Patent Grant
8283122
The invention relates generally to selections of genes expressed in a patient with neuroblastoma that function to characterize the neuroblastoma, and methods of using the same for predicting the outcome of and for targeting the therapy of neuroblastoma. The invention also relates generally to the use of supervised pattern recognition methods to predict the outcome of patients with neuroblastoma. More specifically, the invention relates to the use of supervised pattern recognition methods, such as artificial neural networks for the prognosis of patients with neuroblastoma using high dimensional data, such as gene expression profiling data.
Diagnosis and/or prognosis of disease is based on a myriad of factors, both objective and subjective, including but not limited to symptoms, laboratory test values, demographic factors and environmental factors. Diagnosis and/or prognosis relies on a clinician such as a physician or a veterinarian being able to identify and evaluate the relevant factors. Often this task can be difficult, and becomes exceedingly more so as the number of factors to be considered increases.
An example of a disease whose diagnosis or prognosis is difficult is cancer. Cancer may be diagnosed or prognosis developed on the basis of clinical presentation, routine histology, immunohistochemistry and electron microscopy. However, the histological appearance may not reveal the genetic aberrations or underlying biologic processes that contribute to the malignancy. Monitoring global gene expression levels using DNA microarrays could provide an additional tool for elucidating tumor biology as well as the potential for molecular diagnostic classification of cancers. Several studies have demonstrated that gene expression profiling using DNA microarrays is able to classify tumors with a high accuracy, and discover new cancer classes.
In clinical practice, several techniques are used for diagnosis or prognosis, including immunohistochemistry, cytogenetics, interphase fluorescence in situ hybridization and reverse transcription (RT)-PCR. Immunohistochemistry allows the detection of protein expression, but it can only examine one protein at a time. Molecular techniques such as RT-PCR are used increasingly for diagnostic confirmation following the discovery of tumor-specific translocations such as EWS-FLI1; t(11;22)(q24;q12) in EWS, and the PAX3-FKHR; t(2;13)(q35;q14) in alveolar rhabdomyosarcoma (ARMS). However, molecular markers do not always provide a definitive diagnosis or prognosis, as on occasion there is failure to detect the classical translocations, due to either technical difficulties or the presence of variant translocations.
DNA microarray technology is a recently developed high throughput technology for monitoring gene expression at the transcription level. Its use is akin to performing tens of thousands of northern blots simultaneously, and has the potential for parallel integration of the expression levels of an entire genome. A DNA microarray includes DNA probes immobilized on a solid support such as a glass microscope slide. The DNA probes can be double stranded cDNA or short (25 mers) or long (50-70 mers) oligonucleotides of known sequences. An ideal DNA microarray should be able to interrogate all of the genes expressed in an organism.
In DNA microarrays using cDNA, the probes are PCR amplified from plasmid cDNA clones that have been purified and can be robotically printed onto coated glass slides. DNA microarrays using oligonucleotides have an advantage over cDNA microarrays because physical clones are not necessary. The oligonucleotides can either be previously synthesized and printed on glass slides, or can be synthesized directly on the surface of silicon or glass slides. Several print-ready oligonucleotide (60-70 mers) sets are commercially available for human, mouse and other organisms (http://www.cgen.com, http://www.operon.com).
Another technique for fabricating oligonucleotides microarrays chemically synthesizes the oligonucleotides (25 mers) on a silicon surface using photolithography techniques. (Affymetrix Inc., Santa Clara, Calif.). Originally such arrays were designed to detect single-nucleotide mutations, but now have applications for gene expression profiling studies. Yet another technique delivers single nucleic acids, which ultimately form longer oligonucleotides (60 mers), by ink-jet onto glass surfaces.
One method of utilizing gene expression data from microarrays is given by Tusher et al., PNAS 98(9) p. 5116-21, April, 2001. The method of Tusher et al. is a statistical method titled Significance Analysis of Microarrays (“SAM”). The general approach in SAM is based on commonly used statistical tests, t-tests specifically, to find genes that discriminate between two classes in a gene-by-gene fashion. SAM uses replication of experiments to assign a significance to the discriminating genes in terms of a false discover rate. SAM therefore offers a method of choosing particular genes from a set of gene expression data, but does not offer a diagnosis based on those genes.
Gene-expression profiling using DNA microarrays may permit a simultaneous analysis of multiple markers, and can be used for example to categorize cancers into subgroups or provide other information concerning the relationship of the gene expression profile and the disease state. The only limitation associated with the use of DNA microarrays is the vast amount of data generated thereby. A method that would allow for the easy and automated use of DNA microarray data in disease diagnosis or prognosis is therefore desirable. Therefore, there remains a need for a method of using gene expression data to diagnose, predict, or prognosticate about a disease condition.
In accordance with one embodiment of the invention, there is provided a selection of genes, expressed in a patient with neuroblastoma, that functions to predict the outcome of the patient when the expression of a gene selection from the cancer cell is compared to the expression of an identical selection of genes from a noncancerous cell or an identical selection of genes from a cancer cell from a patient with a good outcome and/or porr outcome. Devices for carrying out the above methods of the invention are also included within the scope of the invention.
Another embodiment of the invention includes a method of targeting a product of at least one of the genes in Table 2 that includes identifying a therapeutic agent. Another embodiment of the invention includes a method of targeting a product of at least one of the genes in Table 3 that includes identifying a therapeutic agent having an effect on said gene product.
Another embodiment of the invention provides a method of predicting, and/or prognosticating about a disease including obtaining experimental data, wherein the experimental data includes high dimensional data, filtering noise from the data, reducing the dimensionality of the data by using one or more methods of analysis, training a supervised pattern recognition and/or classification method, ranking individual data from the overall data based on the relevance of the individual data to the diagnosis, prediction, prognosis or classification, choosing multiple individual data members, wherein the choice is based on the relative ranking of the individual data, and using the chosen data to determine if an unknown set of experimental data indicates a particular disease prognosis, or prediction. Methods of the invention may utilize linear methods, and preferably, methods of the invention use nonlinear (with hidden layers) networks.
Methods of the invention can be utilized in a number of different applications. For example, diagnostic chips can be fabricated based on the identification of the diagnostic or prognostic genes. Such chips would be very useful in clinical settings, as it would allow clinicians to diagnose cancers or provide a prognosis from a relatively small set of genes instead of purchasing entire gene sets.
Methods of the invention can also be used to define which patients with neuroblastoma are likely to respond to treatment. This would allow a physician to intensify treatment for those with a more negative prognosis based on their gene expression profiles as detected utilizing a method of the invention. One aspect of the invention includes a method of predicting the outcome of a patient having neuroblastoma comprising detecting an increase in expression of at least one gene selected from the group consisting of DLK1, SLIT3, PRSS3, and mixtures thereof in a neuroblastoma cell from the patient; wherein an increase in expression of at least one of the genes is indicative of poor outcome of the subject.
Another method of predicting the outcome of patient having neuroblastoma comprises detecting a change in expression at least one gene or polynucleotide selected from the group consisting of DLK1, PRSS3, ARC, SLIT3, JPH1, ARH1, CNR1, ROBO2, BTBD3, KLRC3, Hs. 434957, Hs. 346735, Hs. 120591, Hs. 196008, Hs. 124776, Hs. 119947, Hs. 349094, and mixtures thereof, in a neuroblastoma cell from the patient, wherein the expression profile of the gene or polynucleotide is indicative of the outcome of the patient.
In some embodiments of the methods, the expression of at least one of the genes or polynucleotides selected from the group consisting of MYCN, DLK1, PRSS3, ARC, SLIT3, JPH1, Hs. 434957, Hs. 346735, Hs. 120591, and mixtures thereof, is upregulated, indicating the outcome of the patient is poor. In other embodiments, the expression of at least one gene or polynucleotide selected from the group consisting of CD44, ARH1, CNR1, ROBO2, BTBD3, KLRC3, Hs. 196008, Hs. 124776, Hs. 119947, Hs. 349094, and mixtures thereof, is downregulated, indicating the outcome of the patient is poor.
In some embodiments all of the genes or polynucleotides of Table 2 are analyzed. In other embodiments at least one or all of the genes of Table 3 are analyzed.
Another aspect of the invention includes a set or selection of genes or polynucleotides comprising at least two genes or polynucleotides selected from the consisting of DLK1, PRSS3, ARC, SLIT3, JPH1, ARH1, CNR1, ROBO2, BTBD3, KLRC3, Hs. 434957, Hs. 346735, Hs. 120591, Hs. 196008, Hs. 124776, Hs. 119947, Hs. 349094, and mixtures thereof, or the complements thereof. The set of genes may further comprise MYCN and/or CD44.
Methods of the invention can also be used for identifying pharmaceutical targets. Methods of the invention can be used to determine which genes to target in efforts to target specific diseases. Such methods include a method of identifying an agent that can modulate the expression or activity of at least one gene or polynucleotide comprising measuring expression or activity of at least one polynucleotide or gene selected from the group consisting of DLK1, PRSS3, ARC, SLIT3, JPH1, ARH1, CNR1, ROBO2, BTBD3, KLRC3, Hs. 434957, Hs. 346735, Hs. 120591, Hs. 196008, Hs. 124776, Hs. 119947, Hs. 349094, and mixtures thereof, in the presence or absence of a candidate agent; and identifying the candidate agent that inhibits or increases expression or activity of the polynucleotide or gene. Another method comprises measuring expression or activity of at least one gene or polynucleotide selected from the group consisting of DLK1, PRSS3, ARC, SLIT3, JPH1, Hs. 434957, Hs. 346735, Hs. 120591, and mixtures thereof, in the presence or absence of the candidate antagonist; determining whether the candidate antagonist inhibits expression or activity of at least one of the polynucleotides or genes. In another embodiment, a method of identifying an agonist comprises measuring expression or activity of at least one polynucleotide or gene selected from the group consisting of ARH1, CNR1, ROBO2, BTBD3, KLRC3, Hs. 196008, Hs. 124776, Hs. 119947, Hs. 349094, and mixtures thereof, in the presence and absence of the candidate agonist; and determining whether the candidate agonist increases expression and/or activity of the polynucleotide or gene.
Another aspect provides for kits, devices for implementing the methods of the invention.
Methods of the invention can also be utilized as a research tool for analyzing all types of gene expression data including cDNA and oligonucleotide microarray data. Methods of the invention can also be utilized to identify and rank, by importance, the genes that contribute to a prognosis. A minimal set of genes that can correctly predict clinical outcomes can also be determined using methods of the invention. Methods of the invention identify the most significant genes, by calculating the sensitivity of the classification to a change in the expression level of each gene. A list of genes, ranked by their significance to the classification, is produced thereby. This allows for cost effective fabrication of subarrays for use in predicting clinical outcomes.
The invention is a method of classifying, diagnosing, prognosticating about, and predicting disease conditions or other biological states using supervised pattern recognition methods to analyze high dimensional data.
One aspect of the invention is illustrated in
Supervised pattern recognition methods are useful, interalia, to analyze gene expression profiles useful to diagnose and/or provide prognosis of disease. One aspect of the invention, provides for a method for predicting the outcome of a patient or subject having neuroblastoma comprising detecting an increase in expression of at least one gene in a neuroblastoma cell from the patient selected from the group consisting of DLK1, PRSS3, SL1T3 and mixtures thereof, wherein the increase in expression is indicative of poor outcome. In some embodiments, an increase in expression is determined by detecting mRNA expression as compared to a nonneuroblastoma cell, for example, from a mixture of other types of cancer cells. In other embodiments, expression is compared to expression in a cell from a neuroblastoma tumor from a subject with a good outcome and/or a poor outcome. In some embodiments, a control may also be employed to detect the expression of 5-10 housekeeping genes. The invention also includes another method for predicting the outcome of a patient or subject having neuroblastoma comprising detecting a change in expression of at least one gene or polynucleotide or all genes or polynucleotides selected from the group consisting of DLK1, PRSS3, ARC, SLIT3, JPH1, ARH1, CNR1, ROBO2, BTBD3, KLRC3, Hs. 434957, Hs. 346735, Hs. 120591, Hs. 196008, Hs. 124776, Hs. 119947, Hs. 349094, and mixtures thereof, in a neuroblastoma cell from the patient, wherein the expression profile or change in expression of the gene or polynucleotide is indicative of the outcome of the patient. In some embodiments, a change in expression is determined as compared to a nonneuroblastoma cell or as compared to expression in a cell from a neuroblastoma tumor from a subject with a good outcome and/or a poor outcome.
In some embodiments, the methods further comprise detecting the expression of MYCN and/or CD44. In some embodiments, at least one of the genes or polynucleotides selected from the group consisting of DLK1, PRSS3, ARC, SLIT3, JPH1, Hs. 434957, Hs. 346735, Hs. 120591 and mixtures thereof, is upregulated indicating that the outcome of the patient is poor. In other embodiments, at least one gene or polynucleotide selected from the group consisting of ARH1, CNR1, ROBO2, BTBD3, KLRC3, Hs. 196008, Hs. 119947, Hs. 124776, Hs. 349094, and mixtures thereof, is downregulated indicating that the outcome of the patient is poor.
Another aspect of the invention involves a method of targeting a gene or polynucleotide for treatment for neuroblastoma. A method comprises identifying an antagonist or agonist of at least one gene or polynucleotide for which a change in expression is correlated with poor outcome in a patient or subject having neuroblastoma. In some embodiments, the gene or polynucleotide is upregulated in a tumor cell and is associated with poor outcome. If a gene is upregulated, a method comprises identifying an antagonist of the gene or polynucleotide. A gene or polynucleotide that is upregulated comprises or is selected from the group consisting of DLK1, PRSS3, SLIT3, and mixtures thereof. In other embodiments, a gene or polynucleotide selected from the group consisting of DLK1, PRSS3, ARC, SLIT3, JPH1, and mixtures thereof, is upregulated indicating a poor outcome. A method comprises identifying an antagonist of at least one gene or polynucleotide upregulated in neuroblastoma cell comprising measuring expression or activity of at least one gene or polynucleotide selected from the group consisting of DLK1, PRSS3, ARC, SLIT3, JPH1, Hs. 434957, Hs. 346735, Hs. 120591, and mixtures thereof in the presence or absence of a candidate agent; and identifying the candidate agent that inhibits expression or activity of at least one of the genes.
In some embodiments, at least one gene or polynucleotide is downregulated and correlated with poor outcome of a patient having neuroblastoma. When a gene or polynucleotide is downregulated, a method comprises identifying an agonist of a gene or polynucleotide downregulated in a neuroblastoma cell comprising measuring expression or activity of at least one gene or polynucleotide selected from the group consisting of ARH1, CNR1, ROBO2, BTBD3, KLRC3, Hs. 196008, Hs. 124776, Hs. 119947, Hs. 349094, and mixtures thereof, in the presence and absence of a candidate agent, identifying as an agonist the candidate agonist that increases the expression or activity of the gene or polynucleotide.
Another aspect of the invention provides a set or selection of genes, kits, and devices for carrying out the methods of the invention.
A. Methods of Using Supervised Pattern Recognition to Analyze High Dimensional Data for Prognosis and Identifying Therapeutic Targets.
