DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
In the amendment filed 10/10/2025 the following occurred: Claims 1, 3, and 20 were amended. Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-20 are drawn to methods, which is/are statutory categories of invention (Step 1: YES).
Independent claim 1 recites for each patient of a first plurality of patients: retrieving respective functional data and respective clinical data corresponding to the respective patient, wherein: the respective functional data includes initial cell viability and cell viability in response to exposure to one or more drug therapies; and the respective clinical data includes patient information over time; and forming a respective feature vector comprising the respective functional data and the respective clinical data corresponding to the respective patient; using at least a first subset of the feature vectors to predict individual patient response to a first drug therapy, wherein the first model includes to form an aggregate prediction for the first drug therapy; and storing the first model in a database for subsequent use in predicting patient response to the first drug therapy.
Independent claim 20 recites performed at identifying a patient having a first disease condition; retrieving a first model built to predict response to a first drug therapy for treating the first disease condition, wherein: the first model according to data for a plurality of previous patients; each previous patient provided medical data during drug therapy that includes one or more drugs; and at least a first subset of the previous patients underwent one or more drug therapies that include the first drug therapy; receiving medical data for the patient, the medical data including functional data and clinical data corresponding to features used by the first model, wherein: the functional data includes initial cell viability; and the clinical data includes patient information over time; extracting, from the medical data, features corresponding to the features used by the first model; forming a feature vector comprising the extracted features; applying the first model to the feature vector to generate an aggregate prediction of the patient's response to the first drug therapy; and providing the predicted patient's response to the first drug therapy.
The respective dependent claims 2-19, but for the inclusion of the additional elements specifically addressed below, provide recitations further limiting the invention of the independent claim(s).
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity, as reflected in the specification, which states that the invention is “to providing predicted patient response to drug therapies and more specifically to systems and methods for predicting a patient's response to chemotherapy drug therapies” (see: specification paragraph 2). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The present claims cover certain methods of organizing human activity because they address a problem where “the efficacy of specific drugs is assessed based on disease progression in diseased sites before and after treatments…a time consuming and financially costly approach that can take up to weeks, if not months, before returning results” (see: specification paragraph 3), which presents “a need for tools that can accurately calculate and predict a patient's in-vivo response to different drug therapies (e.g., a likelihood that a patient will have a positive response to dmg therapies), including single agent drug therapies and multiple agent dmg therapies” (see: specification paragraph 6), and which the present invention addresses with a “solution [] to train predictive models to provide predicted patient response to different dmg therapies (including single agent dmg therapies and multiple agent drug therapies). For each patient, functional, genetic, and clinical data is used to provide predicted in-vivo response in a cost effective and timely manner” (see: specification paragraph 7). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).
This judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including an “a computing device having one or more processors and memory storing one or more programs configured for execution by the one or more processors:…to train a first model…trained…” (claim 1), “train a second model…trained…” (claim 8), “trained…trained…trained… a plurality of decision trees for use by a random forest …trained…” (claim 9), “used to train the first model…” (claim 13), “trained…” (claim 15), “a computing device having one or more processors and memory storing one or more programs configured for execution by the one or more processors:…trained…trained…has been trained…the first trained model includes a plurality of decision trees…trained…trained…trained…using a random forest of the plurality of decision trees in the first trained model…” (claim 20), which are additional elements that are recited at a high level of generality (e.g., the “a computing device having one or more processors and memory” performs functions to predict patient responses to a first drug therapy through no more than a statement than that said functions are performed “by the one or more processors” as “configured for execution” according to “programs” stored in said memory; the “trained” model(s) is configured though no more than a statement than that data has been used to “train” said model(s), and that the trained model(s) “include” a plurality of decision trees such that a random forest of the decision trees can be “us[ed]” in the training) such that they amount to no more than mere instruction to apply the exception using generic computer components. See: MPEP 2106.05(f).
