. Because the metastatic cascade is a complex process that includes multiple steps, single factors that contribute to tumor process have limitations for prognostic assessment. The gene expression profiles of this invention will provide increased prognostic power.

SUMMARY OF THE INVENTION

[0004] The invention is a method of assessing the likelihood of a recurrence or metastasis of breast cancer in a patient diagnosed with or treated for breast cancer. The method involves the analysis of a gene expression profile.

[0005] In one aspect of the invention, the gene expression profile includes 56 genes. In yet other aspects of the invention, the profiles comprise those of at least 45 genes, 26 genes, 13 genes, and 6 genes respectively.

[0006] Articles used in practicing the methods are also an aspect of the invention. Such articles include gene expression profiles or representations of them that are fixed in machine-readable media such as computer readable media.

[0007] Articles used to identify gene expression profiles can also include substrates or surfaces (such as microarrays) to capture and/or indicate the presence, absence, or degree of gene expression.

[0008] In yet another aspect of the invention, kits include reagents for conducting the gene expression analysis prognostic of breast caner recurrence or metastasis.

BRIEF DESCRIPTION OF THE DRAWINGS

[0009] FIG. 1 is a standard Kaplan-Meier Plot constructed from the patient data as a training set as described in the Examples.

[0010] FIG. 2 is a standard Kaplan-Meier Plot constructed from the patient data as a testing set as described in the Examples.

[0011] FIG. 3 is a standard Kaplan-Meier Plot constructed from the patient data of 54 patients (training and testing data combined) using a 56-gene expression profile.

DETAILED DESCRIPTION

[0012] The mere presence or absence of particular nucleic acid sequences in a tissue sample has only rarely been found to have diagnostic or prognostic value. Information about the expression of various proteins, peptides or mRNA, on the other hand, is increasingly viewed as important. The mere presence of nucleic acid sequences having the potential to express proteins, peptides, or mRNA (such sequences referred to as "genes") within the genome by itself is not determinative of whether a protein, peptide, or mRNA is expressed in a given cell. Whether or not a given gene capable of expressing proteins, peptides, or mRNA does so and to what extent such expression occurs, if at all, is determined by a variety of complex factors. Irrespective of difficulties in understanding and assessing these factors, assaying gene expression can provide useful information about the occurrence of important events such as tumerogenesis, metastasis, apoptosis, and other clinically relevant phenomena. Relative indications of the degree to which genes are active or inactive can be found in gene expression profiles. The gene expression profiles of this invention are used to provide a prognosis and treat patients for breast cancer.

[0013] Sample preparation requires the collection of patient samples. Patient samples used in the inventive method are those that are suspected of containing diseased cells such as epithelial cells taken from a breast or lymph node sample or from surgical margins. One useful technique for obtaining suspect samples is Laser Capture Microdisection (LCM). LCM technology provides a way to select the cells to be studied, minimizing variability caused by cell type heterogeneity. Consequently, moderate or small changes in gene expression between normal and cancerous cells can be readily detected. In a preferred method, the samples comprise circulating epithelial cells extracted from peripheral blood. These can be obtained according to a number of methods but the most preferred method is the magnetic separation technique described in U.S. Pat. No. 6,136,182 assigned to Immunivest Corp which is incorporated herein by reference. Once the sample containing the cells of interest has been obtained, RNA is extracted and amplified and a gene expression profile is obtained, preferably via micro-array, for genes in the appropriate portfolios.

[0014] Preferred methods for establishing gene expression profiles include determining the amount of RNA that is produced by a gene that can code for a protein or peptide. This is accomplished by reverse transcriptase PCR (RT-PCR), competitive RT-PCR, real time RT-PCR, differential display RT-PCR, Northern Blot analysis and other related tests. While it is possible to conduct these techniques using individual PCR reactions, it is best to amplify complimentary DNA (cDNA) or complimentary RNA (cRNA) produced from mRNA and analyze it via microarray. A number of different array configurations and methods for their production are known to those of skill in the art and are described in U.S. patents such as: U.S. Pat. Nos. 5,445,934; 5,532,128; 5,556,752; 5,242,974; 5,384,261; 5,405,783; 5,412,087; 5,424,186; 5,429,807; 5,436,327; 5,472,672; 5,527,681; 5,529,756; 5,545,531; 5,554,501; 5,561,071; 5,571,639; 5,593,839; 5,599,695; 5,624,711; 5,658,734; and 5,700,637; the disclosures of which are incorporated herein by reference.