An embodiment of the invention provides a method of predicting, and/or prognosticating about a disease comprising obtaining experimental data, wherein the experimental data includes high dimensional data, filtering noise from the data, reducing the dimensionality of the data by using one or more methods of analysis, training a supervised pattern recognition and/or classification method, ranking individual data from the overall data based on the relevance of the individual data to the diagnosis, prediction, prognosis or classification, choosing multiple individual data members, wherein the choice is based on the relative ranking of the individual data, and using the chosen data to determine if an unknown set of experimental data indicates a particular disease prognosis, or prediction.
Obtaining Experimental Data
The first step in a method of the invention is to obtain experimental data. Experimental data utilized in methods of the invention is high dimensional data. High dimensional data is data that has at least hundreds of individual pieces of information associated with one sample. An example of high dimensional data useful in methods of the invention is gene expression data. Gene expression data is high dimensional data because each sample has a large number of gene expression levels. Generally speaking, gene expression data generally has thousands of gene expression levels for each sample. Other examples of high dimensional data useful in the invention include but are not limited to protein arrays and protein chips, cell array based expression analysis, analysis of patterns of single nucleotide polymorphisms in disease conditions, and comparative genomic hybridization on metaphase, BAC genomic, cDNA and oligonucleotide arrays.
Preferably, the gene expression data is obtained through use of DNA microarray technology. DNA microarrays are preferred as a source of data because they generally offer a more complete picture of the interactions of a large number of genes with a limited number, or even one experiment. An example of a general description of how gene expression data can be obtained by using cDNA microarray technology is given below.
DNA microarrays, although a relatively new technology, have already been saddled with a number of different names, biochip, DNA chip, gene chip, genome chip, cDNA microarray, and gene array. The use of any of these terms herein refers generally to DNA microarrays. The underlying principle of DNA microarrays is base pairing or hybridization i.e., A-T and G-C for DNA, and A-U and G-C for RNA.
DNA microarrays provide a medium for matching known and unknown DNA samples based on the base pairings given above. DNA microarrays can either be fabricated by high-speed robotics or can be fabricated in a laboratory setting. They are generally patterned on glass, but can also be fabricated on nylon substrates. Microarrays generally have sample spot sizes of less than 200 μm diameter, and generally contain thousands of DNA spots on one microarray.
One method of fabricating cDNA microarrays begins by first producing gene-specific DNA by polymerase chain reaction (PCR) amplification of purified template plasmid DNAs from cloned expressed sequence tags (ESTs). The PCR product is then purified, resuspended and printed onto a substrate. DNA microarrays are also commercially available from a number of sources, including but not limited to Affymetrix, Inc. (Santa Clara, Calif.), Agilent Technologies (Palo Alto, Calif.), and Research Genetics (Huntsville, Ala.).
One general procedure for a cDNA microarray experiment begins by preparing DNA samples and arraying them (either with an arraying robot, or by hand), to form a DNA microarray. Next, the RNA samples are extracted from the cells of interest, purified, reverse transcribed into cDNA and differentially fluorescently labeled to create probes. Then, the fluorescently labeled cDNA probes are hybridized to the cDNA microarray. If a probe contains a cDNA whose sequence is complementary to the DNA on a given spot, the cDNA probe will hybridize to that spot. After the cDNA probes are hybridized to the array, and any loose probe has been washed away, the microarray is imaged to determine how much of each probe is hybridized to each spot. This indicates how much of each gene from the microarray is expressed in the two samples. If the amount of starting material is small, for example from needle biopsies, the RNA can first be subject to amplification by modified Eberwine methods as described by Gelder et al. (Amplified RNA synthesized from limited quantities of heterogeneous cDNA. (Proc. Natl. Acad. Sci. USA 1990 March; 87(5):1663-7).) The experimental high dimensional data, preferably obtained from gene expression experiments, preferably performed using cDNA microarrays, is then further analyzed by a method of the invention.
Filtering the Data
The next step in a method of the invention is filtering the data 102 to remove individual pieces of data that are deemed undesirable. This filtering step functions to eliminate weak and/or problematic data from further use in the method. Accomplishment of the step of filtering depends greatly on the type of high dimensional data utilized. Any method known to those of ordinary skill in the art can be used to eliminate data determined to be undesirable.
One basis for carrying out this filtering, if a DNA microarray is being utilized for obtaining the high dimensional data, is the intensity of the fluorescence from the individual microarray spots. This basis of omitting data is based on failure or error in the imaging of the specific spots. A preferred method of performing initial data filtering on cDNA microarray data to remove those spots where imaging was a problem is to utilize the intensity of the various spots and utilize only those spots that have an intensity over a certain threshold value. Other methods of filtering DNA microarray data include but are not limited to eliminating spots in which the number of pixels represented is less than a threshold defined by the user, eliminating spots in which the standard deviation of the signal on the spots is too large, as defined by the user, eliminating spots in which the background intensity of a single spot is too high, or any combination thereof. In addition quality values based on intensity, can be assigned to each spot, standard deviation of intensity, background and/or size of each spot, then a spot could be eliminated if its quality value falls below a threshold as defined by the user.
Reducing the Dimensionality of the Data
The next step in methods of the invention is reducing the dimensionality of the data 103. The number of samples needed to calibrate a classifier with good predictive ability, depends critically on the number of features used in the design of the classifier. In the case of high-dimensional data, such as microarray data, where the number of samples is much smaller than the number of individual pieces of data there exists a large risk of over-fitting. There are two different solutions to this problem. First, the calibration process can be carefully monitored using a cross-validation scheme to avoid over-fitting (see below). Second, the dimension of the data can be reduced, either by using a dimensional reduction algorithm or by selecting a smaller set of data for input to the supervised pattern recognition method. Dimensionality reduction allows the number of parameters representing each sample to be reduced. This allows for the design of a classifier that has less risk of over-fitting, thereby increasing its predictive ability. Examples of methods of reducing the dimensionality of the data include but are not limited to principal component analysis (PCA), weighted gene analysis, t-test, rank based Wilcoxon or Mann-Whitney tests, signal-to-noise statistic, Fisher's discriminant analysis, or ANOVA tests. In a preferred embodiment of the invention, PCA is used to reduce the dimensionality of the data.
In the case of PCA on gene expression data, reduction of the dimensionality is achieved by rotating gene expression space, such that the variance of the expression is dominated by as few linear combinations of genes as possible. Even though the formal dimension of the problem is given by the number of individual data points, the effective dimension is just one less than the number of samples. Hence the eigenvalue problem underlying PCA can be solved without diagonalizing 2308×2308 matrices by using singular value decomposition. Thus each sample is represented by 88 numbers, which are the results of projections of the data using the PCA eigenvectors.
A potential risk when using PCA on relatively few samples is that components might be singled out due to strong noise in the data. It could be argued that the outputs (labels) should be included in the dimensional reduction, using e.g. the Partial Least Squares (PLS) algorithm, in order to promote components with strong relevance for the output. However, based on explorations with similar data sets, this is not optimal; bias is introduced and implicitly “over-trains” from the outset by including the outputs in the procedure.
Setting Up a Validation Method for the Supervised Pattern Recognition Method
Once the data has been filtered 102 and its dimensionality reduced 103, a validation method is set up for monitoring and validating the training of the supervised pattern recognition method 115. Any method commonly used by those of skill in the art for validating the training of a supervised pattern recognition method can be used.
In one embodiment, the first step in setting up a validation method is to randomly divide the data into eight groups of data. (See
In an embodiment, the 8-fold cross validation procedure (steps 104 through 110) is performed on all of the samples. A data group having a known number of samples is given as an example. The known (labeled) number samples are randomly shuffled 104 and split into equally 8 sized groups. The supervised pattern recognition method 111 is then calibrated as discussed below using the training group 109. The eighth group, a validation group 110, is reserved for testing predictions. Comparisons with the known answers refer to the results from the validation group 110 (i.e. when using a model, the samples used for training the model are never used in predictions). This procedure is repeated 8 times, each time with a different group used for validation. The random shuffling 104 is done about 100 to 10000 times, preferably 100 times. For each shuffling, one supervised pattern recognition method 111 model is generated. Thus, in this embodiment, in total, each sample belongs to validation group 110, 100 times and 800 supervised pattern recognition methods 111 have been calibrated. Other cross validation schemes can be designed and readily utilized.
Training the Supervised Pattern Recognition Method
The supervised pattern recognition method 111 is then trained. The specific method of training the supervised pattern recognition method 111 is dependent on the specific form that the supervised pattern recognition method 111 takes. The choice of the supervised pattern recognition method 111 and the training thereof is well within one of skill in the art, having read this specification.
One example of a supervised pattern recognition method is an artificial neural network (ANN). ANNs are computer-based algorithms that are modeled on the structure and behavior of neurons in the human brain and can be trained to recognize and categorize complex patterns. Pattern recognition is achieved by adjusting parameters of the ANN by a process of error minimization through learning from experience. They can be calibrated using any type of input data, such as gene-expression levels generated by cDNA microarrays, and the output can be grouped into any given number of categories. ANNs have been recently applied to clinical problems such as diagnosing myocardial infarcts and arrhythmias from electrocardiograms and interpreting radiographs and magnetic resonance images.
In some embodiments where an artificial neural network (ANN) is employed as the supervised pattern recognition method 111, calibration is preferably performed using MATLAB (The Mathworks, Natick, Mass.), preferably, the resilient backpropagation learning algorithm is used with initial delta=0.07, max delta=50, delta increase=1.2, and the delta decrease=0.5. The calibration is performed using a training set and it is monitored both for the training set and a validation set, which is not subject to calibration (see below). The weight values are updated and the calibration is terminated after 100 passes (epochs) through the entire training set. In one embodiment of a method of the invention, the resulting parameters for the completed training of a supervised pattern recognition method 111 defines a “model”.
The possibility of using all the PCA components as inputs followed by a subsequent pruning of weights to avoid “over-fitting” is also one alternative.
Verifying the Outcome of the Supervised Pattern Recognition Method
Once the supervised pattern recognition method 111 is trained, the next step is to determine whether the validation of the supervised pattern recognition method 111 is successful 112. This step determines whether the supervised pattern recognition method 111 adequately predicted the results for the validation data set 110 using any number of performance measurements and error measurements.
Any method known to those of ordinary skill in the art can be utilized to evaluate the performance of the training of the supervised pattern recognition method 111. Generally speaking, the performance is evaluated by comparison with some predetermined level of correct predictions that the user has determined is acceptable.
If the performance of the supervised pattern recognition method 111 is sufficiently poor, and a measure of error is greater than an allowable threshold, the processing may return to module 103 where the dimensionality of the data is reduced in a different manner and the entire training and validation process is repeated.
Ranking the Data
Once module 112 determines that the network 111 has been adequately trained, the processing proceeds to rank the output of the supervised pattern recognition method 113.
The outcome of the supervised pattern recognition method 111 can be looked at either independently or in a compiled form. Each supervised pattern recognition method 111 gives a number between 0 (good outcome) and 1 (poor outcome) as an output for each sample. If the predictions are viewed independently, the maximal output is forced to 1 while the other outputs are forced to 0. Then it is determined how many of the predictions are correct. If the predictions are viewed in a compiled form, all of the predicted outputs are considered in their numerical form, after which all of the numbers are averaged and the resulting average is forced to 0 or 1. In one embodiment of the method, the predictions, as compiled, are used to classify samples.
In one embodiment, each sample is classified as belonging to the good or poor outcome corresponding to the largest average in the compilation. Optionally, in addition, it may be desirable to be able to reject the second largest vote, as well as test samples that do not fall within a distance dc from a sample to the ideal vote for each outcome type is defined as
where c is a outcome type, oi is the average from the compilation for outcome type i, and δi,c is unity if i corresponds to outcome type c and zero otherwise. The distance is normalized such that the distance between two ideal samples belonging to different outcome types is unity. Optionally, based on the validation group, an empirical probability distribution of its distances is generated for each outcome type.
Optionally, empirical probability distributions may be built using each supervised pattern recognition method 111 independently (not the average from the compilation). Thus, the number of entries in each distribution is given by 100 multiplied by the number of samples belonging to the outcome type. For a given test sample, the possible classifications based on these probability distributions can be rejected. This means that for each outcome type a cutoff distance from an ideal sample is defined, within which, based on the validation samples, a sample of this category is expected to be. The distance given by the 95th percentile of the probability distribution is preferably chosen as a cutoff, which means that if a sample is outside of this cutoff distance it cannot confidently provide a prognosis. It should be noted that the classification as well as the extraction of important genes (see below) converges using less than 100 supervised pattern recognition method 111 models. 800 supervised pattern recognition method 111 models are preferred is because sufficient statistics exist for these empirical probability distributions.
For each output category the sensitivity and specificity of the prognosis may be calculated (see Table 1 below). Table 1 gives sensitivity, specificity for both validation and test samples. Both the sensitivity and the specificity are very high for all categories. It should be noted, that they generally depend on the kind of samples that are used as test samples.
The Receiver Operator Characteristic (ROC) curve area is identical to another more intuitive and easily computed measure of discrimination: the probability that in a randomly chosen pair of samples, one belonging to and one not belonging to the outcome category, the one belonging to the category is the one with the closest distance to the ideal for that particular category. Since the ROC curve areas are unity for all output categories, it is possible to define cutoff distances such that both the sensitivity and the specificity are 100% for all outcomes. However, based on the training and validation groups it is difficult to motivate such cutoff distances.
The next step in a method in accordance with the invention is to actually rank the data. This step can in principle be done in two ways; (1) model-independent and (2) model-dependent analysis respectively. Due to the relative small number of samples, the model-dependent analysis is preferred when using ANN models.
The sensitivity (S) of the outputs (o) with respect to any of the input variables (xk) is defined as:
where Ns is the number of samples and No is the number of outputs (4). The procedure for computing Sk involves a committee of models. In addition we have defined a sensitivity for each output i (Si), which is analogous to Eq. (2) but without the sum over outputs. Furthermore, a sensitivity can be defined for each sample (or subsets of samples) individually, by only using that sample(s) in the sum over samples in Eq. (2). For all these sensitivities the sign of the sensitivity has also been defined. The sign signals whether the largest contribution to the sensitivity stems from positive or negative terms. A positive sign implies that increased expression of the gene increases the possibility that the sample belongs to the poor outcome type, (P+ means higher expression in the death or poor outcome group) while a negative sign means decreased expression of the gene is associated with poor outcome (P− means decreased expression in poor outcome group).
In one embodiment, once ranked, a relevant set of data can be selected module 114 by minimizing the amount of data to be used to classify and identify a particular disease. In one embodiment, a predetermined amount of data having the highest ranking are selected. Of course, other selection methods may be employed without deviating from the spirit and scope of the present invention as recited in the attached claims.
Implementation of Methods of the Invention
In embodiments of the method in which the supervised pattern recognition method 111 is an artificial neural network, a general purpose computing system as depicted in
Those of ordinary skill in the art will appreciate that the ANN processing system 200 may include many more components than those shown in
The ANN processing system 200 also includes processing unit 212, video display adapter 214, and a mass memory, all connected via bus 222. The mass memory generally includes RAM 216, ROM 232, and one or more permanent mass storage devices, such as hard disk drive 228, a tape drive, CD-ROM/DVD-ROM drive 226, and/or a floppy disk drive. The mass memory stores operating system 220 for controlling the operation of ANN processing system 200. It will be appreciated that this component may comprise a general purpose server operating system as is known to those of ordinary skill in the art, such as UNIX, LINUX, MAC OS®, or Microsoft WINDOWS NT®. Basic input/output system (“BIOS”) 218 is also provided for controlling the low-level operation of ANN processing system 200.
