WO2009123634A1 - Systèmes et procédés pour prédire une réponse d'échantillons biologiques - Google Patents

Systèmes et procédés pour prédire une réponse d'échantillons biologiques Download PDF

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WO2009123634A1
WO2009123634A1 PCT/US2008/059176 US2008059176W WO2009123634A1 WO 2009123634 A1 WO2009123634 A1 WO 2009123634A1 US 2008059176 W US2008059176 W US 2008059176W WO 2009123634 A1 WO2009123634 A1 WO 2009123634A1
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model
spline
markers
univariate
physiological response
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PCT/US2008/059176
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English (en)
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Debopriya Das
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Regents Of The University Of California
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Priority to PCT/US2008/059176 priority Critical patent/WO2009123634A1/fr
Priority to US12/333,192 priority patent/US20090177450A1/en
Priority to PCT/US2008/086473 priority patent/WO2009076551A2/fr
Publication of WO2009123634A1 publication Critical patent/WO2009123634A1/fr

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/30—Unsupervised data analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B50/00—ICT programming tools or database systems specially adapted for bioinformatics
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression

Definitions

  • Embodiments relate to genomic technologies using spline functions that predict physiological responses of cells. For example, responses of cancer cells to specific medications and/or treatments may be predicted based on adaptive linear spline analyses.
  • a method for predicting a physiological response of a patient to a treatment comprising: providing a sample physiological response for each of a plurality of training samples to the treatment; providing a quantification value of a marker for each of the plurality of training samples; determining a predictive model relating the sample physiological responses to the quantification values, the model comprising a spline function; and predicting a physiological response of a biological sample to the treatment using the model.
  • a system for relating quantification values of markers to physiological response comprising an input component configured to receive input data for each of a plurality of samples, the input data comprising a physiological response to a treatment and a quantification value of a marker in the sample; a univariate model generator configured to determine a univariate model relating the physiological response to the quantification value using a spline-based analysis; and an output device configured to output one or more variables or equations related to the univariate model.
  • a method for identifying a marker influencing a physiological response of a sample comprising: providing a physiological response for each of a plurality of training samples to the treatment; providing a value of each of a plurality of markers for each of the plurality of training samples; determining a plurality of univariate models, each model relating the physiological responses to values of one of the plurality the marker, each model comprising a spline function; and identifying a marker influencing the physio logicial response based on the plurality of univariate models.
  • Figure 1 shows a process for developing a model of a response to a therapeutic treatment.
  • Figure 2 shows a schematic of the hierarchical modeling approach.
  • Univariate models, ⁇ f x (x, ) ⁇ are constructed for each dataset at the first level of the hierarchy; multivariate models, [F x (x l 5 x 2 ,K ) ⁇ , that combine the univariate predictors are built for each dataset separately at the next level; the final predictor of response, H(Jc, ⁇ , ⁇ g t ⁇ , ⁇ p t ⁇ ), which integrates all multivariate models from various platforms is obtained at the final level of hierarchy.
  • Figure 3 shows a system for determining a physiological prediction.
  • Figure 4 shows an adaptive linear spline fits to simulated data sets with (a) linear variation, and 2-class structures where (b) neither class has a significant internal variation, (c) only one class has internal variation, and (d) both classes have internal variation.
  • Figure 5 shows results of simulations. The predictive accuracy of different univariate tests for various types of underlying models: (a) two classes with different constant log(GI 50 ) in each class, (b) linear correlation with expression, (c) two classes, one class with constant log(GI 50 ) and the other with linear variation, (d) two classes, each with a different linear correlation. Results are displayed for four different tests: t-test (diamonds), linear fit (circles), single linear spline fit (x's) and adaptive spline fit (squares). The left panel (left axis) shows the goodness of fit (discrimination for t-test) for the best marker for each of the tests, reflecting its predictive power.
  • the right panel shows the similarity between the expression profile of the best marker for each test and that of the original marker used to build the model.
  • Figure 6 shows 5-FU induced apoptosis in colon cancer cells,
  • (c) Leave-one- out cross-validation accuracy of the multivariate model using adaptive linear splines. Equation of the trendline: 0.55 + 0.32x (p 6.9e-08).
  • Figure 7 shows sensitivity of breast cancer cells to Lapatinib. Measured GI 50 profile of 40 breast cancer cell-lines to Lapatinib. Cell-lines with positive ERBB2 status are shown with the unfilled bars.