[0015] Microarray technology allows for the measurement of the steady-state mRNA level of thousands of genes simultaneously thereby presenting a powerful tool for identifying effects such as the onset, arrest, or modulation of uncontrolled cell proliferation. Two microarray technologies are currently in wide use. The first are cDNA arrays and the second are oligonucleotide arrays. Although differences exist in the construction of these chips, essentially all downstream data analysis and output are the same. The product of these analyses are typically measurements of the intensity of the signal received from a labeled probe used to detect a cDNA sequence from the sample that hybridizes to a nucleic acid sequence at a known location on the microarray. Typically, the intensity of the signal is proportional to the quantity of cDNA, and thus mRNA, expressed in the sample cells. A large number of such techniques are available and useful. Preferred methods for determining gene expression can be found in U.S. Pat. No. 6,271,002 to Linsley, et al.; U.S. Pat. No. 6,218,122 to Friend, et al.; U.S. Pat. No. 6,218,114 to Peck, et al.; and U.S. Pat. No. 6,004,755 to Wang, et al., the disclosure of each of which is incorporated herein by reference.

[0016] Analysis of the expression levels is conducted by comparing such intensities. This is best done by generating a ratio matrix of the expression intensities of genes in a test sample versus those in a control sample. For instance, the gene expression intensities from a diseased tissue can be compared with the expression intensities generated from normal tissue of the same type (e.g., diseased breast tissue sample vs. normal breast tissue sample). A ratio of these expression intensities indicates the fold-change in gene expression between the test and control samples.

[0017] Gene expression profiles can also be displayed in a number of ways. The most common method is to arrange a raw fluorescence intensities or ratio matrix into a graphical dendogram where columns indicate test samples and rows indicate genes. The data is arranged so genes that have similar expression profiles are proximal to each other. The expression ratio for each gene is visualized as a color. For example, a ratio less than one (indicating down-regulation) may appear in the blue portion of the spectrum while a ratio greater than one (indicating up-regulation) may appear as a color in the red portion of the spectrum. Commercially available computer software programs are available to display such data including "GENESPRINT" from Silicon Genetics, Inc. and "DISCOVERY" and "INFER" software from Partek, Inc.

[0018] Modulated genes used in the methods of the invention are described in the Examples. The genes that are differentially expressed are either up regulated or down regulated in patients with a relapse of breast cancer relative to those without a relapse. Up regulation and down regulation are relative terms meaning that a detectable difference (beyond the contribution of noise in the system used to measure it) is found in the amount of expression of the genes relative to some baseline. In this case, the baseline is the measured gene expression of a non-relapsing patient. The genes of interest in the diseased cells (from the relapsing patients) are then either up regulated or down regulated relative to the baseline level using the same measurement method. Diseased, in this context, refers to an alteration of the state of a body that interrupts or disturbs, or has the potential to disturb, proper performance of bodily functions as occurs with the uncontrolled proliferation of cells. Someone is diagnosed with a disease when some aspect of that person's genotype or phenotype is consistent with the presence of the disease. However, the act of conducting a diagnosis or prognosis includes the determination of disease/status issues such as determining the likelihood of relapse or metastasis and therapy monitoring. In therapy monitoring, clinical judgments are made regarding the effect of a given course of therapy by comparing the expression of genes over time to determine whether the gene expression profiles have changed or are changing to patterns more consistent with normal tissue.

[0019] Preferably, levels of up and down regulation are distinguished based on fold changes of the intensity measurements of hybridized microarray probes. A 2.0 fold difference is preferred for making such distinctions or a p-value less than 0.05. That is, before a gene is said to be differentially expressed in diseased/relapsing versus normal/non-relapsing cells, the diseased cell is found to yield at least 2 times more, or 2 times less intensity than the normal cells. The greater the fold difference, the more preferred is use of the gene as a diagnostic or prognostic tool. Genes selected for the gene expression profiles of the instant invention have expression levels that result in the generation of a signal that is distinguishable from those of the normal or non-modulated genes by an amount that exceeds background using clinical laboratory instrumentation.