The mass memory as described above illustrates another type of computer-readable media, namely computer storage media. Computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules or other data. Examples of computer storage media include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computing device.
The mass memory also stores program code and data for providing an ANN processing and network development. More specifically, the mass memory stores applications including ANN processing module 230, programs 234, and other applications 236. ANN processing module 230 includes computer executable instructions which, when executed by ANN processing system 200, performs the logic described above.
The ANN processing system 200 also comprises input/output interface 224 for communicating with external devices, such as a mouse, keyboard, scanner, or other input devices not shown in
A set of processing modules making up an embodiment of an artificial neural network according to the invention is illustrated in
In all of these competitive learning networks, the networks are typically presented a set of input data that possesses a corresponding set of results data. From these data values, the network of nodes “learns” a relationship between the input data and its corresponding results data. In this process, the probability distribution relationship is estimated using the multi-dimensional network of nodes. This relationship is represented within a set of artificial neural network coefficients for a particular topology of nodes.
One skilled in the art will recognize that competitive learning networks include a nearly infinite number of network topologies that may be used to represent a particular probability distribution relationship without deviating from the spirit and scope of the present invention as recited within the attached claims. In addition, artificial neural networks may utilize various well-known algorithm architectures, including hard-competitive learning (i.e. “winner-take-all” learning), soft competitive learning without a fixed network dimensionality, and soft competitive learning with a fixed network dimensionality, to specify an artificial neural network according to the invention as recited within the attached claims. Each of these algorithm architectures represents the same probability distribution relationship; however each of the various algorithm architectures better optimize corresponding processing parameters, which are often mutually exclusive with each other. These parameters include error minimization or the minimization of an expected quantization error, entropy maximization for the reference vectors used within a network, and topology-preserving or feature mapping architectures that attempt to map high-dimensional inputs signals onto lower-dimensional structures in a manner that attempts to preserve similar relationships found within the original data within the post-mapping data. As such, any of these types of algorithm architectures may be used to construct an artificial neural network without deviating from the spirit and scope of the present invention as recited within the attached claims.
Now referring to
The data processing within the learning module 311 may proceed in a batch processing fashion in which all of the vectors within the experimental data 315—experimental result 316 data set are processed at a single time. In such a process, the experimental data 315—experimental result 316 data set is received by the input module 312, processed by the training module 313, and the generated coefficients are placed within the database 313 by the storage module 314. Alternatively, the experimental data 315—experimental result 316 data set may be processed as a sequence of smaller data sets in which the experimental data 315—experimental result 316 data set data values are generated at different times. In such a process, the training module 313 uses the previously stored coefficients retrieved by the storage module along with a new small data set provided by the input module 312 to generate an updated set of coefficients. These updated coefficients may be once again stored within the database 313 for use at a later time.
Once an artificial neural network 301 has been trained, the prediction module 321 may be used to predict, or classify, a particular test data value 325. The prediction module 321 includes a data prediction input module 322, an ANN prediction module 323, and an ANN curve slope module 324. The data prediction input module 322 receives the input test data generated as described above for use in the prediction module. The ANN prediction module 323 receives and utilizes the network coefficient values for the neural network from the ANN coefficient database 313 to predict the possible result for the probability distribution relationship specified within the neural network. This output value is used by the ANN curve slope module 324 to determine all possible values for a given gene, in the manner discussed above, to determine a curve slope value. This slope value is then output for later use in ranking and classifying the individual genes used to determine the presence, absence or prognosis of a disease.
The embodiments described herein are implemented as logical operations performed by a computer. The logical operations of these various embodiments of the present invention are implemented (1) as a sequence of computer implemented steps or program modules running on a computing system and/or (2) as interconnected machine modules or hardware logic within the computing system. The implementation is a matter of choice dependent on the performance requirements of the computing system implementing the invention. Accordingly, the logical operations making up the embodiments of the invention described herein can be variously referred to as operations, steps, or modules.
While the above embodiments of the invention describe the use of an artificial neural network to identify relevant genes associated with diseases and use the identified genes to classify and identify diseases, one skilled in the are will recognize that the use of the processing system discussed above are merely example embodiments of the invention. As long as experimental data is used to self-train a processing system using competitive learning processing, the present invention to would be useable in other data processing-systems. It is to be understood that other embodiments may be utilized and operational changes may be made without departing from the scope of the present invention as recited in the attached claims.
Prediction of Clinical Outcome Using Gene Expression Profiling and ANN
In an embodiment, a prognostic profile and prediction of clinical outcome of patients having neuroblastoma can be made using gene expression data and a method of analyzing the data using ANNs as described herein.
The high dimensional data is obtained from neuroblastoma tumor cells. In some embodiments, total mRNA from neuroblastoma cells is obtained and expression levels are determined using commercially available sequence verified cDNA libraries comprising about 42,578 cDNA clones representing 25,933 unique genes (Unigene clusters: 13,606 known genes and 12,327 unknown expressed sequence tags).
In an embodiment, gene expression ratio information obtained from neuroblastoma cells and reference RNA on each microarray can be normalized using a pin based normalization method. Quality of each individual cDNA can be evaluated by Chen et al. (Bioinformatics, 18:207 (2002)). Spots with an average quality across all of the samples can be excluded.
In an embodiment, principal component analysis can be used to reduce the dimensionality of the expression data to the principal components as inputs for artificial neural networks. A feed forward resilient back propagation multilayer perceptron artificial neural network (coded in Matlab, The Mathworks, Natick, Mass.) can be used having at least 3 layers: an input layer of the top 10 principal components of the data or the gene expression ratios of each cDNA spot (for the minimized gene set); a hidden layer with 3 nodes; and an output layer generating a committee vote that discriminates between two classes (i.e. good and poor outcome groups). Average artificial neural network committee votes can be used to classify samples and 0.5 can be selected as decision boundary. An ideal vote was 0 for good outcome group (alive) and 1 for poor outcome group (dead).
The artificial neural networks can be trained using an 8 fold cross validation scheme. (See
In an embodiment, to identify the prognostic genes and outcome prediction, a separate ANN analysis is conducted using a gene minimization procedure. The gene minimization procedure involves ranking each of the input clones according to its importance to prediction of ANNs. Increasing numbers of the top ranked clones are used to to train ANNs and the classification error monitored. The minimal number of clones that yielded the minimal classification error is identified and the top ranked clones are used to retrain ANNs and predict the test samples without preforming a principal component analysis.
An analysis of 56 neuroblastoma samples from patients (34 patients alive and 22 deceased as shown in
B. Compositions, Methods, and Devices for Predicting the Clinical Outcome of Patients with Neuroblastoma.
Methods
Neuroblastoma is the most common solid extracranial tumor of childhood and is derived from the sympathetic nervous system. Patients in North America are currently stratified by the Children's Oncology Group into high, intermediate, and low risk based on age, tumor staging, Shimada Histology, MYCN amplification, and DNA ploidy (Brodeur, et al., Neuroblastoma. In: PA Pizzo and DG Poplack, editors, Principles and practice of pediatric oncology, 4th ed. Philadelphia: Lippincott-Raven, pp. 895-937 (2002)). Patients<1 year of age or with lower stage diseases (International Neuroblastoma Staging System stages 1 and 2) usually have better outcome than older patients or those with advanced stage diseases (International Neuroblastoma Staging System stages 3 and 4). Certain consistent cytogenetic changes, including gain of 2p24 and 17q and loss of heterozygosity at 1p36 have been associated with a more aggressive phenotype (Schwab et al., Lancet Oncol., 4:472-480 (2003); Westermann et al., Cancer Lett., 184:127-147 (2002)). The MYCN gene is amplified in ˜22% of all neuroblastoma patients (Brodeur, Nat. Rev. Cancer, 3:203-216 (2003)) and is an independent predictor for poor prognosis, especially for patients>1 year of age. Although other genes, such as TRKA, TRKB, hTERT, BCL-2, caspases, and FYN (Brodeur, Nat. Rev. Cancer, 3:203-216 (2003); Berwanger et al., Cancer Cell, 2:377-386 (2002)) have been associated with neuroblastoma prognosis, they all lack the predictive power of MYCN and are not used currently in clinical practice.
High-risk patients compose ˜50% of all neuroblastoma cases; however, despite significant improvement in the therapy of neuroblastoma using neoadjuvant chemotherapy, surgery, and radiation, the death rate for these patients remains at 70% (Pearson et al., In: G M Brodeur, T. Sawada, Y. Tsuchida, P A Voute, editors. Neuroblastoma, 1st ed. Amsterdam, The Netherlands: Elsevier Science, p. 555 (2000)). Although the Children's Oncology Group risk stratification has been carefully developed to take into account the above risk factors, it is primarily used to guide therapy and does not predict which individual patients will be cured from the disease.
Gene expression profiles from cDNA microarrays are described herein and are useful to predict the outcome and identify a prognostic gene set in patients with neuroblastoma using artificial neural networks. A prediction of the outcome of the patient having neuroblastoma will assist the physicians in selecting an appropriate treatment regimen. For those patients whose gene expression profile indicates a poor outcome, more aggressive treatment may be warranted. For those patients whose gene expression profile indicates a good outcome, less aggressive treatment may be warranted. The identification of the set of genes useful for prognosis will provide for microarray assays or other clinical assays useful in predicting outcome of patients having neuroblastoma. Once the prognostic profile is identified as described herein, the prediction of outcome may be accomplished without the use of artificial neural network analysis.
One aspect of the invention, provides for a method for predicting the outcome of a patient or subject having neuroblastoma comprising detecting an increase in expression of at least one gene selected from the group consisting of DLK1, PRSS3, SL1T3 and mixtures thereof in a neuroblastoma cell from the patient, wherein the increase in expression is indicative of poor outcome. Optionally, the expression levels of at least one gene or polynucleotide are compared to that of a patient with a good outcome and/or poor outcome. For example, if the expression level is upregulated in comparison to expression levels in a patient with good outcome, then it is likely the patient will have a poor outcome. Poor outcome refers to patient that is likely to die, die in a much shorter time, and/or has died. Good outcome refers to a patient that is still alive and/or is in remission (no progression or relapse) for at least 3 years. In some embodiments, an increase in expression is determined by detecting mRNA expression as compared to a nonneuroblastoma cell or as compared to expression in a cell from a neuroblastoma tumor from a subject with a good and/or poor outcome. Examples of the expression levels of prognostic genes identified herein is shown in
In some embodiments, reference RNA is included in the microassay analysis, such reference RNA can be obtained from a nonneuroblastoma cell such as from a mixture of other type of cell lines. In addition, a control may be included for detecting expression of at least one housekeeping gene, preferably 5-10 housekeeping genes. In some embodiments, an increase in mRNA expression is detected using a microarray, hybridization assay or PCR assays including real time PCR. In other embodiments, expression of at least one of the genes is detected by measuring the concentration of the protein in a biological sample using standard methodologies such as ELISA, immuno PCR, and other like assays.
Multiple clones may provide for detection of any one of the genes identified herein as prognostic for neuroblastoma. See, for example,
In some embodiments, the gene for DLK1 comprises a polynucleotide sequence of Image ID NO: 296815 or Image ID NO: 436121. The DLK1 gene may also comprises a polynucleotide sequence of SEQ ID NO:1. In some embodiments, the gene for SLIT3 comprise a nucleotide sequence of Image ID NO: 450382, or Image ID NO: 192225, or Image ID NO: 2030301. The SLIT3 gene may also comprise a polynucleotide sequence of SEQ ID NO:6. In some embodiments, the PRSS3 gene comprises a polynucleotide sequence of Image ID NO: 1913366. The PRSS3 gene may also comprise a polynucleotide sequence of SEQ ID NO:3.
In some embodiments, DLK1 comprises an amino acid sequence as provided in Accession No. NP—003827 (gI: 21361080) and having a sequence of SEQ ID NO:254. In an embodiment, PRSS3 comprises an amino acid sequence of Accession No. NP—002762 (gi|21536452) having a sequence of SEQ ID NO:255. In an embodiment, SLIT3 comprises an amino acid sequence of Accession No. NP—003053 (gi|11321571) and having a sequence of SEQ ID NO:256. Other secreted polypeptides include PRSS12, GAL, and IL-7. The polypeptides may be useful to develop antibodies or other reagents that may be useful to detect an increase of the polypeptide in a biological sample.
In some embodiments of the methods, the expression of at least two of the genes, preferably at least three of the genes is detected. In a further embodiment, the method may further comprise detecting an up-regulation of expression of MYCN and/or a downregulation of expression of CD44. In some embodiments, the gene for MYCN comprises the sequence of Image ID NO: 41565. The gene for MYCN may also comprise a polynucleotide sequence of SEQ ID NO:16. In some embodiments, the gene for CD44 comprises the sequence of Image ID NO: 1967589. The gene for CD44 may also comprise a polynucleotide sequence of SEQ ID NO:12.
The invention also includes another method for predicting the outcome of a patient or subject having neuroblastoma comprising detecting a change in expression of at least one gene or polynucleotide or all genes or polynucleotides selected from the group consisting of DLK1, PRSS3, ARC, SLIT3, JPH1, ARH1, CNR1, ROBO2, BTBD3, KLRC3, Hs. 434957, Hs. 346735, Hs. 120591, Hs. 196008, Hs. 124776, Hs. 119947, Hs. 349094, and mixtures thereof in a neuroblastoma cell from the patient, wherein the expression profile or change in expression of the gene or polynucleotide is indicative of the outcome of the patient. In some embodiments, the method further comprises detecting the expression of MYCN and/or CD44. In some embodiments, at least one of the genes or polynucleotides selected from the group consisting of DLK1, PRSS3, ARC, SLIT3, JPH1, Hs. 434957, Hs. 346735, Hs. 120591, and mixtures thereof, is upregulated indicating that the outcome of the patient is poor. In other embodiments, at least one gene or polynucleotide selected from the group consisting of ARH1, CNR1, ROBO2, BTBD3, KLRC3, Hs. 196008, Hs. 124776, Hs. 119947, Hs. 349094, and mixtures thereof, is downregulated indicating that the outcome of the patient is poor. Optionally, the expression levels of at least one gene or polynucleotide are compared to that of a patient with a good outcome and/or poor outcome. For example, if the expression level of the gene is upregulated or down regulated in comparison to expression levels in a patient with good outcome, then it is likely the patient will have a poor outcome.