  • Figure 8 shows spline models of sensitivity to Lapatinib.
  • Log(GI 50 ) (bars, left y-axis) and predicted class score (black curve, right y- axis) of cell- lines in the training set.
  • the maximum GI 50 of the predicted sensitive class (left of dashed line) is lower than the minimum GI 50 of the predicted resistant class (right of dashed line), indicating clear separation characteristic of classification.
  • Figure 9 shows ingenuity analysis of significant mRNA markers of response to Lapatinib.
  • the most significant network shown below, has ERBB2 as a major node. The shading indicate the p-value significance from low to high.
  • the network is associated with 6 significant pathways (p ⁇ 0.05): axonal guidance signaling, ephrin receptor signaling, protein ubiquitination, PPAR ⁇ /RXR ⁇ activation, VEGF signaling and p53 signaling.
  • Figure 10 shows leave-one-out cross-validation error (LOOCV) for model size selection. Plots of predicted vs measured log(GI 50 ) in LOOCV calculation of model size selection in weighted voting approach for (a) mRNA expression, (b) DNA copy number and (c) protein expression datasets.
  • LOOCV leave-one-out cross-validation error
  • Figures 12A-B shows the progression- free survival in 49 ERBB2 positive tumors treated with Lapatinib plus Paclitaxel and 28 ERBB2 positive tumors treated with Paclitaxel plus placebo.
  • methods and systems that use splines to predict the magnitude of response of cells to various treatments and also to classify cancer samples (e.g., into sensitive and resistant classes) in an unsupervised manner.
  • these methods or systems may be used to predict the efficacy of a treatment for a specific person/patient, cancer type or cell line.
  • a hierarchical modeling scheme may be used to integrate profiles from different types of molecular datasets. Methods and systems disclosed herein may provide a generalizable framework for predictive modeling of complex genetic dependencies of diverse physiological responses.
  • Figure 1 shows one process 100 for developing a model of a response to a therapeutic treatment.
  • Process 100 beings at step 105 with the collection of a plurality of samples.
  • the samples are obtained from patients and typically comprise a diseased cell or tissue.
  • the sample may comprise a cancer cell or tissue from a tumor.
  • Samples may be collected across a plurality of patients. In some instances, all patients have been diagnosed with a similar or the same disease or condition (e.g., breast cancer), while in other instances, they have not. Control samples may be collected from patients who have not been diagnosed with a disease or condition to be studied or who are otherwise healthy.
  • the samples comprise a panel of cell lines. This panel may be comprised of cell- lines specific to an organ, e.g.
  • this panel may comprise of cell- lines from diverse organs, e.g. NCI-60, which includes a panel of sixty cancer cell lines of diverse lineage (lung, renal, colorectal, ovarian, breast, prostate, central nervous system, melanoma and hematological malignancies).
  • NCI-60 which includes a panel of sixty cancer cell lines of diverse lineage (lung, renal, colorectal, ovarian, breast, prostate, central nervous system, melanoma and hematological malignancies).
  • Process 100 continues at step 110 with an analysis of each of the samples based on a plurality of putative markers.
  • the putative markers may comprise different types of marks, such as mRNA expression, protein expression, microRNA expression, CpG methylation, and DNA amplification.
  • step 110 comprises the determination of molecular profiles of each of the samples.
  • Each of the sampled may be analyzed based on a plurality of putative markers within each type of sample.
  • the number of putative markers is greater than about 20, 50, 100, 500, 1000, 5000 or 10,000.
  • the number of molecular predictors e.g.
  • a quantification value (such as an expression level or amplification value) of each marker (such as an mRNA strand, protein, microRNA, or DNA strand) may be determined for each sample. Techniques and systems to measure expression levels are well known in the art. For example, mRNA levels may be monitored using Affymetrix Ul 33 A arrays, and protein levels may be measured using western blot assays.
  • Figure 2 shows an example in which N samples are analyzed based on DNA amplification, mRNA expression and protein expression.
  • the amplification of a specific DNA strand, the mRNA expression for a specific mRNA strand, and the protein expression for a specific protein for the ith sample are represented as C 1 , g, and p,.
  • Figure 2 shows only one c, g and p data set, a number of other c, g and p data sets are typically determined based on DNA, mRNA and proteins.
  • the process need not execute all the steps shown in Figure 2. For instance, if there is exactly one data set available (e.g. mRNA expression data), only first and second steps may be executed. In some embodiments, only the first step may be executed.