[0020] Statistical values can be used to confidently distinguish modulated from non-modulated genes and noise. Statistical tests find the genes most significantly different between diverse groups of samples. The Student's t-test is an example of a robust statistical test that can be used to find significant differences between two groups. The lower the p-value, the more compelling the evidence that the gene is showing a difference between the different groups. Nevertheless, since microarrays measure more than one gene at a time, tens of thousands of statistical tests may be asked at one time. Because of this, one is unlikely to see small p-values just by chance and adjustments for this using a Sidak correction as well as a randomization/permutation experiment can be made. A p-value less than 0.05 by the t-test is evidence that the gene is significantly different. More compelling evidence is a p-value less then 0.05 after the Sidak correction is factored in. For a large number of samples in each group, a p-value less than 0.05 after the randomization/permutation test is the most compelling evidence of a significant difference.

[0021] Another parameter that can be used to select genes that generate a signal that is greater than that of the non-modulated gene or noise is the use of a measurement of absolute signal difference. Preferably, the signal generated by the modulated gene expression is at least 20% different than those of the normal or non-modulated gene (on an absolute basis). It is even more preferred that such genes produce expression patterns that are at least 30% different than those of normal or non-modulated genes.

[0022] Genes can be grouped so that information obtained about the set of genes in the group provides a sound basis for making a clinically relevant judgment such as a diagnosis, prognosis, or treatment choice. These sets of genes make up the portfolios of the invention. In this case, the judgments supported by the portfolios involve breast cancer. As with most diagnostic markers, it is often desirable to use the fewest number of markers sufficient to make a correct medical judgment. This prevents a delay in treatment pending further analysis as well inappropriate use of time and resources.

[0023] Preferably, portfolios are established such that the combination of genes in the portfolio exhibit improved sensitivity and specificity relative to individual genes or randomly selected combinations of genes. In the context of the instant invention, the sensitivity of the portfolio can be reflected in the fold differences exhibited by a gene's expression in the diseased state relative to the normal state. Specificity can be reflected in statistical measurements of the correlation of the signaling of gene expression with the condition of interest. For example, standard deviation can be a used as such a measurement. In considering a group of genes for inclusion in a portfolio, a small standard deviation in expression measurements correlates with greater specificity. Other measurements of variation such as correlation coefficients can also be used in this capacity.

[0024] A preferred method of establishing gene expression portfolios is through the use of optimization algorithms such as the mean variance algorithm widely used in establishing stock portfolios. This method is described in detail in the patent application entitled "Selection of Markers" by Tim Jatkoe, et. al., filed on Mar. 21, 2003 (application Ser. No. 10/394,087, incorporated herein by reference). Essentially, the method calls for the establishment of a set of inputs (stocks in financial applications, expression as measured by intensity here) that will optimize the return (e.g., signal that is generated) one receives for using it while minimizing the variability of the return. Many commercial software programs are available to conduct such operations. "Wagner Associates Mean-Variance Optimization Application", referred to as "Wagner Software" throughout this specification, is preferred. This software uses functions from the "Wagner Associates Mean-Variance Optimization Library" to determine an efficient frontier and optimal portfolios.

[0025] Use of this type of software requires that microarray data (i.e. intensity measurements) be transformed so that it can be treated as an input in the way stock return and risk measurements are used when the software is used for its intended financial analysis purposes.

[0026] The process of portfolio selection and characterization of an unknown is summarized as follows:

[0027] 1. Choose baseline class

[0028] 2. Calculate mean, and standard deviation of each gene for baseline class samples

[0029] 3. Calculate (X*Standard Deviation + Mean) for each gene. This is the baseline reading from which all other samples will be compared. X is a stringency variable with higher values of X being more stringent than lower.

[0030] 4. Calculate ratio between each Experimental sample versus baseline reading calculated in step 3.

[0031] 5. Transform ratios such that ratios less than 1 are negative (eg. using Log base 10). (Down regulated genes now correctly have negative values necessary for MV optimization).

[0032] 6. These transformed ratios are used as inputs in place of the asset returns that are normally used in the software application.