In some embodiments, the genes or polynucleotides comprises a sequence of the Image Id Nos as follows: a gene DLK1 comprises a polynucleotide sequence of Image ID NO: 296815 or 436121; a gene PRSS3 comprises a polynucleotide sequence of Image ID NO: 1913366; a gene ARC comprises a polynucleotide sequence of Image ID NO: 222457; a gene SLIT3 comprises a polynucleotide sequence of Image ID NO: 450382, or Image ID NO: 192225, or Image ID NO: 2030301; a gene JPH1 of Image ID NO: 811874; a gene ARH1 comprises a polynucleotide sequence of Image ID NO: 2336916; a gene CNR1 comprises a polynucleotide sequence of Image ID NO: 26295; a gene ROBO2 comprises a polynucleotide sequence of Image ID NO: 377573; a gene BTBD3 comprises a polynucleotide sequence of Image ID NO: 811918; a gene KLRC3 comprises a polynucleotide sequence of Image ID NO: 2361911; Hs. 434957 comprises a polynucleotide sequence of Image ID NO: 681891; Hs. 346735 comprises a polynucleotide sequence of Image ID NO: 143169; Hs. 120591 comprises a polynucleotide sequence of Image ID NO: 1540478; Hs. 196008 comprises a polynucleotide sequence of Image ID NO: 111264; Hs. 124776 comprises a polynucleotide sequence of Image ID NO: 1574206; Hs. 119947 comprises a polynucleotide sequence of Image ID NO: 379779; and Hs. 349094 comprises a polynucleotide sequence of Image ID NO: 687667. SEQ ID NOs corresponding to a representative polynucleotide sequence for each gene or polynucleotide are provided in Tables 2 and 3. Sequences corresponding to the SEQ ID NOs are provided in the sequence listing provided herein. The sequence listing forms a part of this disclosure and the contents of the sequence listing are hereby incorporated herein.
In some embodiments of the methods, the expression of at least two of the genes or polynucleotides, preferably at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen or all of the genes of Table 2 are detected. In a further embodiment, the method may further comprise detecting an up-regulation of expression of MYCN and/or a downregulation of expression of CD44. In some embodiments, the gene for MYCN comprises the sequence Image ID NO: 41565. The gene for MYCN may also comprise a polynucleotide sequence of SEQ ID NO:16. In some embodiments, the gene for CD44 comprises the sequence of Image ID NO: 1967589. The gene for CD44 may also comprise a polynucleotide sequence of SEQ ID NO:12.
In some embodiments of the methods, the patient having neuroblastoma is classified as high risk according to the criteria of the Children's Oncology group. This criteria has been described in Brodeur et al. cited supra. In other embodiments, the tumor from the patient having neuroblastoma does not have an amplification of MYCN. The methods of the invention are useful to predict the outcome of high risk patients including those patients that do not have an amplification of MYCN.
In some embodiments, the methods may further comprise detecting at least one other gene or polynucleotide identified in Table 3. The methods may involve successively detecting each of the next 10 top ranked genes or polynucleotides as provided in Table 3 up to and including detecting all 250 genes or polynucleotides identified in Table 3. For example, the methods for predicting outcome of a patient having neuroblastoma may further comprise detecting the expression levels of at least the top twenty to thirty ranked genes, the top thirty to forty top ranked genes etc. or combination thereof. In Tables 3 and 9A, B, C, the expression profile of the genes as upregulated or downregulated in neuroblastoma cells is shown.
Another aspect of the invention provides methods for selecting a treatment for patients having neuroblastoma comprising a) determining a gene expression profile of the neuroblastoma tumor cell of at least one gene or polynucleotide selected from group consisting of DLK1, PRSS3, ARC, SLIT3, JPH1, ARH1, CNR1, ROBO2, BTBD3, KLRC3, Hs. 434957, Hs. 346735, Hs. 120591, Hs. 196008, Hs. 124776, Hs. 119947, Hs. 349094, and mixtures thereof; and b) predicting whether the outcome of the patient is poor or good based on the expression profile; c) optionally, designing a more aggressive treatment for the patient if the predicted outcome for the patient is poor or designing a less aggressive treatment if the predicted outcome is good. Predicting whether the outcome is good or bad can be determined by comparing the expression profile to the expression profile of a patient with a good outcome and/or the expression of profile of a patient with poor outcome. For example, if the expression level is upregulated or down regulated in comparison to expression levels in a patient with good outcome, then it is likely the patient will have a poor outcome. Standard statistical methods may be employed to conduct the comparison, including Kaplan Meier methods. An example of the expression profile is provided herein in
The genes given in table 2 and 3 below can also be used to make up a selection or set of genes for predicting the outcome of a patient with neuroblastoma (NB). Gene selections such as these can be used to predict the clinical outcome of a patient with neuroblastoma as discussed above.
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Table 3 shows the top 250 ranked genes for predicting the outcome of a patient having neuroblastoma. Table 3 provides exemplary sequences for each of genes or polynucleotides by Unigene No., Accession No. and a corresponding SEQ ID NO:, other polynucleotide sequences and/or amino acid sequences can be readily identified by one of skill in the art. The sequence listing forms a part of this disclosure and is hereby incorporated by reference. Table 3 also shows expression level of the gene or polynucleotide in a poor outcome patient with P+ meaning the gene is upregulated in poor outcome patients and P− is downregulated in poor outcome patients. An example of expression levels of each gene or polynucleotide is shown in Tables 9A, B, and C.
One embodiment of the invention offers a selection or set of genes that are expressed in a neuroblastoma cell. Such a selection or set of genes function to predict the outcome of a patient with neuroblastoma when the gene selection from the neuroblastoma cell is compared to the expression of an identical selection of genes from a non-neuroblastoma cell, or a neuroblastoma cell associated with a good outcome and/or poor outcome. As used herein, the phrase “function to predict the outcome of a patient” can mean to identify, to be indicative of, to be highly and/or differentially expressed in patients having different outcomes. As used herein, the phrase “different outcomes” can refer to time remaining before death, survival versus death, response to a particular course of treatment, for example. In one embodiment, at least one of the genes is chosen from table 2. In another embodiment, at least one of the genes is chosen from table 2, or 3. In a further embodiment, there are at least 9 genes chosen from table 2, preferentially selected from the top ranked genes. In an even further embodiment, there are at least 9 genes chosen from table 2 or 3, preferentially selected from the top ranked genes.
The invention also includes a gene set or selection comprising at least two genes or polynucleotides selected from the group consisting of DLK1, PRSS3, ARC, SLIT3, JPH1, ARH1, CNR1, ROBO2, BTBD3, KLRC3, Hs. 434957, Hs. 346735, Hs. 120591, Hs. 196008, Hs. 124776, Hs. 119947, Hs. 349094, and mixtures thereof, or the complements thereof. In some embodiments, the gene set or selection further comprises MYCN and/or CD44. Image ID NOs corresponding to these genes or polynucleotides have been described in
In some embodiments, the gene selection comprises at least two of the genes or polynucleotides, preferably at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen or at least 19 of the genes of Table 2 or the complements of these polynucleotides or genes.
In other embodiments, the gene set or selection comprises at least two genes upregulated in a neuroblastoma in patients with poor outcome. A gene set or selection comprises DLK1, PRSS3, SLIT3, or mixtures thereof. A gene set or selection may further comprise ARC, MYCN, JPH1, Hs. 434957, Hs. 346745, Hs. 120591, or mixtures thereof or complements thereof. The gene set or selection may further comprise one or more additional genes shown in Table 3 that are upregulated in a neuroblastoma cell with poor outcome (identified as P+) or the complements thereof.
In a further embodiment, the gene set or selection comprises at least two genes downregulated in a neuroblastoma cell in a patient with poor outcome. A gene set or selection comprises CD44, ARH1, CNR1, ROBO2, BTBD3, KLRC3, Hs. 196008, Hs. 124776, Hs. 119947, Hs. 349094, or mixtures thereof or complements thereof. The gene set or selection may further comprise at least one additional gene or polynucleotide downregulated in a neuroblastoma cell from a patient with poor outcome (identified as P−) as shown in Table 3 or complements thereof.
In some embodiments, the gene selection may further comprise at least one other gene or polynucleotide identified in Table 3. The gene selection may successively include each of the next 10 top ranked genes or polynucleotides as provided in Table 3 up to and including all 250 genes or polynucleotides identified in Table 3 or their complements. For example, the gene selection may further comprise at least the top twenty to thirty ranked genes, the top thirty to forty top ranked genes etc., and combinations thereof or their complements.
The gene selection or set of genes and probes or primers that can detect these genes or polynucleotides can be used to prepare a microarray, hybridization assay, PCR assay that can be used to analyze a neuroblastoma tumor cell or sample in order to provide a prediction regarding the outcome of the patient having a neuroblastoma tumor. In some embodiments, gene products, such as polypeptides, can be detected using standard methodologies such as ELISA, immunoPCR and the like. An amino acid sequence of the polypeptides encoded by the polynucleotide are available by accessing the Image ID NOs. or Accession Nos. using a publicly available database such as the source database at Stanford.
Another embodiment of the invention includes a selection of least one product of a selection of genes. As used herein, the term “product of a gene” or “gene product” can include entities that are naturally produced by the cancer cell. Examples of gene products include, but are not limited to, DNA, mRNA, and proteins. Gene products can be utilized in methods of the invention for predicting the outcome of a patient with neuroblastoma or as a target for therapeutic treatment of a patient with neuroblastoma.
Another aspect of the invention provides a kit for predicting the outcome of a patient having a neuroblastoma. A kit for predicting the outcome of a patient having neuroblastoma comprises an agent for detecting expression of at least two genes or polynucleotides, or the complements thereof, selected from the group consisting of DLK1, PRSS3, SLIT3, and mixtures thereof, or the complements thereof, and optionally, instructions for detecting increased expression as compared to a control, wherein enhanced expression is indicative of poor outcome. The control can be prepared from one or more nonneuroblastoma cells including at least one housekeeping gene, or it can be a neuroblastoma cell from a patient or patients with good and/or poor outcomes. Examples of such information concerning expression levels of genes or polynucleotides in neuroblastoma cells form patients with good outcome and/or poor outcome is provided herein in
A number of different known assays can be utilized to determine expression levels of a gene from a cell or patient sample. These assays include, for example, microarray assays, hybridization assays, PCR assays, ELISA assays, immunoPCR assays. One embodiment, may involve detecting increased levels of one or more polypeptides in a biological sample, such as serum, from patients having neuroblastoma. In some embodiments, the agent is at least one probe or primer that can detect at least one of DLK1, PRSS3 and SLIT3. In other embodiments, the agent is at least one antibody that can detect at least one of DLK1, PRSS3, or SLIT3. Preferably, the antibody is detectably labeled with a radioactive or fluorescent moiety.
In other embodiments, a kit comprises an agent that can detect expression of at least two genes or polynucleotides selected from the group consisting of DLK1, PRSS3, ARC, SLIT3, JPH1, ARH1, CNR1, ROBO2, BTBD3, KLRC3, Hs. 434957, Hs. 346735, Hs. 120591, Hs. 196008, Hs. 124776, Hs. 119947, Hs. 349094, and mixtures thereof, or the complements thereof, and optionally, instructions providing the expression profile of at least one polynucleotide that is indicative of a poor and/or good outcome of the patient. Preferably, the expression profile of all of the genes is provided. An agent can comprise at least one probe or primer that can detect at least one of the genes or polynucleotides. In other embodiments, the agent is at least one antibody that can detect at least one polypeptide encoded by the gene or polynucleotide.
In some embodiments, the kit comprises a plurality of agents that can detect expression of all the genes or polynucleotides of Table 2, or the complements thereof. In some embodiments, the kit comprises a plurality of agents that can detect expression of at least one additional gene or polynucleotide or all of the genes or polynucleotides of Table 3, or complements thereof. The plurality of agents may comprise a primer or probe that can detect expression of each of the polynucleotides or genes, or their complements, of Table 2. Another embodiment includes a plurality of polynucleotides comprising two or more genes or polynucleotides of Table 3, or their complements, optionally attached to a solid substrate, and preferably excluding MYCN or CD44. The agents may be attached to a solid substrate such as a polystyrene plate or glass slide.
In some embodiments, the kits provide instructions that provide that a poor outcome is characterized by upregulation of at least one gene or all genes selected from the group consisting of DLK1, PRSS3, ARC, SLIT3, JPH1, Hs. 434957, Hs. 3467345, Hs. 120591, and mixtures thereof. The instructions may also provide that a poor outcome is characterized by downregulation of at least one gene or all genes selected from the group consisting of ARH1, CNR1, ROBO2, BTBD3, KLRC3, Hs. 196008, Hs. 124776, Hs. 119947, Hs. 349094, and mixtures thereof.
C. Methods of Targeting a Gene Product to Produce a Therapeutic Agent Useful to Treat Neuroblastoma.
One embodiment of the invention includes a method of targeting a product of at least one of the genes in table 2 or 3 that includes identifying a therapeutic agent having a therapeutic effect on said gene product. Another embodiment includes a method of therapeutic treatment of neuroblastoma by using a selection of genes or their products that are expressed in a neuroblastoma cell, wherein the genes and/or their products function to predict the outcome of the neuroblastoma cell when the gene selection from the neuroblastoma cell is compared to the expression of an identical selection of genes from a non-neuroblastoma cell, or a cancer cell from a patient with a good outcome and/or poor outcome. Another embodiment includes a method of targeting a product of at least one of the genes in Tables 2 or 3 for identification of an antagonist or agonist that can be utilized to treat neuroblastoma.
Another aspect of the invention involves a method of screening for an agent that modulates a gene or polynucleotide for treatment for neuroblastoma. A screening method comprises a method for detecting an agent that can modulate the expression of at least one gene or polynucleotide for which a change in expression is correlated with poor outcome in a patient or subject having neuroblastoma. In some embodiments, the genes or polynucleotides are selected from the top ranked genes of Table 2. In other embodiments, at least one of the top ranked genes of table 2 is screened excluding MYCN and/or CD44. In other embodiments, at least one gene or polynucleotide can be selected from any of the genes of Table 3.
In some embodiments, the gene or polynucleotide is upregulated in a neuroblastoma tumor cell and is associated with poor outcome. If a gene is upregulated, a method comprises identifying an antagonist of the gene or polynucleotide. A gene or polynucleotide that is upregulated comprises or is selected from the group consisting of DLK1, PRSS3, SLIT3, and mixtures thereof. In other embodiments, a gene or polynucleotide is selected from the group consisting of DLK1, PRSS3, ARC, SLIT3, JPH1, and mixtures thereof. A method comprises identifying an antagonist of at least one gene or polynucleotide upregulated in neuroblastoma cell comprising measuring expression or activity of at least one gene or polynucleotide selected from the group consisting of DLK1, PRSS3, ARC, SLIT3, JPH1, Hs. 434957, Hs. 346735, Hs. 120591, and mixtures thereof in the presence or absence of a candidate agent; and identifying the candidate agent that inhibits expression or activity of at least one of the genes or polynucleotides. The method can further comprise at least one or more genes or polynucleotides that are upregulated in a neuroblastoma cell and associated with poor outcome, such as provided in Table 3 and identified as P+.
In some embodiments, at least one gene or polynucleotide is downregulated and correlated with poor outcome of a patient having neuroblastoma. When a gene or polynucleotide is downregulated, a method comprises identifying an agonist of a gene or polynucleotide downregulated in a neuroblastoma cell comprising measuring expression or activity of at least one gene selected from the group consisting of ARH1, CNR1, ROBO2, BTBD3, KLRC3, Hs. 196008, Hs. 124776, Hs. 119947, Hs. 349094, and mixtures thereof, in the presence and absence of a candidate agent, identifying as an agonist the candidate agonist that increases the expression or activity of the gene or polynucleotide. The method can further comprise screening for an agonist of one or more of the genes or polynucleotides that are downregulated in a neuroblastoma cell and associated with poor outcome, such as provided in Table 3 and identified as P−.