  • a physiological response is determined for each of the samples.
  • the physiological response may comprise a binary indication or a magnitude of response.
  • each sample is contacted with a compound or a drug.
  • the sample may be categorized as being sensitive or resistant (a binary indication) to the compound or drug.
  • a quantitative assessment of the effect of the compound or drug on the sample is performed. For example, a GI 50 value (a concentration of the compound or drug that causes 50% growth inhibition) or a sensitivity value (equal to the - log(GI 50 )) may be determined for each sample. Techniques to determine such quantitative assessments are well known in the art. For example, a dose response curve may be generated for each sample using an assay that measures cell viability, such as the CellTiter Glo® Luminescent Cell Viability assay, which may then be used to estimate GI 50 for the sample.
  • Process 100 continues at step 120 with the determination of a plurality of univariate models using spline analysis.
  • Each univariate model may be based on one of the plurality of putative markers.
  • functions relating the physiological responses to putative markers are fit with splines.
  • a spline is defined as a piecewise polynomial function separated at point called knots.
  • the spline comprises a linear spline, wherein the spline has a degree of one. Linear splines are linear above a knot, and zero below it. Additionally, linear splines provide a complete set of basis functions, and thus, can facilitate comprehensive modeling of the response profiles.
  • Fitting with splines may include identification of optimal partitions and fitting a function (e.g., a linear function) within each partition.
  • the partition may, in effect, separate samples based on their class identity.
  • the dependence of the physiological response on the putative marker may vary between the classes, but since the fitted function is continuous, this difference may thereby be determined (learnt) in a single optimization determination.
  • univariate functions Jc(C 1 ), J g (g,) and J p (P t ) are determined based on physiological responses and the DNA amplification data C 1 , mRNA expression data g,, or protein expression data/?, respectively.
  • the spline may comprise an adaptive spline.
  • the adaptive splines can simultaneously account for class information and magnitude of response within a single framework.
  • the spline analysis may provide superior fitting and/or better predictions as compared to supervised classification or linear regression analyses.
  • An adaptive spline comprises at least one un- fixed knot. That is, the position of the knot is determined based on (e.g., fit to) the data.
  • Adaptive splines can provide a flexible framework to model a variety of responses ranging from bimodal distributions to more continuous distributions. If the spline model has no knots, then it is a linear model.
  • model has one knot and the slope of the line is zero in one partition, then the model is equivalent to a single linear spline. If the model has two knots and the slopes of the lines are zero in two exterior partitions (but non-zero slope in the interior partition), then it is the same as a classification model.
  • x represents the appropriate predictor variable: logarithm of expression (mRNA or protein) or DNA amplification.
  • ⁇ 0 is the intercept and ⁇ k 's are the slopes.
  • the function h k (x) is defined as:
  • the algorithm enumeratively searches for the best location of knots. Model parameters may then be estimated by minimizing the residual sum of squares.
  • the spline comprises a non-adaptive spline, in which the position of the knot/s are fixed and do not depend on the data.
  • the spline may also be partially adaptive, such that the positions of one or more knots are fixed while the positions of one or more other knots are not fixed, or such that the positions of one or more knots are constrained.
  • the response data may be modeled as sum of linear splines, where the predictor variables are markers such as DNA amplification, mRNA expression or protein expression levels.
  • the adaptive splines model containing M internal knots, ... ⁇ M is written as ( ⁇ o and ⁇ M + I are the boundary values of x):
  • x represents the appropriate predictor variable.
  • ⁇ 0 is the intercept and ⁇ k 's are the slopes.
  • the function h k (x) is defined as:
  • the first and last diagonal elements of A , and first and last elements of b are computed as:
  • each univariate model comprises a sum of linear splines, where the predictor variable is the specific molecular profile of the potential marker.
  • an algorithm may identify location of knots by, for example, minimizing the residual sum of squares.
  • the number of knots is predetermined, while in other embodiments, the number of knots is determined based on the data.
  • LOOCV leave-one-out cross-validation method
  • Process 100 continues at step 125 with the identification of significant markers based on the univariate models.
  • significant markers are identified based on how well the spline could fit a function relating the physiological response to the marker. For example, a p-value may be used to determine significant markers.
  • LOOCV error of the spline fit is used to determine whether the marker is significant. A value associated with the fit (e.g., a p-value or LOOCV error) may be compared to a fixed and/or relative threshold.
  • the significant markers are clustered.
  • the markers may be clustered by an unsupervised or a supervised process.