[0033] 7. The software will plot the efficient frontier and return an optimized portfolio at any point along the efficient frontier.

[0034] 8. Choose a desired return or variance on the efficient frontier.

[0035] 9. Calculate the Portfolio's Value for each sample by summing the multiples of each gene's intensity value by the weight generated by the portfolio selection algorithm.

[0036] 10. Calculate a boundary value by adding the mean Portfolio Value for Baseline groups to the multiple of Y and the Standard Deviation of the Baseline's Portfolio Values. Values greater than this boundary value shall be classified as the Experimental Class.

[0037] 11. Optionally one can reiterate this process until best prediction accuracy is obtained.

[0038] Alternatively, genes can first be pre-selected by identifying those genes whose expression shows some minimal level of differentiation. The pre-selection in this alternative method is preferably based on a threshold given by 1 1 ( t - n ) ( t + n ) ,

[0039] where .mu., is the mean of the subset known to possess the disease or condition, .mu..sub.n is the mean of the subset of normal samples, and .phi., +.phi..sub.n represent the combined standard deviations. A signal to noise cutoff can also be used by pre-selecting the data according to a relationship such as 2 0.5 ( t - MAX n ) ( t + n ) .

[0040] This ensures that genes that are pre-selected based on their differential modulation are differentiated in a clinically significant way. That is, above the noise level of instrumentation appropriate to the task of measuring the diagnostic parameters. For each marker pre-selected according to these criteria, a matrix is established in which columns represents samples, rows represent markers and each element is a normalized intensity measurement for the expression of that marker according to the relationship: 3 ( t - I ) t

[0041] where I is the intensity measurement.

[0042] It is also possible to set additional boundary conditions to define the optimal portfolios. For example, portfolio size can be limited to a fixed range or number of markers. This can be done either by making data pre-selection criteria more stringent (e.g, 4 8 ( t - MAX n ) ( t + n )

[0043] instead of 5 0.5 ( t - MAX n ) ( t + n )

[0044] ) or by using programming features such as restricting portfolio size. One could, for example, set the boundary condition that the efficient frontier is to be selected from among only the most optimal 10 genes. One could also use all of the genes pre-selected for determining the efficient frontier and then limit the number of genes selected (e.g., no more than 10).

[0045] The process of selecting a portfolio can also include the application of heuristic rules. Preferably, such rules are formulated based on biology and an understanding of the technology used to produce clinical results. More preferably, they are applied to output from the optimization method. For example, the mean variance method of portfolio selection can be applied to microarray data for a number of genes differentially expressed in subjects with breast cancer. Output from the method would be an optimized set of genes that could include some genes that are expressed in peripheral blood as well as in diseased tissue. If samples used in the testing method are obtained from peripheral blood and certain genes differentially expressed in instances of breast cancer could also be differentially expressed in peripheral blood, then a heuristic rule can be applied in which a portfolio is selected from the efficient frontier excluding those that are differentially expressed in peripheral blood. Of course, the rule can be applied prior to the formation of the efficient frontier by, for example, applying the rule during data pre-selection.

[0046] Other heuristic rules can be applied that are not necessarily related to the biology in question. For example, one can apply the rule that only a given percentage of the portfolio can be represented by a particular gene or genes. Commercially available software such as the Wagner Software readily accommodates these types of heuristics. This can be useful, for example, when factors other than accuracy and precision (e.g., anticipated licensing fees) have an impact on the desirability of including one or more genes.

[0047] One method of the invention involves comparing gene expression profiles for various genes (or portfolios) to ascribe prognoses. The gene expression profiles of each of the genes comprising the portfolio are fixed in a medium such as a computer readable medium. This can take a number of forms. For example, a table can be established into which the range of signals (e.g., intensity measurements) indicative of disease/relapse is input. Actual patient data can then be compared to the values in the table to determine whether the patient samples are normal or diseased. In a more sophisticated embodiment, patterns of the expression signals (e.g., flourescent intensity) are recorded digitally or graphically. The gene expression patterns from the gene portfolios used in conjunction with patient samples are then compared to the expression patterns. Pattern comparison software can then be used to determine whether the patient samples have a pattern indicative of recurrence of the disease. Of course, these comparisons can also be used to determine whether the patient is not likely to experience disease recurrence. The expression profiles of the samples are then compared to the portfolio of a control cell. If the sample expression patterns are consistent with the expression pattern for recurrence of a breast cancer then (in the absence of countervailing medical considerations) the patient is treated as one would treat a relapse patient. If the sample expression patterns are consistent with the expression pattern from the normal/control cell then the patient is diagnosed negative for breast cancer.