Exemplary sequences for the genes, polynucleotides, or polypeptides of Tables 2 and 3 can be found in Image ID NOs and Accession Nos. as provided in
An antagonist or agonist useful as a therapeutic agent can comprise a biological or chemical entity that is based on some aspect of a gene. Examples of therapeutic agents include, but are not limited to, vaccines, antibodies, oligonucleotide DNA antisense, RNAi, chemical molecules, proteins, inhibitors, antagonists, or combinations thereof. Having a therapeutic effect on a gene product can include, but is not limited to, inhibition of some activity or process of a cell, cessation of some activity or process of a cell, an increase in some activity or process of a cell, interference with some process or activity of a cell, modification of the expression of at least one gene, modification of the expression of at least one gene product, modification of the function of at least one gene, and modification of the function of at least one gene product.
An antagonist of any of the genes, polynucleotides or gene products is effective to inhibit expression or activity of the gene or gene product and can include antisense nucleic acid, nucleic acid or protein vaccines, siRNA, aptamers, and antagonist antibodies (including humanized antibodies). An agonist of any of the genes, polynucleotides or gene products is effective to enhance or increase expression or activity of the gene or gene products and can include agonist antibodies (including humanized antibodies). Other agonists include polynucleotides providing for expression, preferably overexpression, of the downregulated gene or polynucleotide. Antibodies can be prepared by methods known to those of skill in the art and in references such as U.S. Pat. No. 6,331,415; Kohler et al., Eur. J. Immun., 6:511 (1976); Winter et al., Ann. Rev. of Immunol., 12:433 (1994); and U.S. Pat. No. 5,225,539.
The antagonists and/or agonists identified herein may be utilized in methods to treat neuroblastoma. For example, a therapeutic agent such as a humanized antibody or antisense nucleic acid may be administered to a patient in order to downregulate expression of genes or polynucleotides that are upregulated in patients that are predicted to have a poor outcome. Such therapeutic agents may be utilitized in combination with other therapies, including conventional chemotherapeutic agents.
The following examples provide a nonlimiting illustration of various embodiments of the invention.
Preparation of Glass cDNA Microarrays, Probe Labeling, Hybridization and Image acquisition can be performed according to the protocol given below, which is a standard NHGRI protocol (http://www.nhgri.nih.gov/DIR/LCG/15K/HTML/protocol.html).
Gene-specific DNA is produced by PCR amplification of purified template plasmid DNAs from cloned ESTs. The PCR product is purified by ethanol precipitation, thoroughly resuspended in 3×SSC, and printed onto a poly-L-lysine coated slide.
The materials, reagents, and solutions used include: 96 well alkaline lysis miniprep kit (Edge BioSystems, Gaithersburg, Md.); L B Broth (Biofluids, Rockville, Md.); Superbroth (Biofluids, Rockville, Md.); dATP, dCTP, dGTP, dTTP, 100 mM each #27-2035-02, store frozen, −20° C. (Pharmacia, Peapack, N.J.); PCR primer AEK M13F (5′-GTTGTAAAACGACGGCCAGTG-3′) (SEQ ID NO. 251) and AEK M13R (5′-CACACAGGAAACAGCTATG-3′) (SEQ ID NO. 252) at 1 mM concentration, store frozen, −20° C.; 10×PCR Buffer, # N808-0189, and Ampli-Taq DNA polymerase, # N808-4015 store frozen, −20° C. (Perkin Elmer, Norwalk, Conn.); Carbenicillin (Gibco-BRL, Rockville, Md.); Ethanol (200 Proof USP Ethyl Alcohol); 1M Tris-HCl (pH 8); 0.5M NaEDTA (pH 8); T Low E; Buffer; 20×SSC; Glycerol (enzyme grade); Sodium Acetate (tri-hydrate); Boric Acid; Sodium Hydroxide (1M); Glacial Acetic Acid; Succinic anhydride, #23969-0 and 1-methyl-2-pyrrolidinone, #32863-4 (Aldrich Chemical Co., St. Louis, Mo.); Diethyl Pyrocarbonate (DEPC) treated H2O; Master set of clone-purified, sequence verified human ESTs (e.g. gf211 release, Research Genetics, Huntsville, Ala.); 96 pin inoculating block (#VP 4088, V&P Scientific, Inc, San Diego, Calif.); Airpore Tape Sheets, (#19571, QIAGEN Inc., Valencia, Calif.); Sterile 96-well plate seals, (e.g. # SEAL-THN-STR (Elkay Products, Inc., Shrewsbury, Mass.); 96-well U-Bottom Microtiter Plates, #3799 and 96-well V-Bottom Microtiter Plates, #3894 (Corning Inc., Corning, N.Y.); Thin wall PCR plate and Cylcleseal PCR plate sealer (e.g. #1038-50-0 and #1044-39-4, Robbins Scientific Corp. Sunnyvale, Calif.); household one-gallon sealable storage bags (e.g. Glad Lock); heat sealable storage bags and heat sealer; 0.2 mm Sterile Filtration unit; Diamond scribe for writing on slides; Pyrex baking dish (˜24×34×5 cm); UV transparent plastic wrap (e.g. Glad Cling Wrap); 30 slide rack (stainless steel) #113 and 30 slide glass tank, #122 (Shandon Lipshaw, Pittsburgh, Pa.); 1 L glass tank; 1 L glass beaker; 1 L graduated; cylinder; Stir bar; Slide Box (plastic with no paper or cork liners), (e.g. #60-6306-02, PGC Scientific, Gaithersburg, Md.); PCR heat cycler (e.g. DNA Engine Tetrad, MJ Research, Waltham, Mass.); Centrifuge with a horizontal (“swinging bucket”) rotor with a depth capacity of 6.2 cm for spinning microtiter plates and filtration plates (e.g. Sorvall Super T 21, Sorvall Inc., Newtown, Conn.); 37° C. Shaker incubator with holders for deep-well plates; 37° C. Waterbath; 65° C. Incubator; Vortex mixer; Immunowash microtiter plate washer, #1575 (BioRad, Hercules, Calif.); pH Meter; Platform Shaker; UV Stratalinker 2400, (Stratagene La Jolla, Calif.); Stirrer/Hotplate; Robotic slide printer; −80° C. Freezer; −20° C. Freezer; 45% (w/v) Sterile Glycerol; 450 grams enzyme grade glycerol per liter 9 Autoclave and store at room temperature); T low E Buffer; 1M Tris-HCl (pH 8.0) 10 mL; 0.5 M EDTA (pH 8.0) 0.2 mL; DEPC treated H2O 990 mL (Autoclave and store at room temperature); Carbenicillin stock solution (1 gram of carbenicillin in 10 mls of sterile water, Sterile filter with a 0.2 micron filter, Store frozen at −20° C.); LB with 100 μg/ml carbenicillin (Add 1 ml of carbenicillin stock solution to 1 liter of LB, Make fresh); 3M Sodium Acetate pH=6.0 (408.24 grams sodium acetate (tri-hydrate) per liter, 3M acetic acid (172.4 ml per liter), Titrate the pH of the 3M sodium acetate solution to pH 6.0 with the 3M acetic acid solution, Filter sterilize using a 0.2 micron filter, Store at room temperature); Ethanol/acetate mix (Ethanol (100%) 950 ml, Sodium acetate pH=6.0, 50 ml); 1000 ml 3×SSC; DEPC H2O 42.5 ml; 20×SSC 7.5 ml; 50 ml 70% Ethanol; Ethanol (100%) 350 ml; DEPC H2O 150 ml; 500 ml.
The first step is to grow the EST clones. An exemplary method is described below. In one embodiment, the cDNA clones were obtained from Research Genetics (Huntsville, Ala.) and were their standard microarray set, which consisted of 3789 sequence-verified known genes and 2778 sequence-verified ESTs. In other embodiments, sequence verified libraries with 42,578 cDNA clones were used and obtained from Research Genetics (Huntsville, N.C.) as described in Example 3.
The sealed master plates are incubated over night at 37° C. Most suppliers provide low density bacterial cultures. Replicating directly from these dilute stocks frequently results in non-growth in the secondary culture. If making the template from a plate that had previously been cultured to high density before freezing, this initial growth step should not be used, as it will reduce the viability of the cultures.
A set of standard 96 well round (U) bottom plates is then prepared by labeling all plates and placing 100 μl of LB broth containing 100 μg/ml carbenicillin in each well. These plates are used as working copies. To preserve the master set of plates, it is useful to make replicate copies of the master plate to serve as working copies when the master plate is first replicated. The EST clones are then checked to insure that they were in a vector conferring ampicillin resistance, as is common with human IMAGE clones.
The master plates are spun briefly (about two minutes) at 1000 rpm in a horizontal microtiter plate rotor to remove condensation and droplets from the seals before opening. Bacterial culture fluid on the sealers can easily be transferred from one well to others, cross-contaminating the stocks.
Then a container is partially filled with 100% alcohol. The 96 pin-replicating tool is dipped in the alcohol, removed and then the pins were flamed.
The inoculation block is allowed to cool briefly, then the replicating tool is dipped in the master plate and then into the daughter plate. This is repeated as necessary for each plate inoculated. It is useful to color the plate corner near the A-1 well of all master and daughter plates with a marker pen before beginning the replication process in order to reduce mistakes in the relative orientation of the plates. The suggested plates have a notch at this corner as well.
The inoculated LB plates, with the lids on, are placed into a one gallon sealable bag containing a moistened paper towel and grow overnight at 37° C. Many 37° C. incubators tend to dry out microtiter plate cultures. Placing the plates in a highly humidified bag avoids this problem.
Next, deep well plates are filled with 1 ml of Superbroth (100 μg/ml carbenicillin) per well. These plates serve as the source of culture for template preparation. Using the replicating tool, the deep well plates are then inoculated directly from the freshly grown LB plates. Next, the openings of the deep well plates are covered with Qiagen Airpore Tape Sheets and the plastic lids are placed over the sheet. The plates are then placed in a 37° C. shaker incubator at 200 RPM for twenty-four hours. 50 μl of 45% (w/v) sterile glycerol is added to each well of any working plates that are to be frozen (−80° C.) and subsequently used as culture sources.
After the EST clones are grown, the plasmid templates have to be isolated. First, the lysis buffer (Edge Biosystems Kit) is warmed to 37° C. to dissolve the SDS. Then the RNAse solution is added to the resuspension buffer (Edge Biosystems Kit), 1 ml/100 ml, and stored at 4° C. The receiving plates are prepared from the Edge Biosystems Kit by adding 350 μl of ethyl alcohol to each well of the receiving plates. The filter plate is then placed on top and secured with tape. The bacterial cultures in the deep well plates are centrifuged at 1500×g for seven minutes in a centrifuge equipped with a horizontal rotor for 96-well plates. They were then briefly inverted and excess media is tapped out on a clean paper towel. The pellets will loosen and may be lost when pouring off excess media if this step is delayed.
The pellet is then resuspended in 100 μl of Resuspension Buffer, and Vortexed until the entire pellet was re-suspended. This step is critical. Poor resuspension of the cells results in clumps of cells that do not lyse in subsequent steps. This reduces the yield and decreases the purity of the product. 100 μl of Lysis Buffer is then added and the solution is mixed gently by rocking the plates from side to side, to avoid shearing the bacterial chromosomal DNA. 100 μl of Precipitation buffer is added to each well and briefly mixed. Then, 100 μl of Neutralization buffer is added to each well and vortexed.
The contents of the deep wells are then transferred to the waiting filter plates/receiving plate stacks using the wide bore pipette tips provided in the kits. The stacked plates are then centrifuged at 1500×g for twelve minutes in a centrifuge equipped with a horizontal rotor for 96-well plates. The stacked plates are then removed from the centrifuge. The filter plates are removed and discarded. The alcohol and filtrate are decanted from the receiver plate and the excess alcohol is touched off on clean paper towels. 500 μl of 70% ethanol is added to each well and immediately decanted and excess alcohol is touched off with a clean paper towel. Then, the plates are placed in a clean drawer without their lids, covered with a clean paper towel and allowed to dry overnight.
The next day, the DNA is resuspended in 200 μl of T Low E Buffer. The top is sealed with plate sealer and rehydrated at 4° C. for at least two days before using. They are stored at −20° C. in the interim.
After the plasmid templates have been isolated, the EST inserts are amplified. For each 96 well plate to be amplified, a PCR reaction mixture is prepared containing the following ingredients: 1000 μl of 10×PCR Buffer, 20 μL of dATP (100 mM), 20 μL of dGTP (100 mM), 20 μL of dCTP (100 mM), 20 μL of dTTP (100 mM), 5 μL of AEK M13F primer (1 mM), 5 μL of AEK M13R primer (1 mM), 100 μL of Ampli-Taq polymerase (5 U/μl), and 8800 mL of H2O. The 96-well PCR plates are then labeled and 100 μl of the PCR reaction mixture from above is aliquotted to each well. The plates are then gently tapped to insure that no air bubbles are trapped at the bottom of the wells. 1 μl of purified EST plasmid template from above is then added to each well. The donor and recipient plates are then marked at the corner, near the A1 well to facilitate correct orientation during transfer of the template. It is important to make sure that the pipette tips are all submerged in the PCR reaction mix when delivering the template. Missing the liquid is easier when multi-channel pipettes are used.
The following thermal cycle series is then performed: 1 initial cycle of heating to 96° C. and holding for 30 sec, 25 cycles of denaturing at 94° C. for 30 sec, reannealing at 55° C. for 30 sec, and extending at 72° C. for 150 sec, one final cycle of holding at 72° C. for 5 minutes, then cooling to ambient temperature. After the above cycle, the plates are held at 4° C. while quality controls are performed.
The quality control is done by agarose gel electrophoresis of the ESTs. If this is the first time the template for these ESTs is being amplified, 2 μl of each PCR product is analyzed on a 2% agarose gel. If amplified products from this template had been previously tested, then one row of wells from each plate amplified is analyzed. Gel imaging allowed a rough quantitation of product while giving an excellent characterization of the product. Band size, as well as the number of bands observed in the PCR products, contributed to an understanding of the final results of the hybridization. The use of gel well formats suitable for loading from 96 well plates and programmable pipetters made this form of analysis feasible on a large scale.
The materials, reagents and solutions for the quality control check included: Electrophoresis apparatus with capacity for four 50 well combs, (e.g. #D3, Owl Scientific, Woburn, Mass.); 50× Tris-Acetate Electrophoresis BufferM; Agarose; Dye Solution (Xylene Cyanol/Bromophenol Blue) (e.g. #351-081-030, Quality Biological Inc., Gaithersburg Md.); Glycerol (enzyme grade); Ethidium Bromide solution (10 mg/ml); 100 base-pair ladder size standard; Programmable, 12-channel pipetter (e.g. #2019, Matrix Technologies, Lowell, Mass.); Disposable microtiter mixing trays (e.g. Falcon #353911, Becton Dickinson, Franklin Lake, N.J.); Electrophoresis power supply; 1×TAE Buffer; 50×TAE Buffer 40 ml; Ethidium Bromide (10 mg/ml) 0.1 ml and Water 960 ml; 1000 ml; Loading Buffer; Glycerol (enzyme grade) 4.0 ml, DEPC Water 0.9 ml, and Dye Solution* 0.1 ml for a total of 5.0 ml (*THis solution is 0.25% (w/v) Xylene Cyanol and 0.25% (w/v) Bromophenol Blue); 100 bp Size Standards; DNA ladder (1 mg/ml) 50 μL, 1 M Tris-HCl (pH 8.0) 5 μl, 0.5 M EDTA (pH 8.0) 5 μl, and Loading Buffer 440 μl for a total of 500 μl
The electrophoresis is carried out with a 2% agarose gel (1×TAE) with four combs (50 tooth) that is submerged in an electrophoresis apparatus with sufficient 1×TAE buffer to just cover the surface of the gel. A reservoir of Loading Buffer is prepared, using 12 wells of a microtiter plate. Then a pipetter is programmed to sequentially carry out the following steps: fill with 2 μl, fill with 1 μL, fill with 2 μl, mix a volume of 5 μl five times, expel 5 μl. Twelve (12) disposable tips are then placed on the pipetter. 2 μl of PCR product from wells A1-A12 of the PCR plate were loaded, followed by 1 μl of air, then 2 μl of Loading Buffer from the reservoir. The tips are then placed in clean wells of a disposable mixing tray and the pipette is allowed to mix the sample and loading dye. The pipette tip is then placed in a 50 well row so that the tip containing the PCR product from well A1 is in the second well of the row, and the other tips are in every other succeeding well.