  • the clustering may comprise hierarchical clustering.
  • the number of clusters is predetermined, while in others it is not. For example, it may be determined that the markers will be clustered into one resistant class and one sensitive class. Identification characteristics of the classes may be determined before or after the clustering.
  • the markers may be clustered into a resistant and sensitive class, or the markers may be clustered into two classes, which are later determined to correspond to resistant and sensitive classes.
  • univariate response predictors are determined.
  • Each univariate model can be used to make a single prediction of the physiological response of a biological sample not used in the generation of the univariate model.
  • the univariate model may be used to predict cell growth inhibition or apoptosis based on the expression of a specific protein.
  • the predictor of cell viability or apoptosis of a new sample may be predicted based on the protein expression in the cells of the sample.
  • univariate predictors are determined for all putative markers.
  • univariate predictors are determined for significant markers. Thus, there may be a set of predictors, each predictor associated with a different marker (and thus with a different univariate model).
  • a commercially available database of biochemical functions, pathways and analogously defined entities is one such example, though not limiting, is the Ingenuity database (http://www.ingenuity.com/).
  • Process 100 continues at step 140 with the formation of a multivariate model for each type of marker (e.g., mRNA expression, protein expression, microRNA expression, CpG methylation, or DNA amplification).
  • the multivariate model may be formed by combining univariate predictors.
  • the multivariate model comprises weighted averages of the univariate models. All univariate predictors, all significant univariate predictors or a subset of the univariate predictors may be used in developing the multivariate model.
  • the weights in the weighted voting scheme may be determined based on a characteristic of a fit, such as a correlative fit or a spline fit, used to obtain the univariate model.
  • the weight associated with each univariate predictor may be proportional to a magnitude of a correlation between the physiological response and the corresponding marker.
  • the weight may be associated with a coefficient or significance of a spline fit used to obtain the univariate model.
  • the weights may be proportional to the logarithm of the p-value of the univariate spline model.
  • multivariate models F c , F G , and F P are determined based on the corresponding univariate models for each of DNA amplification, mRNA expression and protein expression, respectively.
  • D indicates a data-type
  • g indicates a prioritized univariate predictor for this data-type
  • log(G/ 50 ) D S is the predicted value of log(G/ 50 ) based on the feature g
  • N G the total number of predictors used
  • w D g indicates the normalized weight for this univariate feature for data type D, being proportional to the magnitude of correlation with response:
  • the model size, N G may be determined by minimizing the LOOCV error.
  • a multivariate model comprises a fit based on the significant feature variables. This fit may be independent from equations, variables and/or fits of the univariate models. In some embodiments, the fit includes some parameters from the univariate models but learns other parameters based on the data. In one example, knots of splines from the univariate models are used, but polynomial equations used in the splines are learned based on the data. In another example, once significant markers are identified, a spline equation may be used to identify a new multivariate relationship between the physiological response and the significant markers.
  • a spline equation may be used to identify a new multivariate relationship between the physiological response and the significant markers.
  • a fit used in determination of a multivariate model may be based on any appropriate fitting technique, such as a least squares fitting technique.
  • Process 100 continues at step 145 with the integration of the multivariate models across marker types.
  • One example of an integrated model across data types is:
  • N M total number of data-types.
  • the normalized weight W D is proportional to the average log of/?- values, and is calculated as:
  • wTM g is the average log(p- value) of the univariate predictors included in the model for this data type D.
  • the model H predicts a response based on DNA amplification, mRNA expression and protein expression for a sample.
  • the model is obtained by integrating the multivariate models F c , F G , and F P .
  • a physiological prediction is made using a model described herein.
  • the physiological prediction may include a prediction as to the response (e.g., the same as or similar to the response determined in step 115) of a new biological sample (e.g., cell type, cancer or an alive or deceased patient).
  • Quantification values e.g., expression, concentration, or amplification
  • the samples collected in step 105 were breast cancer cell- lines, and the response determined in step 115 was cell viability in response to a drug.
  • Quantification values from a new sample collected from another cell-line or a patient diagnosed with breast cancer may then be determined and the cell viability response to the drug may be predicted using the model.
  • the samples collected in step 105 may be collected from patients diagnosed with a plurality of cancer types, and the response determined in step 115 was cell viability in response a treatment.
  • Quantification values from a new sample may then be collected from another patient diagnosed with cancer (of a new type or of one the plurality of types) and the cell viability response to the treatment may be predicted using the model.