The combination of these additional elements is no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s). Accordingly, the claims are directed to an abstract idea(s) (Step 2A Prong Two: NO).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea(s) into a practical application, using the additional elements to perform the abstract idea(s) amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using generic components cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See: MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea(s). The originally filed specification supports this conclusion:
Paragraph 9, where “In accordance with some implementations, a method for building models for predicting patient response to drug therapies executes at an electronic device with a display, one or more processors, and memory. For example, the electronic device can be a smart phone, a tablet, a notebook computer, a desktop computer, an individual server computer, or a server system (e.g., running in the cloud)…”
Paragraph 30, where “In accordance with some implementations, a method of predicting patient response to one or more drug therapies executes at an electronic device with a display, one or more processors, and memory. For example, the electronic device can be a smart phone, a tablet, a notebook computer, a desktop computer, a server computer, a system of server computers, or a wearable device such as a smart watch…”
Paragraph 61, where “Figure 1A illustrates training one or more predictive model(s) 132 using patient data 120 from a plurality of patients 110 (e.g., previous patients, patients who have previously undergone one or more drug therapies, or patients who are currently being treated with one or more drug therapies). The patient data 120 is input into a machine learning engine 130 configured to train (e.g., produce, generate) one or more predictive models 132 for predicting a patient's response to one or more drug therapies.”
Paragraph 65, where “…The machine learning engine 130 then uses the feature vectors to train the one or more predictive models 132 so that the predictive models 132 can predict a patient's response to one or more drug therapies.”
Paragraph 70, where “Figure 2A is a block diagram illustrating a computing device 200, corresponding to a computing system, which can train and/or execute predictive model(s) 132 in accordance with some implementations. Various examples of the computing device 200 include a desktop computer, a laptop computer, a tablet computer, a server computer, a server system, a wearable device such as a smart watch, and other computing devices that have a processor capable of training and/or running predictive model(s) 132. The computing device 200 may be a data server that hosts one or more databases, models, or modules, or may provide various executable applications or modules. The computing device 200 typically includes one or more processing units (processors or cores) 202, one or more network or other communications interfaces 204, memory 206, and one or more communication buses 208 for interconnecting these components…”
Paragraph 71, where “The memory 206 includes high-speed random-access memory, such as DRAM, SRAM, DDR RAM, or other random-access solid-state memory devices; and may include nonvolatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. In some implementations, the memory 206 includes one or more storage devices remotely located from the processors 202. The memory 206, or alternatively the non-volatile memory devices within the memory 206, includes a non-transitory computer-readable storage medium…a machine learning engine 130 configured to train the predictive model(s) 132 using the patient data 120 (including functional data 122, clinical data 124, and genetic data 126) as inputs for training the predictive model(s) 132…”
Paragraph 81, where “Referring to Figure 3B, the machine learning engine 130 uses the training data 310 and the testing data 312 to train the predictive model of the plurality of predictive models 132. The machine learning engine 130 uses the training data 310 to train (e.g., generate) a predictive model in-training 320 and uses the testing data 312 to test and refine the predictive model in-training 320 in order to generate (e.g., train) the predictive model 132-m. Once the predictive model 132-m is trained, the predictive model 132-m can be used to predict a patient's response to a specific drug therapy.”
Paragraph 159, where “In some implementations, the first model 132-1 is a first type of machine learning model and the second model 132-2 is a second type of machine learning model that is different from the first type of machine learning model. For example, the first model 132-1 is a random forest that includes a plurality of decision trees and the second model 132-2 is a neural network that includes a plurality of layers. In another example, the first model 132-1 is a random forest that includes a plurality of decision trees and the second model 132-2 is a support vector machine.”
Further, the concepts of receiving or transmitting data over a network, such as using the Internet to gather data, and storing and retrieving information in memory have been identified by the courts as well-understood, routine, and conventional activities. See: MPEP 2106.05(d)(II).
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea(s) with routine, conventional activity specified at a high level of generality in a particular technological environment. Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea(s) (Step 2B: NO).
Dependent claim(s) 2-19, when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea(s) without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2, 4-11, and 13-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 2009/0177450 to Gray in view of U.S. Patent Application Publication 2020/0107781 to Navalgund.