The process is repeated (changing tips each time), to load PCR plate row B starting in the 3rd well, interleaved with the A row, the C row starting at well 26, and the D row at well 27, interleaved with the C row. Then 5 μl of 100 bp Size Standards are placed in wells 1 and 50. This process is repeated, to load samples from rows E, F, G, and H in the second, 50 well row of gel wells, to load samples from two 96 well PCR plates per gel, or single row samples from 16 PCR plates. To reduce diffusion and mixing, a voltage is applied to the gel for a minute between loading each well strip. This caused the DNA to enter the gel, and reduced band spreading and sample loss.
A voltage is then applied to the gel and it is run until the bromophenol blue (faster band) has nearly migrated to the next set of wells. For a gel that is 14 cm in the running dimension, and 3 cm between each row of wells, 200 volts were applied for 15 minutes. Digital photos of the gel are taken and the images stored for future reference. The gels should show bands of fairly uniform brightness distributed in size between 600 to 2000 base-pairs. Further computer analysis of such images can be carried out with image analysis packages to provide a list of the number and size of bands. Ideally this information can be made available during analysis of the data from hybridizations involving these PCR products.
After the quality control checks are run on the plates, the next step involves purifying the PCR products. 96 well V-bottom plates were filled with 200 μl per well of ethanol/acetate mix. The ethanol acetate solution used for precipitation is less acidic (pH 6) than is typically used. In this instance, more acidic solutions produce precipitates which are harder to resuspend without improving yield.
100 μl per well of PCR product is transferred into V-bottom plates and mixed by pipetting a volume of 75 μl per well four times. The plates are then placed in a −80° C. freezer for one hour or stored overnight at −20° C. The plates are stored at −20° C. if they were to be left for more than one hour, because aggressive precipitation produces precipitates which are hard to resuspend. The plates are then thawed to reduce brittleness and melt any ice, which may have formed in the wells.
The plates are loaded into a centrifuge with a horizontal microtiter plate rotor and spun at 2600×g for 40 minutes at 4° C. Next, the supernatant from each well is aspirated using the Immunowash plate washer. Settings for the depth of aspiration by the plate washer needed to be adjusted to suit the microtiter plates used. It is advisable to leave approximately 10-20 ml in the bottom of the well to avoid disturbing the pellet.
200 μl of 70% ethanol is delivered to each well in the plate using the Immunowash plate washer, and the plates are centrifuged at 2600×g for 40 minutes. The supernatant is aspirated from each well using the Immunowash plate washer, and the plates are dried overnight in a closed drawer. They should not be dried in a speed-vac because desiccated PCR products are hard to resuspend.
After the PCR products are purified, they are then resuspended by adding 40 μl of 3×SSC per well. The plates are then sealed with a foil sealer, taking care to achieve a tight seal over each well. The plates are then placed in heat sealable bags with paper towels moistened with 3×SSC and the bag is sealed with a heat sealer. The high external humidity within the sealed bag helped to keep the volumes in the individual wells from varying. The bags are then placed in a 65° C. incubator for 2 hours. The heat in the incubator is then turned off, and the plates are allowed to cool gradually in the incubator to avoid condensation on the sealers. The plates are stored at −20° C.
The yield of the PCR suspension is then checked by fluorometric determination of DNA concentration. 1 μl of resuspended PCR product from one row of wells from each plate on a 2% agarose gel was analyzed as previously described. Adequate precipitation and resuspension produce very intense bands, with no material failing to leave the loading well, and no smear of material from the band towards the loading well.
While it would be ideal to be able to exactingly quantify each EST PCR product and spot each DNA species at equivalent concentrations, it is impractical for most labs to do so when thousands of ESTs must be prepared. Fortunately, it is possible to use a strategy where excess DNA is spotted, so that the exact quantities used do not produce much variation in the observed results. When using this strategy, it is necessary to track the average productivity of the PCR reactions. Fluorometry provides a simple way to obtain an approximate concentration of the double-stranded PCR product in the PCR reaction mix.
Next, the double stranded DNA is quantified. The materials, reagents, and solutions necessary include: reference double-stranded DNA (0.5 mg/ml) (e.g. #15612-013 Gibco/BRL, Bethesda, Md.), 96 well plates for fluorescent detection (e.g. #7105, Dynex, Chantilly, Va.), Fluorometer (e.g. #LS50B, Perkin Elmer, Norwalk, Conn.), FluoReporter Blue dsDNA Quantitation Kit (#F-2962, Molecular Probes, Eugene, Oreg.), TE, 12 channel multi-pipetters, Computer equipped with Microsoft Excel software, Ds-DNA Standards: 50 μg/ml, 100 μg/ml, 250 μg/ml, 500 μg/ml, μl TE 90, 80, 50, 0 μl ds-DNA (0.5 mg/ml) 10, 20, 50, 100, (It is good practice to check both the integrity (agarose gel) and the concentration (absorbance) of the standard before use); Fluor Buffer (HoecHist 33258 solution (contains the dye at an unspecified concentration in a 1:4 mixture of DMSO:H2O) (from kit) 25 μl, TNE Buffer (TNE Buffer is 10 mM Tris-HCl (pH 7.4), 2 M NaCl, 1 mM EDTA) (from kit) 10 ml.
The double stranded DNA is quantified as follows. 96 well plates are labeled for fluorescence assay. 200 μl of Fluor Buffer is added to each well. 1 μl of PCR product from each well in a row of a PCR plate is added to a row of the fluorometry plate. Samples are added to rows A through G of the fluorometry plate. In the final row of the fluorometry plate 1 μl of each of the series of ds-DNA standards 0 μg/ml (TE only), 50, 100, 250 and 500 μg/ml ds-DNA are added. This series is repeated twice in the final row.
The fluorometer is set for excitation at 346 nm and emission at 460 nm, and adjusted as necessary to read the plate. If the fluorometer used does not support automated analysis, the data table is exported to Excel. The response for the standards is tested to see that it was linear and reproducible from the range of 0 to 500 μg/ml of ds-DNA.
Next, the concentration of ds-DNA in the PCR reactions is calculated using the following equation, after subtracting the average 0 μg/ml value from all other sample and control values:
[ds-DNA(μg/ml)]=((PCR sample value)/(average 100 μg/ml value))*100
Constantly tracking the yields of the PCRs makes it possible to rapidly detect many ways in which PCR can fail or perform poorly. This assay can also be applied after precipitation and resuspension of the PCR products to monitor overall recovery of product. 1 μl of amplified products from one row of wells from each amplified plate by fluorometry is analyzed.
Slides are then coated with poly-L-lysine to have a surface that is both hydrophobic and positively charged. The hydrophobic character of the surface minimizes spreading of the printed spots, and the charge appears to help position the DNA on the surface in a way that makes cross-linking more efficient.
Materials, reagents, and solutions for coating the slides includes: Gold Seal Microscope Slides (#3011, Becton Dickinson, Franklin Lake, N.J.), Ethanol (100%), Poly-L-lysine (#P8920, Sigma, St. Louis, Mo.), 50 Slide Stainless Steel Rack, #900401, and 50 Slide Glass Tank, #900401, (Wheaton Science Products, Millville, N.J.), Sodium Hydroxide, Stir Plate, Stir Bar, Platform Shaker, 30 Slide Rack, #196, plastic, and 30 slide Box, #195, plastic, (Shandon Lipshaw, Pittsburgh, Pa.), Sodium Chloride, Potassium Chloride, Sodium Phosphate Dibasic Heptahydrate, Potassium Phosphate Monobasic, Autoclave, 0.2 mm Filter: Nalgene, Centrifuge: Sorvall Super 20, Slide Box (plastic with no paper or cork liners), (e.g. #60-6306-02, PGC Scientific, Gaithersburg, Md.), 1 L Glass Beaker; 1 L Graduated Cylinder, 1M Sodium Borate (pH 8.0) (Dissolve 61.83 g of Boric acid in 900 ml of DEPC H2O. Adjust the pH to 8.0 with 1N NaOH. Bring volume up to one liter. Sterilize with a 0.2 micron filter and store at room temperature), Cleaning Solution (H2O 400 ml, Ethanol 600 ml, NaOH 100 g—Dissolve NaOH in H2O. Add ethanol and stir until the solution clears. If the solution does not clear, add H2O until it does), and Poly-L-lysine Solution (poly-L-lysine (0.1% w/v) 35 ml PBS 35 ml H2O 280 ml 350 ml)
First, the slides are placed into 50 slide racks and the racks are placed in glass tanks with 500 ml of cleaning solution. Gold Seal Slides are highly recommended, as they have been found to have consistently low levels of autofluorescence. It is important to wear powder free gloves when handling the slides to avoid contamination.
The tanks are placed on platform shakers for two hours at 60 rpm. After being shook, the cleaning solution is poured out, and the slides are then washed in H2O for three minutes. This wash is repeated four times. The slides are then transferred to 30 slide plastic racks and placed into small plastic boxes for coating. The slides are then submerged in 200 ml poly-L-lysine solution per box. The slide boxes are then placed on platform shaker for one hour at 60 rpm. The slides are rinsed three times with H2O, and submerged in H2O for one minute, and then centrifuged for two minutes at 400×g and the slide boxes used for coating are dried.
The slides are then placed back into the slide box used for coating and allowed to stand overnight before transferring to a new slide box for storage. This allowed the coating to dry before it was handled. The slides are allowed to age for two weeks on the bench, in a new slide box, before they are printed on. The coating dried slowly, becoming more hydrophobic with time.
Slide boxes used for long term storage should be plastic and free of cork lining. The glue used to affix the cork will leach out over time and give slides stored in these types of boxes a greasy film that has a high degree of autofluorescence. All glassware and racks used for slide cleaning and coating should be cleaned with highly purified H2O only, and detergent should not be used.
Once the slides are coated, they were printed. The variety of printers and pens for transferring PCR products from titer plates to slides precludes highly detailed descriptions of the process. The following steps provide a general description of the processing.
The print pens are pre-cleaned according to the manufacturer's specification. The printer slide deck is then loaded with poly-L-lysine coated slides from above. The plates containing the purified EST PCR products are thawed and centrifuged briefly, (about two minutes) at 1000 rpm in a horizontal microtiter plate rotor to remove condensation and droplets from the seals before being opened. 5 to 10 μl of the purified EST PCR products are transferred to a plate that served as the source of solution for the printer. Printing with quill-type pens usually requires that the volume of fluid in the print source was sufficiently low, so that when the pen was lowered to the bottom of the well, it was submerged in the solution to a depth of less than a millimeter. This keeps the pen from carrying a large amount of fluid on the outside of the pen shaft and producing variable, large spots on the first few slides printed.
A repetitive test print is run on the first slide. In this operation, the pens are loaded with the DNA solution, and then the pens serially deposited this solution on the first slide in the spotting pattern specified for the print. A test is run to check the size and shape of the specified spotting pattern, as well as its placement on the slide. It also serves to verify that the pens are loading and spotting, and that a single loading produced as many spots as were required to deliver material to every slide in the printer. If one or more of the pens is not performing at the desired level, it is re-cleaned or substituted with another pen and tested again. If all pens are performing, the full print is carried out.
At the end of the print, the slides are removed from the printer, labeled with the print identifier and the slide number by writing on the edge of the slide with a diamond scribe and placed in a dust free slide box to age for one week. It was useful to etch a line, which outlined the printed area of the slide, onto the first slide. This served as a guide to locate the area after the slides are processed, and the salt spots are then washed off.
The slides are placed, printed side face up, in a casserole dish and covered with cling wrap. The slides were then exposed to a 450 mJ dose of ultraviolet irradiation in the Stratalinker. Slides should have been and are aged at ambient temperature in a closed slide box for one week prior to blocking. The slides are then transferred to a 30 slide stainless steel rack and the rack is placed into a small glass tank. 6.0 g succinic anhydride is dissolved in 325 ml 1-methyl-2-pyrrolidinone in a glass beaker by stirring with a stir bar. Nitrile gloves are worn and the work is carried out in a chemical fume hood while handling 1-methyl-2-pyrrolidinone (a teratogen).
25 ml 1M sodium borate buffer (pH 8.0) is added to the beaker. The solution is allowed to mix for a few seconds, then rapidly poured into a glass tank with slides. Succinic anhydride hydrolyzed quite rapidly once the aqueous buffer solution is added. To obtain quantitative passivation of the poly-L-lysine coating, the reactive solution is brought in contact with the slides as quickly as possible. The glass tank is placed on a platform shaker in a fume hood for 20 minutes. Small particulates resulting from precipitation of reaction products may be visible in the fluid.
While the slides are incubating on the shaker a boiling H2O bath is prepared to denature the DNA on the slides. After the slides are incubated for 20 minutes, they are transferred into the boiling H2O bath. The heating element is immediately turned off after the slides are submerged in the bath. The slides are allowed to stand in the H2O bath for 2 minutes. The slides are then transferred into a glass tank filled with 100% ethanol and incubated for 4 minutes. The slides are removed and centrifuged at 400 rpm for 3 minutes in a horizontal microtiter plate rotor to dry the slides. The slides are then transferred to a clean, dust free slide box and allowed to stand overnight before being used for collection of gene expression data.
This protocol details exemplary methods used to extract RNA from cells, purify the RNA by a combination of phase extraction and chromatography, and prepare a labeled cDNA copy of the message fraction of the purified RNA. The protocol also describes the process of making fluorescent cDNA representations of the message pools within the isolated total RNA pools. This is accomplished by using the pure total RNA as a substrate for reverse transcription in the presence of nucleotides derivatized with either a Cy3 or a Cy5 fluorescent tag.