  • the physiological prediction may include a classification.
  • a new sample may be determined to be resistant or sensitive to a treatment. For example, if the sample comprises expression of certain markers below identified knots in spline equations, the sample may be determined to be resistant to a treatment.
  • a classification is predicted for a sample of the samples collected in step 105. For example, a specific cell line may be classified as resistant to a treatment.
  • the physiological prediction may include a prediction related to a patient.
  • the physiological prediction may estimate survival time, likelihood of survival, or probability of survival within a time period.
  • the prediction may be related to the probability of experiencing an adverse event or an interaction of treatments.
  • the physiological prediction may include a prediction related to treatment efficacy.
  • a testing sample is obtained from a person who is or may be suffering from a specific disease. Quantification values of the testing sample are determined, and a physiological response is predicted based on a model described herein. This prediction may be used to predict how effective a treatment would be for the person who provided the testing sample.
  • the testing sample is obtained from a specific cell line or from a patient suffering from a specific disease, and the predicted physiological response may then be used to predict how effective a treatment would be for the cell line or against the specific disease.
  • the physiological prediction may include an efficacy value.
  • a treatment may be effective in eliminating 50% of a specific tumor (e.g., for a specific person).
  • a specific tumor e.g., for a specific person
  • the physiological prediction related to treatment efficacy may comprise a value associated with cell viability and/or apoptosis or survival, or even related to metabolism, e.g. glycolytic index value.
  • the prediction may comprise a binary result, e.g. sensitive or resistant to a drug.
  • the physiological prediction may include a risk probability assessment or a diagnosis.
  • the samples collected in step 105 may be collected from subjects suffering from a disease and healthy subjects or from subjects suffering from multiple strains of a disease.
  • a spline-based method may naturally separate samples from the two groups. Thus, analysis of specific quantification values in a new sample may indicate whether a patient suffers from a specific disease.
  • the physiological prediction may include identification of specific markers.
  • the specific markers may include significant markers and/or those determined to be indicative of a disease, a classification (e.g., of a cell, tumor or cancer), or a treatment response.
  • the physiological prediction may include a treatment.
  • the treatment may be one that is predicted to be effective in treating a disease or condition.
  • a plurality of models is determined, each relating a response to a different treatment to quantification values. By determining quantification values in a new sample, a single treatment among the different treatments may be identified as being most probable to be effective.
  • the treatment may be one previously used in determining responses of the samples in step 115 or may be a new treatment. For example, based on one or more models, properties of treatments indicative of efficacy may be identified and effective treatments may be predicted.
  • the physiological prediction may include a number, a percent, a classification, or a description.
  • the prediction may include a cell viability number predicted to occur in response to a treatment.
  • the prediction may include a percent (e.g., of cell viability) predicted to occur in response to a treatment relative to no treatment.
  • the prediction may include a number indicating a predicted response relative to responses or predicted responses of other samples.
  • the prediction may include a discrete response, such as binary or trinary responses. In one such example, the prediction may be either resistant or sensitive.
  • the prediction may include confidence intervals.
  • a computer-readable medium or computer software comprises instructions to perform one or more steps of process 100 (e.g., steps 120-150).
  • the software may comprise instructions to output (e.g., display, print or store) the physiological prediction.
  • one or more steps shown in Figure 1 are not included in process 100.
  • step 130 may be excluded from process 100.
  • additional steps are included in process 100.
  • the steps are arranged differently than shown in Figure 1. Multiple steps may be combined (e.g., steps 125 and 135 may be combined into one step), and/or single steps may be separated into a plurality of steps.
  • process 100 allows the integration of profiles from diverse molecular datasets. Additionally, while other analyses use only a subset of the samples for predicting physiological response, process 100 accounts for responses from all samples, thereby leading to nonlinear response signatures and facilitating tissue-specific analysis. A subset of samples may also be used in the process 100,
  • Process 100 provides a number of advantages over supervised classification, in which samples are segregated into sensitive and resistant classes based on training data, as process 100 provides a quantitative value predicted for the physiological response. This magnitude can provide useful information, which is often lost upon discretizing the data into various classes.
  • fewer markers are needed to predict physiological responses as compared to other methods. For example, fewer markers may be needed in models described herein as compared to models that do not account for response magnitude but instead rely on classification. Fewer markers also make their clinical deployment very cost-effective.
  • supervised classification methods one needs to select at least one response threshold to label samples in training set with their different class-types, e.g. sensitive versus resistant for drug response.