As per claim 1, Gray teaches a method for building models for predicting patient response to drug therapies (Fig. 1; para 34, 'FIG. 1 shows one process 100 for developing a model of a response to a therapeutic treatment'), performed at a computing device (system 300) having one or more processors and memory storing one or more programs configured for execution by the one or more processors (Fig. 3; [0069), 'a system 300 (e.g., a computer system) is provided to make a physiological prediction about a treatment response. As shown in FIG. 3, 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).'; [0079), '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.'):
for each patient of a first plurality of patients (Fig. 1; [0034), 'FIG. 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. For example, 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.'):
retrieving respective functional data and respective clinical data corresponding to the respective patient ([0051), 'where D indicates a data-type, g indicates a prioritized univariate predictor for this data-type, log(Gl50)Dg is the predicted value of log(Gl50) based on the feature g, NG the total number of predictors used, and wDg indicates the normalized weight for this univariate feature for data type D, being proportional to the magnitude of correlation with response'; (0052), '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'; [0120), 'Multivariate models. To obtain a multivariate model, as a start, the most strongly correlated NG univariate predictors were combined using a weighted voting scheme, as described herein. Here, 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.'; [0155), 'The Kaplan-Meyer plots of progression free survival showed that the 6-gene predictor set stratified 53 patients in the intermediate group into 45 patients predicted to be sensitive compared to 8 patients predicted to be resistant (FIG. 18 a). The median survival was longer for the patients predicted to be sensitive, but shorter for the patients to be resistant.'; functional and clinical data is used to train the model),
wherein: the respective functional data includes initial cell viability and cell viability in response to exposure to one or more drug therapies (Fig. 1; [0036), 'At step 115 of process 100, a physiological response is determined for each of the samples. The physiological response may comprise a binary indication or a magnitude of response ... 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(R) Luminescent Cell Viability assay, which may then be used to estimate Gl50 for the sample.'; (0055), 'At step 150 of process 100, 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) of specific, significant or all markers in the sample may be determined. In a first example, 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.'; a response curve for cell viability must include the initial cell viability and then the curve after exposure to the drug therapy over time); and
the respective clinical data includes patient information over time (Fig. 18a shows an example of clinical data for the progression of the disease over time based upon different categories of patients; [0155), 'The Kaplan-Meyer plots of progression free survival showed that the 6-gene predictor set stratified 53 patients in the intermediate group into 45 patients predicted to be sensitive compared to 8 patients predicted to be resistant (FIG. 18 a). The median survival was longer for the patients predicted to be sensitive, but shorter for the patients to be resistant.'); and
forming a respective feature vector comprising the respective functional data and the respective clinical data corresponding to the respective patient ([0051), 'where D indicates a data-type, g indicates a prioritized univariate predictor for this data-type, log(Gl50)Dg is the predicted value of log(Gl50) based on the feature g, NG the total number of predictors used, and wDg indicates the normalized weight for this univariate feature for data type D, being proportional to the magnitude of correlation with response'; (0052), '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'; [0120), 'Multivariate models. To obtain a multivariate model, as a start, the most strongly correlated NG univariate predictors were combined using a weighted voting scheme, as described herein. Here, 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.'; [0155), 'The Kaplan-Meyer plots of progression free survival showed that the 6-gene predictor set stratified 53 patients in the intermediate group into 45 patients predicted to be sensitive compared to 8 patients predicted to be resistant (FIG. 18 a). The median survival was longer for the patients predicted to be sensitive, but shorter for the patients to be resistant.'; various feature vectors values are formed using the data);
using at least a first subset of the feature vectors to train a first model to predict individual patient response to a first drug therapy ([0066). '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.'; [0070), '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. For example, the Gl50 may be determined based on cell viability values associated with different drug concentrations.'; [0120), 'Multivariate models. To obtain a multivariate model, as a start, the most strongly correlated NG univariate predictors were combined using a weighted voting scheme, as described herein. Here, 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.'; [0121), 'The predictive accuracy of the multivariate model is shown using via LOOCV. Here, 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.'; [0122), 'The dose response curves for a total of 40 breast cancer cell lines were determined using the CellTiter Glo assay, which measures cell viability. The response curves were used to estimate the Gl50 value for each cell line, which were then used to perform the correlative analyses to predict sensitivity (=-log(Gl50)). The Gl50 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 (FIG. 7). To comprehensively determine the predictive markers of sensitivity to Lapatinib, from cell-line panel, 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.'; features in the training data are used to train the individual patient response predictive model); and
storing the trained first model in a database for subsequent use in predicting patient response to the first drug therapy ([0063), '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.'; (0076), '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.'; [0077), '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.'; [0079), '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 trained predictive model is stored in memory, which is broadly considered a database, for use in predicting a patient response based upon the patient's input).