The materials, reagents, and solutions needed include: Trizol Reagent (#15596-018, Life Technologies, Rockville, Md.); RNeasy Maxi Kit (#75162, Qiagen, Valencia, Calif.); Chloroform; Ethanol (200 Proof USP Ethyl Alcohol); DPBS (Dulbecco's phosphate buffered saline); 3M sodium acetate (pH 5.2); DATP, dCTP, dGTP, dTTP, 100 mM each, store frozen, −20° C. (#27-2035-02, Pharmacia, Peapack, N.J.); pd(T)12-18 resuspend at 1 mg/ml, and store frozen −20° C. (#27-7858, Amersham Pharmacia Biotech); Anchored oligo primer (anchored; 5′-TTT TTT TTT TTT TTT TTT TTV N-3′) (SEQ ID NO.253); resuspend at 2 mg/ml, store frozen −20° C. (e.g. #3597-006, Genosys); CyTM3-dUTP, 1 mM, and CyTM5-dUTP, 1 mM, store −20° C., light sensitive; RNasinâ Rnase inhibitor, store −20° C. (#N211A, Promega); SUPERSCRIPT™ II Rnase H′ Reverse Transcriptase Kit, store −20° C., (#18064-014, Life Technologies, Rockville, Md.); C0t-1 DNA, 1 mg/ml, store frozen −20° C. (#15279-011, Life Technologies, Rockville, Md.); 0.5M EDTA (pH 8.0); 1 N NaOH; 1M TRIS-HCL; (pH7.5); TE pH 7.4; DEPC water 50× Tris Acetate Buffer; 15 ml round bottom; polypropylene centrifuge tubes; 50 ml conical polypropylene centrifuge tubes; 1.5 ml; Eppendorf tubes; 0.2 ml thin wall PCR tube; MicroCon 100 (Amicon Cat No. 42412); High speed centrifuge for 15 ml tubes; Clinical centrifuge with horizontal rotor for 50 ml conical tubes; Tissue homogenizer (e.g. Polytron PT1200 with Polytron-Aggregate-Dispergier-und-Mischtechnik 147a Ch6014 #027-30-520-0, Brinkmann Instruments Inc., Westbury, N.Y.); RPE Buffer (Add 4 volumes of ethanol per volume of RPE concentrate supplied in Quiagen Kit0; RW1 Buffer (Supplied in Qiagen Kit) 75% EtOH (Ethanol (100%) 375 ml, and DEPC H2O 125 ml for a total of 500 ml); 10× low T dNTP Mix (25 μL dGTP (100 mM), 25 μL dATP (100 mM), 25 μL dCTP (100 mM), 10 μL dTTP (100 mM), and 415 μL DEPC H2O for a total of 500 μL); 5× First Strand Buffer (Provided with Superscript II); TAE Buffer (50× Tris Acetate Electrophoresis Buffer 20 ml, and DEPC H2O 980 mL for a total of 1000 ml)
If the cells that are used were harvested from tissue culture, the cell pellet is washed twice in DPBS. If the cells that are used were from tissue culture, 1 ml of Trizol was added per 2×107 cells and mixed by shaking. If tissue is being used, 100 mg of frozen tissue is added directly to 4 ml of Trizol, and dissociated by homogenization with a rotating blade tissue homogenizer.
Whatever the source, 2/10 volume of chloroform is added to the cells and shook for 15 seconds, and then allowed to stand for 3 minutes, followed by centrifugation at 12,000×g for 15 minutes at 4° C. The supernatant is taken off and added to a polypropylene tube, while recording the volume of the supernatant.
Then 0.53 volumes of ethanol is slowly added to the supernatant while vortexing, this produces a final ethanol concentration of 35%. The ethanol is added drop by drop and allowed to mix completely with the supernatant before more ethanol is added. If a high local concentration of ethanol is produced, the RNA in that vicinity will precipitate.
The supernatant from an extraction of 2×107 to 1×108 cells is added to an RNeasy maxi column, which is seated in a 50 ml centrifuge tube. The tube is then centrifuged at 2880×g in a clinical centrifuge with a horizontal rotor at room temperature for 5 minutes. The flow-through is then poured back onto the top of the column and centrifuged again. This step is necessary because a significant amount of RNA is not captured by the column matrix in the first pass of the RNA containing solution through the column.
The flow-through is discarded and 15 ml of RW1 buffer is added to the column, followed by centrifugation at 2880×g for 5 minutes. The flow-through is discarded again and then 10 ml of RPE buffer is added, followed again by centrifugation at 2880×g for 5 minutes. Once again, the flow through is discarded and another 10 ml of RPE buffer is added, and the column was centrifuged at 2880×g for 10 minutes.
Next, the column is placed in a fresh 50 ml tube and add 1 ml of DEPC treated water from the kit is added to the column, and the column is allowed to stand for 1 minute. The column is then centrifuged at 2880×g for 5 minutes, and another 1 ml of water is added to the column. The column is allowed to stand for 1 minute, followed by centrifugation at 2880×g for 10 minutes.
Then, 400 μl portions of the column eluate is aliquotted to 1.5 ml Eppendorf tubes, to which 1/10 volume of 3M sodium acetate (pH 5.2) is added, along with 1 ml of ethanol. The tubes are then allowed to stand for 15 minutes, after which they are centrifuged at 12000×g at 4 C for 15 minutes. The pellet is then washed two times in 75% EtOH and stored at −80° C.
The RNA is resuspended at approximately 1 mg/ml in DEPC H2O. It is then concentrated to greater than 7 mg/ml by centrifugation on a MicroCon 100 filter unit, centrifuged at 500×g, checking as necessary to determine the rate of concentration. This step removes many residual, small to medium sized, molecules that inhibit the reverse transcription reaction in the presence of fluorescently derivatized nucleotides. The concentration of RNA in the concentrated sample is then determined by spectrophotometry, and the sample was stored at −80° C.
If an anchored oligo dT primer is used, the primer is annealed to the RNA in the following 17 μl reaction (a 0.2 ml thin wall PCR tube is used so that incubations could be carried out in a PCR cycler):
If an oligo dT(12-18) primer was used, the primer was annealed to the RNA in the following 17 μl reaction:
The incorporation rate for Cy5-dUTP is less than that of Cy3-dUTP, so more RNA is labeled to achieve more equivalent signal from each species.
It is then heated to 65° C. for 10 minutes and cooled on ice for 2 minutes. Then, 23 μl (8 μl of 5× first strand buffer, 4 μl of 10× low T dNTPs mix, 4 μl of Cy5 or Cy3 dUTP (1 mM), 4 μl of 0.1 M DTT, 1 μl of Rnasin (30 u/μl), and 2 μl of Superscript II (200 u/μl)) of reaction mixture containing either Cy5-dUTP or Cy3-dUTP nucleotides is added, mixed well by pipetting and a brief centrifuge spin is used to concentrate it in the bottom of the tube. Superscript polymerase is very sensitive to denaturation at air/liquid interfaces, so be careful to suppress foaming in all handling of this reaction.
It is then incubated at 42° C. for 30 min., after which 2 μl Superscript II is added, making sure the enzyme is well mixed in the reaction volume and incubated at 42° C. for 30-60 min. Then, 5 μl of 0.5M EDTA is added, making sure the reaction is stopped with EDTA before adding NaOH (the next step), since nucleic acids precipitate in alkaline magnesium solutions.
Then, 10 μl 1N NaOH is added and it is incubated at 65° C. for 60 minutes to hydrolyze residual RNA, after which it was cooled to room temperature. The purity of the sodium hydroxide solution used in this step is important. Slight contamination or long storage in a glass vessel can produce a solution that will degrade the Cy5 dye molecule, turning the solution yellow. Some researchers achieve better results by reducing the time of hydrolysis to 30 minutes.
It is then neutralized by adding 25 μl of 1M Tris-HCl (pH 7.5). Then, the labeled cDNA is desalted by adding the neutralized reaction, 400 μl of TE pH 7.5 and 20 μg of human C0t-1 DNA to a MicroCon 100 cartridge. It is then pipetted to mix, and spun for 10 minutes at 500×g. 200 μl TE pH 7.5 is added, and the solution is then concentrated to about 20-30 μl (approximately 8-10 min at 500×g). Alternatively, a smaller pore MicroCon 30 is used to speed the concentration step. In this case, the first wash is centrifuged for approximately 4.5 minutes at 16,000×g and the second (200 μl wash) for about 2.5 minutes at 16,000×g.
It is then recovered by inverting the concentrator over a clean collection tube and spinning for 3 min at 500×g. In some cases, the cy5 labeled cDNA forms a gelatinous blue precipitate that is recovered in the concentrated volume. The presence of this material signals the presence of contaminants. The more extreme the contamination, the greater the fraction of cDNA which will be captured in this gel. Even if heat solubilized, this material tends to produce uniform, non-specific binding to the DNA targets. When concentrating by centrifugal filtration, the times required to achieve the desired final volume are variable. Overly long spins can remove nearly all the water from the solution being filtered. When fluor-tagged nucleic acids are concentrated onto the filter in this fashion, they are very hard to remove, so it is necessary to approach the desired volume by conservative approximations of the required spin times. If control of volumes proves difficult, the final concentration can be achieved by evaporating liquid in the speed-vac. Vacuum evaporation, if not to dryness, does not degrade the performance of the labeled cDNA.
Next, a 2-3 μl aliquot of the Cy5 labeled cDNA is taken for analysis, leaving 18-28 μl for hybridization. This probe is run on a 2% agarose gel (6 cm wide×8.5 cm long, 2 mm wide teeth) in Tris Acetate Electrophoresis Buffer (TAE). For maximal sensitivity when running samples on a gel for fluor analysis, a loading buffer with minimal dye was used and no ethidium bromide is added to the gel or running buffer.
The gel is then scanned on a Molecular Dynamics Storm fluorescence scanner (setting: red fluorescence, 200 micron resolution, 1000 volts on PMT). Successful labeling produces a dense smear of probe from 400 bp to >1000 bp, with little pile-up of low molecular weight transcripts. Weak labeling and significant levels of low molecular weight material indicates a poor labeling. A fraction of the observed low molecular weight material is unincorporated fluor nucleotide.
Next, the fluorescent cDNA had to be hybridized to the microarray. The volume of hybridization solution required is first determined. The rule of thumb is to use 0.033 μl for each mm 2 of slide surface area covered by the cover slip used to cover the array. An array covered by a 24 mm by 50 mm cover slip required 40 μl of hybridization solution. The volume of the hybridization solution is critical. When too little solution is used, it is difficult to seat the cover slip without introducing air bubbles over some portion of the arrayed ESTs, and the cover slip will not sit at a uniform distance from the slide. If the cover slip is bowed toward the slide in the center, there will be less labeled cDNA in that area and hybridization will be non-uniform. When too much volume is applied, the cover slip will move easily during handling, leading to misplacement relative to the arrayed ESTs, and non-hybridization in some areas of the array.
For a 40 μl hybridization, the Cy3 and Cy5 labeled cDNAs are pooled into a single 0.2 ml thin wall PCR tube and the volume is adjusted to 30 μl by either adding DEPC H2O, or removing water in a SpeedVac. If a vacuum device is used to remove water, high heat or heat lamps are not used to accelerate evaporation because the fluorescent dyes could be degraded.
For a 40 μl hybridization the following components are combined:
Arrays and samples can vary somewhat, making it necessary to vary the composition of the hybridization cocktail. In cases where there is residual hybridization to control repeat DNA samples on the array, more C0t-1 DNA was used, as in the High Sample Blocking formulation. When there is diffuse background or a general haze on all of the array elements, more of the non-specific blocker components is used, as in the High Array Blocking formulation.
The components are mixed well by pipetting, heated at 98° C. for 2 minutes in a PCR cycler, cooled quickly to 25° C. and 0.6 ul of 10% SDS is added. It was then centrifuged for 5 min at 14,000×g. The fluor labeled cDNAs have a tendency to form small, very fluorescent, aggregates which result in bright, punctate background on the array slide. Hard centrifugation will pellet these aggregates, preventing introduction to the array.
The labeled cDNA is applied to a 24 mm×50 mm glass cover slip and then touched with the inverted microarray. Applying the hybridization mix to the array and cover slipping it is an operation which requires some dexterity to get the positioning of the cover slip and the exclusion of air bubbles just right. It was helpful to practice this operation with buffer and plain slides before attempting actual samples. The hybridization solution is added to the cover slip first, since some aggregates of fluor remain in the solution and will bind to the first surface they touch.
The slide is then placed in a microarray hybridization chamber, 5 μl of 3×SSC is added to the reservoir, if the chamber provided one, or at the scribed end of the slide and the chamber is sealed. The chamber is submerged in a 65° C. water bath and the slide is allowed to hybridize for 16-20 hours. There are a wide variety of commercial hybridization chambers. It was worthwhile to prepare a mock hybridization with a blank slide, load it in the chamber and incubate it to test for leaks, or drying of the hybridization fluid, either of which cause severe fluorescent noise on the array.
Next, the unbound fluorescent cDNA is washed off. The hybridization chamber is removed from the water bath, cooled and carefully dried off. The chamber is unsealed and the slide is removed. As there may be negative pressure in the chamber after cooling, it is necessary to remove water from around the seals so that it is not pulled into the chamber and onto the slide when the seals are loosened.
The slide is placed, with the cover slip still affixed, into a Coplin jar filled with 0.5×SSC/0.01% SDS wash buffer. The cover slip is allowed to fall from the slide and then removed from the jar with a forceps. The slide is allowed to wash for 2-5 minutes. The slide is transferred to a fresh Coplin jar filled with 0.06×SSC, and allowed to wash for 2-5 minutes. The sequence of washes may need to be adjusted to allow for more aggressive noise removal, depending on the source of the sample RNA. Useful variations are to add a first wash which is 0.5×SSC/0.1% SDS or to repeat the normal first wash twice.
The slide is then transferred to a slide rack and centrifuged at low rpm (700-1000) for 3 minutes in a clinical centrifuge equipped with a horizontal rotor for microtiter plates. If the slide is simply air dried, it frequently acquires a fluorescent haze. Centrifuging off the liquids results in a lower fluorescent background. As the rate of drying can be quite rapid, it is suggested that the slide be placed in the centrifuge immediately upon removal from the Coplin jar.
Image analysis can be performed using DeArray software (Chen, Y., Dougherty, E. R. and Bittner, M. L. Ratio-based decisions and the quantitative analysis of cDNA microarray images, Biomedical Optics 2, 364-374 (1997).
Fifty-six pre-treatment primary neuroblastoma (NB) tumor samples from 49 NB patients with outcome information were obtained retrospectively from 3 sources presenting between 1992-2000 (Table 4). All patients were treated according to local or national guidelines that followed similar protocols, which included “wait-and-see” after surgery or combinations of vincristine, doxorubicin, carboplatin, cisplatin, cyclophosphamide, melphalan and etoposide, depending on the risk factors. All samples were anonymized, and our protocol was deemed exempt from the NIH Multiple Project Assurance.
All samples (except NB1, NB2, NB3, NB4, NB207, NB209 and NB210) were used in the leave-one-out ANN analysis. Samples highlighted in gray are the 21 test samples, and the rest were used for training during the clone optimization procedure. There were 7 replicated samples, marked by the numbers in superscript.
Sample Source: 1=Cooperative Human Tissue Network (CHTN, Ohio, USA); 2=German Cancer Research Center (GCRC); 3=The Children's Hospital at Westmead (CHW, Australia).
INSS=International Neuroblastoma Staging System.
MYCN amplification status: AMP=amplification; NA=not amplified.
Shimada Histology: F=favorable, “−”=not known, UF=unfavorable.
COG risk stratification: H=high-risk; I=intermediate-risk; L=low-risk.
Ave. ANN Vote=average ANN committee votes.
ANN prediction: average ANN vote <0.5=A (alive); >0.5=D (dead).
Clinical Outcome: A=alive without event; D=deceased due to NB disease.