  • spline-based methods described herein can be applied to smaller datasets than other methods (e.g., those that exclude data from the training set), as the spline- based methods can accurately model all data points together, i.e. without filtering out any sample. For example, these methods may be used to study responses of specific tumor types.
  • a system 300 (e.g., a computer system) is provided to make a physiological prediction about a treatment response.
  • the system may comprise an input component 305.
  • the input component may comprise any input device such as a keyboard, a mouse, or a memory storage device (e.g., a disk, a compact disc, a DVD, or a USB drive).
  • the input component may be configured to receive data related to physiological responses (e.g., to one or more treatments) of a plurality of samples.
  • the input component 305 may be configured to receive data related to quantification values of a plurality of samples.
  • a user inputs mRNA expression values, DNA amplification values, microRNA expression values, CpG methylation values, protein expression values for each of a plurality of samples using a keyboard.
  • the user may also input cell viability value/s associated with a treatment (e.g., for a plurality of drug concentrations).
  • the input component 305 may be configured to receive data related to training samples and/or to test samples.
  • the system 300 may comprise a response parameterization component 310.
  • the response parameterization component 310 determines the efficacy of a treatment for each sample (e.g., each training sample) based on data input at the input component 305, such as a plurality of cell viability or apoptosis values.
  • the GI 50 may be determined based on cell viability values associated with different drug concentrations.
  • the system 300 does not include a response parameterization component 310.
  • the component 310 may not be included if the user may input a GI50 value at the input component 305.
  • the system 300 may comprise a univariate model generator 315.
  • the univariate model generator 315 determines of a plurality of univariate models using spline analysis, the univariate model being any univariate model as described herein.
  • the univariate model generator 315 determines the univariate models based on the data input at input component 305 and optionally the efficacy values from efficacy determination component 310.
  • Each univariate model may predict a value of a physiological response (e.g., the physiological response that was input at the input component 305) based on a single marker (e.g., one of the markers that was input at the input component 305).
  • the system 300 may comprise a marker clustering component 320.
  • the marker clustering component 320 may cluster markers input at input component 305 by unsupervised, hierarchical clustering or any other process as described herein.
  • the marker clustering component 320 may or may not use univariate models from univariate model generator 315.
  • the system 300 may comprise a univariate predictor 325.
  • the univariate predictor 325 may determine univariate response predictions based on univariate models from the univariate model generator 315 and/or based on the marker clusters from marker clustering component 320 by a process described herein. For example, each univariate models associated with a plurality of markers can be used to make a single prediction of the physiological response of a sample not used in the generation of the univariate models.
  • the system 300 may comprise a multivariate model generator 330.
  • the multivariate model generator 330 may determine a multivariate model as described herein.
  • the multivariate model may be formed by combining univariate predictions from the univariate predictor 325 using weighted averages of the univariate response predictions.
  • the system 300 may comprise a multivariate model integrator 335.
  • the multivariate model integrator 335 may integrate multivariate models from the multivariate model generator 330 by a process described herein.
  • the system 300 may comprise a physiological response predictor 340.
  • the physiological response predictor 340 may determine a physiological prediction as described herein by a process as described herein. For example, the physiological response predictor 340 may predict a cell viability of a new sample based on an integrated model from the multivariate model integrator 355.
  • the system 300 may comprise an output device 345.
  • the output device may comprise any appropriate output device, such as a display screen or a printer.
  • the output device may be configured to store output onto a data storage medium.
  • the output device may output models or model components (e.g., coefficient, significance, or fit values), such as those from one or more univariate models generated by univariate model generator 315, one or more multivariate models generated by multivariate model generator 330, or one or more integrated models generated by the multivariate model integrator 335.
  • the output device may output a physiological prediction determined by the physiological predictor 340.
  • one or more components or connections shown in Figure 3 are not included in system 300. In some embodiments, additional components or connections are included in system 300. In some embodiments, the components are connected differently than shown in Figure 3.
  • the system 300 may comprise a memory.
  • the system 300 may be connected to a network, such as the internet.
  • the system 300 may comprise a computer system including a CPU and a memory such as the ROM.
  • Such memory medium may store a program or software for executing steps of process 100.
  • the memory medium can be composed of a semiconductor memory such as a ROM or a RAM, or an optical disk, a magnetooptical disk or a magnetic medium. It may also be composed of a CD-ROM, a floppy disk, a magnetic tape, a magnetic card or a non-volatile memory card.