Gray fails to specifically teach wherein the first model includes a plurality of decision trees for use by a random forest to form an aggregate prediction for the first drug therapy; however, Navalgund teaches one or more machine learning models (e.g. random forest) may be deployed to operate by constructing a multitude of decision tree, and training data may also include patient response data to particular medications (see: Navalgund, paragraph 44, 130, 186, and 251).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the training and model as taught by Gray to include one or more machine learning models (e.g. random forest) may be deployed to operate by constructing a multitude of decision tree, and training data may also include patient response data to particular medications, so that during the testing or use phase, the ML model may be able to predict which medications a patient will respond to, as taught by Navalgund, with the motivation of, during the testing or use phase, the ML model may be used to predict which medications a patient will respond to (see: Navalgund, paragraph 251).
As per claim 2, Gray and Navalgund teach the invention as claimed, see discussion of claim 1, and further teach:
for each patient of the first plurality of patients: retrieving respective genetic data corresponding to the respective patient (Fig. 1; (0035), 'Process 100 continues at step 11 0 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.'),
wherein: the respective genetic data includes information obtained from DNA and RNA extracted from cells obtained from a diseased site of the respective patient (Fig. 1; (0034), 'FIG. 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. For example, 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.'; (0035), '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. In some embodiments, 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.'; genetic data related to both DNA and mRNA in the cancerous cells can be determined from the analysis); and
the respective feature vector further includes the respective genetic data corresponding to the respective patient ((0051 ), 'where D indicates a data-type, g indicates a prioritized univariate predictor for this data-type, log(Gl50)Dg is the predicted value of log(Gl50) based on the feature g, NG the total number of predictors used, and wDg indicates the normalized weight for this univariate feature for data type D, being proportional to the magnitude of correlation with response'; [0052), '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'; (0120), 'Multivariate models. To obtain a multivariate model, as a start, the most strongly correlated NG univariate predictors were combined using a weighted voting scheme, as described herein. Here. 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.'; [0155), 'The Kaplan-Meyer plots of progression free survival showed that the 6-gene predictor set stratified 53 patients in the intermediate group into 45 patients predicted to be sensitive compared to 8 patients predicted to be resistant (FIG. 18 a). The median survival was longer for the patients predicted to be sensitive, but shorter for the patients to be resistant.'; various feature vectors values are formed using the various data types, such as the DNA and mRNA values found during the analysis, and applied for each patient's data).
As per claim 4, Gray and Navalgund teach the invention as claimed, see discussion of claim 2, and further teach:
wherein the respective genetic data includes information regarding at least 100 genes ([0035], 'Each of the sampled may be analyzed based on a plurality of putative markers within each type of sample. In some embodiments, the number of putative markers is greater than about 20, 50, 100, 500, 1000, 5000 or 10,000. Notably, the number of molecular predictors (e.g. genes) is typically very large (P-104) compared to the number of samples available in training sets (typically, N=10-50 for tissue specific cancers). In some embodiments, the ratio of the number of putative markers compared to the number of samples is greater than about 1, 2, 5, 1 0, 20, 50, 100, 200, 500, or 1000.'; [0113], 'Expression data was obtained for a set of 1000 genes and 30 cell-lines by sampling from a normal distribution with micro =0, and s=2. These parameters were held fixed. A gene g in the top half of the gene list by variance was randomly selected. The expression level of this gene, (Eg}, was then used to generate a model for log (Gl50).').