[0048] Numerous well known methods of pattern recognition are available. The following references provide some examples:

[0049] Weighted Voting:

[0050] Golub, TR., Slonim, DK., Tamaya, P., Huard, C., Gaasenbeek, M., Mesirov, JP., Coller, H., Loh, L., Downing, JR., Caligiuri, Mass., Bloomfield, CD., Lander, ES. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286:531-537, 1999

[0051] Support Vector Machines:

[0052] Su, Al., Welsh, JB., Sapinoso, LM., Kern, SG., Dimitrov, P., Lapp, H., Schultz, PG., Powell, SM., Moskaluk, CA., Frierson, HF. Jr., Hampton, GM. Molecular classification of human carcinomas by use of gene expression signatures. Cancer Research 61:7388-93, 2001

[0053] Ramaswamy, S., Tamayo, P., Rifkin, R., Mukherjee, S., Yeang, CH., Angelo, M., Ladd, C., Reich, M., Latulippe, E., Mesirov, JP., Poggio, T., Gerald, W., Loda, M., Lander, ES., Gould, TR. Multiclass cancer diagnosis using tumor gene expresvion signatures Proceedings of the National Academy of Sciences of the USA 98:15149-15154, 2001

[0054] K-nearest Neighbors:

[0055] Ramaswamy, S., Tamayo, P., Rifkin, R., Mukherjee, S., Yeang, CH., Angelo, M., Ladd, C., Reich, M., Latulippe, E., Mesirov, JP., Poggio, T., Gerald, W., Loda, M., Lander, ES., Gould, TR. Multiclass cancer diagnosis using tumor gene expression signatures Proceedings of the National Academy of Sciences of the USA 98:15149-15154, 2001

[0056] Correlation Coefficients:

[0057] van 't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bemards R, Friend SH. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002 Jan. 31;415(6871):530-6.

[0058] The gene expression profiles of this invention can also be used in conjunction with other non-genetic diagnostic methods useful in cancer diagnosis, prognosis, or treatment monitoring. For example, in some circumstances it is beneficial to combine the diagnostic power of the gene expression based methods described above with data from conventional markers such as serum protein markers. A range of such markers exists including such analytes as Estrogen Receptor (ER) with ER+ results indicating a greater likelihood of recurrence or metastasis. Other markers such as the protein (or peptides) produced by the estrogen regulated gene sequence pLIV1 can be used in this capacity as described in U.S. Pat. No. 5,693,465 (incorporated by reference in this specification). In one such method, blood is periodically taken from a treated patient and then subjected to an enzyme immunoassay for one or more serum markers. When the concentration of the marker(s) suggests the return of tumors or failure of therapy, a sample source amenable to gene expression analysis is taken. Where a suspicious mass exists, a fine needle aspirate is taken and gene expression profiles of cells taken from the mass are then analyzed as described above. Alternatively, tissue samples may be taken from areas adjacent to the tissue from which a tumor was previously removed. This approach can be particularly useful when other testing produces ambiguous results.

[0059] Articles of this invention include representations of the gene expression profiles useful for treating, diagnosing, prognosticating, and otherwise assessing diseases. These profile representations are reduced to a medium that can be automatically read by a machine such as computer readable media (magnetic, optical, and the like). The articles can also include instructions for assessing the gene expression profiles in such media. For example, the articles may comprise a CD ROM having computer instructions for comparing gene expression profiles of the portfolios of genes described above. The articles may also have gene expression profiles digitally recorded therein so that they may be compared with gene expression data from patient samples. Alternatively, the profiles can be recorded in different representational format. A graphical recordation is one such format. Clustering algorithms such as those incorporated in "DISCOVERY" and "INFER" software from Partek, Inc. mentioned above can best assist in the visualization of such data.