  • supervised classification t- test
  • regression methods viz. linear regression, single linear spline fit and adaptive linear splines.
  • the first three are parametric tests, while adaptive splines constitute a non-parametric test.
  • the average log(GI 50 ) was used as a threshold for demarcating the sensitive and resistant classes. Because of the noise, average log(GIso) can be different from the midpoint, which is the actual threshold in the pure model. Expression data from these two groups were used to compute the t statistic.
  • the ratio RSS or ⁇ g ⁇ na /RSSf ⁇ na ⁇ was recorded, which is greater than 1 when the fitted model is closer to the final input log(GI 50 ) (i.e. with noise) than the original model (i.e. without noise).
  • False discovery rate was adjusted to ensure ⁇ 2 false discoveries (approximately) throughout this work.
  • the average p-value of the top 50 genes using linear splines is 2e-04, while for linear regression, it is le-03, again highlighting that adaptive splines can model significantly more variation in the data than the linear methods previously used (Table 1).
  • the top predictor, PDZDI l belongs to this set of novel markers. Review of this marker list reveals several molecules (CLN5, CTNS, LYAG) involved in lysosomal processing of macromolecules, indicating possible metabolic determinants of cellular outcome after 5-FU treatment. Some of these genes have been previously associated with cancers: GAA and PTK2 are biomarkers of colonic neoplasms, while RPSl 5 A is known to participate in hepatocellular carcinoma. Functional enrichment analysis of these 48 genes revealed 17 GO terms as significant (p ⁇ 0.1), noteworthy among which are macromolecule metabolism, cellular organization and biogenesis, and establishment and maintenance of chromatin architecture (Table 2).
  • the most strongly correlated N G univariate predictors were combined using a weighted voting scheme, as described herein.
  • the response of a sample is computed from the weighted average of the predicted magnitude of response from each univariate feature, where the weights of features are proportional to the strength of their univariate correlation. This differs from other methods, where weighted vote of class-type of response was used instead.
  • the predictive accuracy of the multivariate model is shown using via LOOCV.
  • one cell-line was left out, the model was trained on the remaining 29 cell- lines, and the trained model was used to predict apoptosis on the left-out cell-line. This process was repeated for each of 30 cell-lines.
  • the predictive power of the 48 significant genes at the multivariate level was examined using LOOCV analysis. To seek the upper bound on the accuracy of weighted voting, a different number of predictors (NG) was used at each iteration, the number being that that led to the best performance for that specific iteration.
  • the dose response curves for a total of 40 breast cancer cell lines were determined using the CellTiter GIo assay, which measures cell viability.
  • the response curves were used to estimate the GI50 value for each cell line, which were then used to perform the correlative analyses to predict sensitivity ( ⁇ -log(GI 50 )).
  • the GI 50 response data displayed a wide dynamic range (spanning >3 logs) and, as expected, strongly correlated with protein levels of ERBB2, the conventional marker of response to Lapatinib (Figure 7).
  • a training set of 30 cell-lines was randomly selected. The training set was then used to learn the molecular markers and the computational model for sensitivity prediction. The remaining 10 cell- lines were used to test the accuracy of the model.
  • ERBB2 the canonical marker of response to Lapatinib (REF)
  • the ERBB2 amplicon (Chr 17q21) and phosphor-ERBB2 are also the top predictors in DNA amplification data and western blot data respectively.
  • These analyses show the same ERBB2 specificity as observed in clinical trials and in other in vitro experiments.
  • the positive associations of ERBB2 with sensitivity were expected because it is a principal target of Lapatinib.
  • genes encoded in the ERBB2 amplicon e.g. GRB7
  • Transmembrane receptor protein tyrosine kinase signaling pathway and intracellular receptor-mediated signaling pathway are among the significant terms, as expected for an inhibitor of ERBB2 and EGFR.
  • Enriched networks and pathways in this gene set were also searched for against the Ingenuity database (http://www.ingenuity.com/). Again, the most significant network had ERBB2 as a major node ( Figure 9a). This network was found to be associated with 5 major signaling pathways: protein ubiquitination, p53 signaling, PPARa/RXRa activation, VEGF signaling and axonal guidance signaling ( Figure 9b). In addition, ephrin receptor signaling pathway also emerged as significant (Table 5).