As per claim 5, Gray and Navalgund teach the invention as claimed, see discussion of claim 1, and further teach:
wherein: the respective functional data includes information obtained from live cells extracted from a tumor site of the respective patient ([0034], 'FIG. 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. For example, 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.'; [0055), '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).'; tumor cell samples from live patients may be taken); and
the respective functional data includes one or more of:
physical integrity of the live cells;
metabolic activity of the live cells ([0058], '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.'; [0118), 'Review of this marker list reveals several molecules (CLN5, CTNS, L YAG) involved in lysosomal processing of macromolecules, indicating possible metabolic
determinants of cellular outcome after 5-FU treatment.'; Table 2 shows various metabolic markers related to apoptosis; [0138], 'Example 4 Metabolism in Breast Cancer Cells: A spline-based algorithm was used to identify the mRNA markers that are predictive of glycolytic index.');
mechanical activity of the live cells;
mitotic activity of the live cells; and
proliferation capacity of the live cells for a predetermined cellular phenotype.
As per claim 6, Gray and Navalgund teach the invention as claimed, see discussion of claim 1, and further teach:
wherein: the respective functional data includes information obtained from live cells extracted from a tumor site of the respective patient ((0034), 'FIG. 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. For example, 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.'; (0055), '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).'; tumor cell samples from live patients may be taken); and
the respective functional data includes one or more of: a size distribution of the live cells; a shape distribution of the live cells; a distribution of the live cells with respect to expression of a biomarker ((0017], '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.'; (0030), '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. In some embodiments, step 11 0 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 ... 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.'; (0089), 'a method for identifying a cancer patient suitable for treatment with a 4-anilinoquinazoline kinase inhibitor is provided, the method comprising: (a) detecting the expression level of one or more genes described in Tables 7a and 7b in a sample from the patient, and (b) comparing the expression level of said gene(s) from the patient with the expression level of said gene(s) in a normal tissue sample or a reference expression level (such as the average expression level of said gene(s) in a cell line panel or a cancer cell or tumor panel, or the like).'; [0113), 'Expression data was obtained for a set of 1000 genes and 30 cell-lines by sampling from a
normal distribution with micro =0, and s=2. These parameters were held fixed. A gene g in the top half of the gene list by variance was randomly selected.'; [0121), 'From the improved computational performance, it is anticipated that the set of 48 genes constitutes a more robust set of biomarkers of 5-FU induced apoptotic response compared to previous reports.'); and phenotypic features of the live cells.
As per claim 7, Gray and Navalgund teach the invention as claimed, see discussion of claim 1, and further teach:
wherein: the respective functional data includes information obtained from live cells extracted from a tumor site of the respective patient ((0034), 'FIG. 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. For example, 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.'; [0055), '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).'; tumor cell samples from live patients may be taken);
the first drug therapy includes at least a first drug ([0036), 'At step 115 of process 100, a physiological response is determined for each of the samples. The physiological response may comprise a binary indication or a magnitude of response. In some embodiments, 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. In some instances, a quantitative assessment of the effect of the compound or drug on the sample is performed. For example, a Gl50 value (a concentration of the compound or drug that causes 50 percent growth inhibition) or a sensitivity value (equal to the-log(Gl50)) may be determined for each sample.');
the respective functional data includes one or more of: a measure of a potency of one or more first drugs for inhibiting a predetermined biochemical function ([0055), '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) of specific, significant or all markers in the sample may be determined. In a first example, 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. In a second example, 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.'; (0058), 'The physiological prediction may include a prediction related to treatment efficacy. In some embodiments, 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. In other embodiments, 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. For example, it may be predicted that a treatment may be effective in eliminating 50 percent of a specific tumor (e.g., for a specific person). As another example, it may be predicted that there is a 60 percent probability that a treatment will eliminate a specific tumor type (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. In some embodiments, the prediction may comprise a binary result, e.g. sensitive or resistant to a drug.'; the efficacy value can related to a cell viability and/or apoptosis or survival, which is a value of how potent the drug is in stopping the biological functioning of a cell);
a maximum cytotoxicity of the one or more first drugs;
an area under a curve (AUC) determined using data corresponding to cell viability in response to dosage of the one or more first drugs; and
the one or more first drugs includes at least the first drug ([0036), 'At step 115 of process 100, a physiological response is determined for each of the samples. The physiological response may comprise a binary indication or a magnitude of response. In some embodiments, 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. In some instances, a quantitative assessment of the effect of the compound or drug on the sample is performed. For example, a Gl50 value (a concentration of the compound or drug that causes 50 percent growth inhibition) or a sensitivity value (equal to the-log(Gl50)) may be determined for each sample.').