[0060] Different types of articles of manufacture according to the invention are media or formatted assays used to reveal gene expression profiles. These can comprise, for example, microarrays in which sequence complements or probes are affixed to a matrix to which the sequences indicative of the genes of interest combine creating a readable determinant of their presence. Alternatively, articles according to the invention can be fashioned into reagent kits for conducting hybridization, amplification, and signal generation indicative of the level of expression of the genes of interest for detecting breast cancer.

[0061] Kits made according to the invention include formatted assays for determining the gene expression profiles. These can include all or some of the materials needed to conduct the assays such as reagents and instructions.

[0062] The invention is further illustrated by the following non-limiting examples.

EXAMPLES

[0063] Genes analyzed according to this invention are typically related to full-length nucleic acid sequences that code for the production of a protein or peptide. One skilled in the art will recognize that identification of full-length sequences is not necessary from an analytical point of view. That is, portions of the sequences or ESTs can be selected according to well-known principles for which probes can be designed to assess gene expression for the corresponding gene.

Example 1

Sample Handling and LCM.

[0064] Fresh frozen tissue samples were collected from patients who had surgery for breast tumors. The samples that were used were from 149 Stage I and II patients (staged according to standard clinical diagnostics and pathology). Clinical outcome of the patients was known. Seventy four of the patients have remained disease-free for more than seven years while seventy five patients had distant metastases within four years. One hundred and three patients were lymph node negative while forty six were lymph node positive.

[0065] The tissues were snap frozen in liquid nitrogen within 20-30 minutes of harvesting, and stored at -80.degree. C. thereafter. For laser capture, the samples were cut (6 .mu.m), and one section was mounted on a glass slide, and the second on film (P.A.L.M.), which had been fixed onto a glass slide (Micro Slides Colorfrost, VWR Scientific, Media, Pa.). The section mounted on a glass slide was after fixed in cold acetone, and stained with Mayer's Haematoxylin (Sigma, St. Louis, Mo.). A pathologist analyzed the samples for diagnosis and grade. The clinical stage was estimated from the accompanying surgical pathology and clinical reports to verify the staging of the tumor. The section mounted on film was after fixed for five minutes in 100% ethanol, counter stained for 1 minute in eosin/100% ethanol (100 .mu.g of Eosin in 100 ml of dehydrated ethanol), quickly soaked once in 100% ethanol to remove the free stain, and air dried for 10 minutes.

[0066] Before use in LCM, the membrane (LPC-MEMBRANE PEN FOIL 1.35 .mu.m No 8100, P.A.L.M. GmbH Mikrolaser Technologie, Bernried, Germany) and slides were pretreated to abolish RNases, and to enhance the attachment of the tissue sample onto the film. Briefly, the slides were washed in DEP H.sub.2O, and the film was washed in RNase AWAY (Molecular Bioproducts, Inc., San Diego, Calif.) and rinsed in DEP H.sub.2O. After attaching the film onto the glass slides, the slides were baked at +120.degree. C. for 8 hours, treated with TI-SAD (Diagnostic Products Corporation, Los Angeles, Calif., 1:50 in DEP H.sub.2O, filtered through cotton wool), and incubated at +37.degree. C. for 30 minutes. Immediately before use, a 10 .mu.l aliquot of RNase inhibitor solution (Rnasin Inhibitor 2500 U=33 U/.mu.l N211A, Promega GmbH, Mannheim, Germany, 0.5 .mu.l in 400 .mu.l of freezing solution, containing 0.15 mol NaCl, 10 mmol Tris pH 8.0, 0.25 mmol dithiothreitol) was spread onto the film, where the tissue sample was to be mounted.

[0067] The tissue sections mounted on film were used for LCM. Approximately 2000 epithelial cells/sample were captured using the PALM Robot-Microbeam technology (P.A.L.M. Mikrolaser Technologie, Carl Zeiss, Inc., Thornwood, N.Y.), coupled into Zeiss Axiovert 135 microscope (Carl Zeiss Jena GmbH, Jena, Germany). The surrounding stroma in the normal mucosa, and the occasional intervening stromal components in cancer samples, were included. The captured cells were put in tubes in 100% ethanol and preserved at -80.degree. C.

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