  • Multivariate models To obtain a multivariate model that combines inputs from all three molecular datasets, an integrative approach was used. For a multivariate model for a given data-type, the weighted voting method was used, as in Example 2. A challenge in weighted voting approach is how to determine the model size, i.e. the number of terms in the model. Previous implementations have, sometimes, involved subjective choices. Here the model size was selected to minimize the LOOCV error, which leads to a unique model. The procedure is, otherwise, similar to that described in Example 2. The optimal model size emerged to be 2 for mRNA expression profiles, 1 for DNA copy number profiles and 3 for protein expression profiles (Figure 10).
  • the inputs here are the multivariate models for each data type, and the weight for each data type is proportional to the average correlation of top N G markers used in the step above.
  • the maximum GI50 of the (predicted) sensitive class is lower than the minimum GI 50 of the resistant class, indicating clear separation characteristic of appropriate classification.
  • the average of these two response values at the separatrix can then be used as a threshold for discriminating the resistant and sensitive cells.
  • the voting method can be extended such that the weights in the model are learnt from the data at each step, rather than being predetermined by univariate correlation. This is accomplished by using a least squares fit, which also facilitates learning the significant feature variables (molecular markers). The knots of splines are retained as the same as that obtained from the univariate analysis, however. Variable selection is done here in a stepwise manner. The optimal size of the model is determined by minimizing LOOCV error. The coefficients of the model as obtained via least squares fit are then the weights of each predictor.
  • the predicted GI50 was found to be correlated with the measured GI50 with a Pearson's correlation of 0.89, corresponding to a /?-value of 4.8e-4, which is comparable to the result obtained with weighted voting.
  • ERBB2 emerged as significant in all 3 datasets.
  • the amplicon CTC-329F6 on chr7p22 was also significant in the DNA copy number data set.
  • MARS multivariate adaptive regression splines
  • principal components regression and multivariate linear regression were compared to the spline-based approach described above.
  • MARS uses linear splines as basis functions, but employs a greedy search strategy.
  • the model is built using a combination of forward addition and backward elimination search strategies.
  • a prioritized set of candidate markers was used as input to MARS, where prior itization was done at the univariate level using adaptive linear splines.
  • PCR method was implemented as described in Mariadason, J. M., Arango, D., Shi, Q., Wilson, A. J., Corner, G. A., Nicholas, C, Aranes, M. J., Lesser, M., Schwartz, E. L. & Augenlicht, L. H. (2003) Cancer Res 63, 8791-812.
  • markers were prioritized using linear regression for the respective dataset. Principal component analysis was performed on their corresponding molecular profiles. Linear regression was performed using the derived principal components.
  • PCR models for various datasets were combined using a linear model.
  • a 6 transcript predictor of response to Lapatinib was tested using in vitro measurements. Specifically, the predictor was used to stratify patient response to Lapatinib in the EGF30001 trial of Lapatinib plus Paclitaxel vs. Paclitaxel plus placebo. This predictor was comprised of two genes (ERBB2 and GRB7) for which increased transcription levels were associated with sensitivity in vitro and four genes (CRK, AC0T9, FLJ31079, and DDX5) for which increased transcription levels were associated with resistance in vitro.
  • a spline-based algorithm was used to identify the mRNA markers that are predictive of glycolytic index. Specifically, the baseline mRNA profiles were correlated with the logarithm of glycolytic index values (GIVs) using an adaptive splines framework. In this approach, both magnitude and class-type of response are simultaneously modeled. Although the GIVs were used as input to the algorithm, i.e. without binarization, the method could automatically identify two-class like partition in the data. This is revealed by performing an unsupervised hierarchical clustering of the mRNA expression levels of the top 100 predictors identified by the spline-based algorithm. The 8 cell-lines in the left hand partition have generally high GIVs, while the 5 cell-lines to the right have low GIVs.
  • GIVs glycolytic index values

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Abstract

Selon l’invention, des modes de réalisation portent sur des technologies génomiques utilisant une analyse de type spline adaptative qui prédit des réponses de cellules cancéreuses. Par exemple des réponses de cellules cancéreuses à des médicaments spécifiques et/ou traitements spécifiques peuvent être prédites sur la base d'analyses de type spline linéaires adaptatives.
PCT/US2008/059176 2007-12-12 2008-04-02 Systèmes et procédés pour prédire une réponse d'échantillons biologiques WO2009123634A1 (fr)

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WO2007082099A2 (fr) * 2006-01-11 2007-07-19 Genomic Health, Inc. Marqueurs d'expression de gène pour pronostic colorectal de cancer
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