As per claim 8, Gray and Navalgund teach the invention as claimed, see discussion of claim 7, and further teach:
for each patient of a second plurality of patients (Fig. 1; (0034), 'FIG. 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. For example, 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.'; [0061), 'The physiological prediction may include a treatment. The treatment may be one that is predicted to be effective in treating a disease or condition. In one instance, 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.'; a second group of patients can be patients receiving a second treatment option for the disease):
retrieving respective functional data and respective clinical data corresponding to the respective patient of the second plurality of patients ([0051], 'where D indicates a data-type, g indicates a prioritized univariate predictor for this data-type, log(Gl50)Dg is the predicted value of log(Gl50) based on the feature g, NG the total number of predictors used, and wDg indicates the normalized weight for this univariate feature for data type D, being proportional to the magnitude of correlation with response'; [0052), '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'; [0120), 'Multivariate models. To obtain a multivariate model, as a start, the most strongly correlated NG univariate predictors were combined using a weighted voting scheme, as described herein. Here, 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.'; (0155), 'The Kaplan-Meyer plots of progression free survival showed that the 6-gene predictor set stratified 53 patients in the intermediate group into 45 patients predicted to be sensitive compared to 8 patients predicted to be resistant (FIG. 18 a). The median survival was longer for the patients predicted to be sensitive, but shorter for the patients to be resistant.'; functional and clinical data is used to train the model for all groups of patients),
wherein: the respective functional data corresponding to the respective patient of the second plurality of patients includes initial cell viability and cell viability in response to exposure to one or more drug therapies (Fig. 1; [0036], 'At step 115 of process 100, a physiological response is determined for each of the samples. The physiological response may comprise a binary indication or a magnitude of response ... 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(R) Luminescent Cell Viability assay, which may then be used to estimate Gl50 for the sample.'; (0055], 'At step 150 of process 100, 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) of specific, significant or all markers in the sample may be determined. In a first example, 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.'; (0061], 'The physiological prediction may include a treatment. The treatment may be one that is predicted to be effective in treating a disease or condition. In one instance, 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.'; a response curve for cell viability must include the initial cell viability and then the curve after exposure to the drug therapy over time);
the respective functional data corresponding to the respective patient of the second plurality of patients data includes one or more of: a measure of a potency of one or more second drugs for inhibiting a predetermined biochemical function ((0055), '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) of specific, significant or all markers in the sample may be determined. In a first example, 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. In a second example, 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.'; (0058], 'The physiological prediction may include a prediction related to treatment efficacy. In some embodiments, 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. In other embodiments, 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. For example, it may be predicted that a treatment may be effective in eliminating 50 percent of a specific tumor (e.g., for a specific person). As another example, it may be predicted that there is a 60 percent probability that a treatment will eliminate a specific tumor type (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. In some embodiments, the prediction may comprise a binary result, e.g. sensitive or resistant to a drug.'; the efficacy value can related to a cell viability and/or apoptosis or survival, which is a value of how potent the drug is in stopping the biological functioning of a cell);
a maximum cytotoxicity of the one or more second drugs; and
an area under a curve (AUC) determined using a plot of cell viability in response to dosage of the one or more second drugs;