Pre-treatment tumor samples were snap-frozen in liquid nitrogen following removal from the patients. Tumors were diagnosed as NB by local centers experienced in the management of these cancers. Additionally, the 56 samples were confirmed to be NBs by ANNs using the previously-identified NB-specific gene expression profiles, shown in the examples above. Patients were divided into two outcome groups: “good-outcome” group had event-free survival (i.e. neither relapse nor NB progression) for at least 3 years (n=30), and “poor-outcome” died due to NB disease (n=19). The median age for the good-outcome group was 0.9 years (range from 0.1 to 4.6 years) and for the poor-outcome group was 2.8 years (range from 0.8 to 10.5 years) (Table 4).
Total RNA was extracted from the frozen pre-treatment tumor samples using a previously published method (Wei, J. S, and Khan, J. Purification of Total RNA from Mammalian Cells and Tissues. In: D. Bowtell and J. Sambrook (eds.), DNA Microarrays: A Molecular Cloning Manual, pp. 110-119. Cold Spring Harbor, N.Y.: Cold Spring Harbor Laboratory Press, 2002). An Agilent BioAnalyzer 2100 (Agilent, Palo Alto, Calif.) was used to assess the integrity of total RNAs from the tumor samples. Total RNA from seven human cancer cell lines (CHP212, RD, Hela, A204, K562, RDES and CA46) was pooled in equal portions to constitute a reference RNA, which was used in all cDNA microarrray experiments.
Messenger RNA was amplified one round using a modified Eberwine RNA amplification procedure (Sotiriou, C., Khanna, C., Jazaeri, A. A., Petersen, D., and Liu, E. T. Core biopsies can be used to distinguish differences in expression profiling by cDNA microarrays. J Mol Diagn, 4: 30-36, 2002). Next, an indirect fluorescent labeling method was used to label cDNA (Hegde, P., Qi, R., Abernathy, K., Gay, C., Dharap, S., Gaspard, R., Hughes, J. E., Snesrud, E., Lee, N., and Quackenbush, J. A concise guide to cDNA microarray analysis. Biotechniques, 29: 548-550, 552-544, 556 passim., 2000) wherein, aminoallyl-dUTP (Sigma-Aldrich, St. Louis, Mo.) was first incorporated into cDNA in a reverse transcription reaction in which amplified anti-sense RNA was converted into cDNA by Superscript II reverse transcriptase enzyme (Invitrogen, Grand Island, N.Y.) according to the manufacturer's instructions. Second, unincorporated aminoallyl-dUTP was removed with Qiagen PCR purification kits (Qiagen, Valencia, Calif.) according to the manufacturer's instructions. Third, monoreactive-Cye5 or Cye3 dyes (AmershamPharmacia, Piscataway, N.J.) were conjugated with the aminoallyl-dUTP on the cDNA. Fluorescent-labeled cDNA was purified with Qiagen PCR purification kits.
The DNA microrarrays were fabricated from sequence-verified cDNA libraries purchased from Research Genetics (Huntsville, Ala.). The library consisted of a total of 42578 cDNA clones, representing 25933 Unigene clusters (13606 known genes and 12327 unknown ESTs). The cDNA were printed on microarrays using a BioRobotics MicroGrid II spotter (Harvard Bioscience, Holliston, Mass.). Fabrication, hybridization and washing of microarrays were performed as described above in Example 1. Images were acquired by an Agilent DNA microarray scanner (Agilent, Palo Alto, Calif.), and analyzed using the Microarray Suite program, coded in IPLab (Scanalytics, Fairfax, Va.).
Gene expression ratios between the tumor sample RNA and the reference RNA on each microarray were normalized using a pin-based normalization method modified from Chen et al (Chen, Y., Dougherty, E. R., and Bittner, M. L. Ratio-based decisions and the quantitative analysis of cDNA microarray images. Biomedical Optics., 2: 364-374, 1997). In order to include only high quality data in the analysis, the quality of each individual cDNA spot was calculated according to Chen et al (Chen, Y., Kamat, V., Dougherty, E. R., Bittner, M. L., Meltzer, P. S., and Trent, J. M. Bioinformatics, 18:1207-1215, 2002). Next, spots with an average quality, across all samples, of less than 0.95 were excluded from the analysis. There were 37920 (90.3%) clones that passed this quality filter.
Feed-forward resilient back-propagation multi-layer perceptron ANNs (coded in Matlab, The Mathworks, Natick, Mass.) with 3 layers were used. The three layers were: an input layer of the top 10 principal components (PCs) of the data (
Average ANN committee votes were used to classify samples, and 0.5 was used as the decision boundary for ANN prediction throughout the study. The ideal vote was 0 for the good-outcome group (alive), and 1 for the poor-outcome group (dead). The ANNs were trained and used to predict NB outcomes using an 8-fold cross validation scheme in all analyses similar as described above.
A leave-one-out prediction strategy was performed first (
Visualization of all 56 NB samples using principal component analysis (PCA) of all quality-filtered 37920 clones revealed NB samples generally grouped according to their clinical outcomes (
The average ANN vote, the ANN predicted outcome of the patient, and the clinical outcome of the NB patients is also summarized in Table 6 below.
Survival length was calculated for the 49 unique NB patients from date of diagnosis until date of death or last follow-up as appropriate. The probability of survival and significance were calculated using the Kaplan-Meier and Mantel-Haenszel methods, respectively (Kaplan, E. and Meier, P. Non-Parametric Estimation from Imcomplete Observations. J. Am. Stat. Assoc., 53: 457-481, 1958; and Mantel, M. Evaluation of Survival Data and Two New Rank Order Statistics Arising in its Consideration. Cancer Chem. Rep., 50: 163-170, 1966).
The Kaplan-Meier curves demonstrated that patients with poor and good gene expression signatures as identified by the ANNs had significantly different survival probabilities (P<0.0001 see
The Cox proportional hazards model (Cox, D. Regression Models and Life Tables. J. Royal Stat. Soc. (B), 34: 187-202, 1972) was used to determine the hazard ratios and confidence intervals (Matthews, D. E. and Farewell, V. T. Using and Understanding Medical Statistics. In, 3rd edition edition, pp. 150-160. Basel: Karger, 1996) for survival between the dichotomized groups of patients, and was used to assess which factors were jointly significant in the association with survival for the 24 high-risk patients (Cox et al.).
The Cox model parameters (bi) were converted to hazard ratios by computing exp(bi), where exp(a)=2.7183a. The 95% confidence interval for the hazard ratio was computed as [exp(biL), exp(biH)] where biL=bi−1.96 [estimated standard error (bi)] and biH=bi=1.96 [estimated standard error (bi)] (Matthews et al.). In this study, the hazard ratio indicates the risk associated with NB-caused death while being in a greater-risk category compared to that of being in the lower-risk category. Using the procedure described by Simon and Altman, a likelihood ratio test was used to assess for importance of the microarray prediction after adjusting for standard prognostic factors such as MYCN amplification, age, or stage (Simon, R. and Altman, D. G. Statistical aspects of prognostic factor studies in oncology. Br J Cancer, 69: 979-985, 1994).
The Cox proportional hazard ratio for the risk of death associated with the poor signature was 16.1 (95% confidence interval: 4.6-56.9, P<0.0001), which was higher than those of all the other risk factors we examined (stage, MYCN amplification, age) except Shimada Histology, and was comparable to the COG risk stratification (Table 7 and
The Cox proportional hazards model was used to calculate all hazard ratios (H.R.) and confidence intervals (C.I.). P-values were calculated using the Mantel-Haenszel method.
To identify the optimal set of genes that results in the minimum classification errors a gene minimization procedure was performed as exemplified above. All 56 samples were randomly partitioned into training (n=35) and testing sets (n=21) and the training set was used for the gene selection algorithm.
From this, it was observed that the top 24 ANN-ranked clones resulted in the minimal classification error (
The top-ranked clone for each gene was taken and this set of genes was used as a minimal gene set. When the overall variance of these genes was visualized using PCA on all 56 samples a clearer separation of the poor- from the good-outcome samples (in comparison to that observed with the PCA for all 37920 clones) (
The ANN was then recalibrated with the 35 training samples using the expression ratios for the 19 genes and correctly predicted the outcomes for 5/5 poor-outcome and 15/16 good-outcome patients in the independent test set, corresponding to a sensitivity of 100% and a specificity of 94% for predicting poor-outcome (
The Kaplan-Meier curves demonstrated that patients with good and poor signatures based on the expression ratios of the 19 genes had significantly different survival probabilities (P<0.0001 see
The top 24 ANN-ranked clones represent 19 UniGene clusters of 12 known genes and 7 ESTs, as DLK1, ARHI, PRSS3, and SLIT3 were represented by multiple cDNA clones (
The expression data presented in
For the gene minimization procedure the 7 replicate samples were placed in the training set and the remaining samples were then randomly partitioned into training (n=35) and testing (n=21) sets (
This example shows that the gene expression signatures for both the full set of genes and the minimized gene set can separate those patients currently stratified as high-risk according to their survival status.
For the 24 high-risk patients in examples 3 and 4, the Kaplan-Meier curves demonstrated that ANNs were able to further partition these patients according to their clinical outcomes using all 37920 quality-filtered clones (P=0.0067), as well as the top 19 ANN-ranked genes (P=0.0005) (
To determine if the gene expression signatures could provide additional predictive power over the conventional risk factors, a Cox model was created using age, stage and MYCN amplification excluding the ANN prediction results. The model showed that MYCN amplification (P=0.0064) was the only significant factor (i.e., P<0.05, see
An ANN-based method for predicting the outcome of patients with NB has been developed that can use the expression profiles of only 19 genes that provides a significant improvement in prediction over the current known risk factors. Moreover, it has been found that the most important advantage of the approach was the ability to further partition COG (Children's Oncology Group) stratified high-risk patients, in particular those without MYCN amplification, into two subgroups according to their survival status. The ability to predict the outcome of individual patients with high-risk NB at initial diagnosis using gene expression signatures has major clinical implications, since approximately 70% of the patients in this group (about 50% of all NB patients) succumb to the disease. Firstly, patients that are identified to have a poor signature, i.e. predicted to die if given conventional therapy, may directly benefit from the newer therapeutic strategy trials that are currently under investigation by the cooperative study groups such as COG. Secondly, since treatment-related death rates have been reported to be as high as 23%, it may be possible to design future dose intensity reduction trials to minimize therapy-related morbidity and mortality for the high-risk patients who have a good signature. An example of such a patient in the latter category is NB14 (stage 4, MYCN-amplified) who despite his high-risk status experienced event free survival for >3 yrs as was predicted by our ANNs. Although the survival rate for patients with COG stratified low-risk disease is 95%, our approach may identify the few patients predicted to have a poor outcome by the ANNs who may benefit from more aggressive therapy. For instance, although case NB18 was classified as low-risk (based on stage 2 and MYCN not amplified), our ANNs predicted this sample as poor-outcome, and this patient died within 1.5 years after diagnosis. These results indicate the potential utility of using the approach for individualized management of patients with cancer
Since there was some overlap in the expression levels of the top 19 ANN-ranked genes between the prognostic groups, the prospect of identifying a single gene that can accurately predict outcome is unlikely. Thus, a combinatorial approach using several genes and artificial machine learning algorithms provided for accurate outcome prediction. MYCN amplification is an established marker for high stage and poor outcome, and plays a role in the aggressive phenotype of NB tumors. Our analysis confirmed MYCN as a prognostic marker (ranked 16 out of 19), however, the median expression level of this gene was similar in the two groups, in agreement with previous reports that MYCN expression levels are not consistently correlated with survival in patients with non-amplified tumors. MYCN amplification is currently the only molecular marker utilized for risk stratification, however, it cannot be used as the sole risk predictor, as only 22% of NB patients have this molecular trait.
Of the 19-predictor genes, 8 out of the 12 known genes have been previously reported to be expressed in neural tissue. Of these, 5 were up regulated in the poor-outcome group (DLK1, PRSS3, ARC, SLIT3, and MYCN) and 3 were down regulated (CNR1, ROBO2, and BTBD3). DLK1 (ranked number 1) is the human homologue of the Drosophilia delta gene and is expressed by neuroblasts in the developing nervous system as well as in neuroblastoma. It is a transmembrane protein that activates the Notch signaling pathway, which has been shown to inhibit neuronal differentiation. Additionally, ARC, MYCN, and SLIT3 are also expressed during neural development. The higher expression levels of these genes in the poor-outcome tumors suggest a more aggressive phenotype characterized by a less differentiated state, reminiscent of proliferating and migrating neural crest progenitors. Up regulation of the neuron axon repellant gene, SLIT3, was observed with the down regulation of one of its receptors, ROBO2, in the poor-outcome group suggesting the possibility that these neuroblastoma cells secrete a substrate to repel connecting axons and potentially prevent differentiation.
Of additional interest, the ARHI gene, which maps to 1p31, is a maternally imprinted tumor suppressor gene implicated in ovarian and breast cancer, possibly through methylation silencing, and is among the down regulated genes for the poor-outcome group. A further study of its role in tumorigenesis as a potential tumor suppressor gene in NB is warranted particularly because of its proximity to the 1p36 region, which is frequently deleted in poor-outcome NB patients.
We noted the absence of three previously reported prognostic related genes, TRKA, TRKB and FYN, amongst our 19 genes. Unfortunately, TRKA was not on the microarrays, and TRKB and FYN were not ranked within the top 500 clones by ANNs. At this point, the predictive role of TRKA, TRKB or FYN is not conclusive, and none are currently utilized to guide therapy.
In this study a small subset of 19 predictor genes was identified from a pool of 25933 unique genes with the majority of genes showing a greater than two fold average differential expression between good- and poor-outcome tumors. This small number of genes can be provide cost-effective clinical assays for outcome prediction. In addition, the products of 3 genes (DLK1, SLIT3, and PRSS3) are secreted proteins, indicating the utility of these proteins as serum markers for prognosis.
In this data set, our ANN-based method provided a significant improvement in prediction over the current risk factors in patients with NB. Moreover, an advantage of the approach is the ability to further partition COG stratified high-risk patients, in particular those without MYCN amplification, into two subgroups according to their survival status. This approach would allow physicians to tailor therapy for each individual patient according to their molecular profile, with the prospect of improving clinical outcome and survival rates in patients with neuroblastoma.
The above specification, examples and data provide a complete description of the manufacture and use of the composition of the invention. Since many embodiments of the invention can be made without departing from the spirit and scope of the invention, the invention resides in the claims hereinafter appended. All references identified herein are hereby incorporated by reference.
This application is a continuation of PCT/US2005/027660, filed Aug. 3, 2005, which claims priority to U.S. Provisional Patent Application Ser. No. 60/598,728, filed Aug. 3, 2004 and claims the benefit of that application under 35 U.S.C. §119(e), which applications are hereby incorporated by reference.
This invention was developed with the support of the Department of Health and Human Services. The Government of the United States of America has certain rights in the invention disclosed and claimed herein below.
| Filing Document | Filing Date | Country | Kind | 371c Date |
|---|---|---|---|---|
| PCT/US2005/027660 | 8/3/2005 | WO | 00 | 4/28/2009 |
| Publishing Document | Publishing Date | Country | Kind |
|---|---|---|---|
| WO2006/026051 | 3/9/2007 | WO | A |
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