the one or more second drugs differs from the one or more first drugs by at least one drug; the one or more second drugs includes a second drug that is different from the first drug ([0033), 'methods and systems are provided that use splines to predict the magnitude of response of cells to various treatments'; [0034], 'FIG. 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. For example, 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.'; [0036], 'At step 115 of process 100, a physiological response is determined for each of the samples. The physiological response may comprise a binary indication or a magnitude of response. In some embodiments, 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.'; [0057], 'The physiological prediction may include a prediction related to a patient. For example, 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.'; [0058], 'The physiological prediction may include an efficacy value. For example, it may be predicted that a treatment may be effective in eliminating 50 percent of a specific tumor (e.g., for a specific person). As another example, it may be predicted that there is a 60 percent probability that a treatment will eliminate a specific tumor type (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. In some embodiments, the prediction may comprise a binary result, e.g. sensitive or resistant to a drug.'; [0061], 'the physiological prediction may include a treatment. The treatment may be one that is predicted to be effective in treating a disease or condition. In one instance, 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.'; a second treatment can include different drugs from a first treatment);
the respective clinical data corresponding to the respective patient of the second plurality of patients includes patient information over time (Fig. 18a shows an example of clinical data for the progression of the disease over time based upon different categories of patients; (0155]. 'The Kaplan-Meyer plots of progression free survival showed that the 6-gene predictor set stratified 53 patients in the intermediate group into 45 patients predicted to be sensitive compared to 8 patients predicted to be resistant (FIG. 18 a). The median survival was longer for the patients predicted to be sensitive, but shorter for the patients to be resistant.'; patient data over time is used for all groups of patients);
forming a respective feature vector comprising the respective functional data and respective clinical data corresponding to the respective patient of the second plurality of patients ([0051], 'where D indicates a data-type, g indicates a prioritized univariate predictor for this data-type, log(Gl50)Dg is the predicted value of log(Gl50) based on the feature g, NG the total number of predictors used, and wDg indicates the normalized weight for this univariate feature for data type D, being proportional to the magnitude of correlation with response'; [0052], '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'; [0120], 'Multivariate models. To obtain a multivariate model, as a start, the most strongly correlated NG univariate predictors were combined using a weighted voting scheme, as described herein. Here, 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.'; [0155], 'The Kaplan-Meyer plots of progression free survival showed that the 6-gene predictor set stratified 53 patients in the intermediate group into 45 patients predicted to be sensitive compared to 8 patients predicted to be resistant (FIG. 18 a). The median survival was longer for the patients predicted to be sensitive, but shorter for the patients to be resistant.'; various feature vectors values are formed using the data for all groups of patients);
using at least a second subset of the feature vectors corresponding to the respective patient of the second plurality of patients to train a second model to predict individual patient response to a second drug therapy that is different from the first drug therapy ([0061], 'the physiological prediction may include a treatment. The treatment may be one that is predicted to be effective in treating a disease or
condition. In one instance, 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.'; (0066), '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.'; [0070], '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. For example, the Gl50 may be determined based on cell viability values associated with different drug concentrations.'; [0120], 'Multivariate models. To obtain a multivariate model, as a start, the most strongly correlated NG univariate predictors were combined using a weighted voting scheme, as described herein. Here, 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.'; [0121], 'The predictive accuracy of the multivariate model is shown using via LOOCV. Here, 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.'; [0122], 'The dose response curves for a total of 40 breast cancer cell lines were determined using the CellTiter Glo assay, which measures cell viability. The response curves were used to estimate the Gl50 value for each cell line, which were then used to perform the correlative analyses to predict sensitivity (=-log (Gl50)). The Gl50 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 (FIG. 7). To comprehensively determine the predictive markers of sensitivity to Lapatinib, from cell-line panel, 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 1 O cell-lines were used to test the accuracy of the model.'; features in the training data are used to train the individual patient response predictive model for all of the different treatment options); and
storing the trained second model in a database for subsequent use in predicting patient response to the second drug therapy, wherein the second drug therapy is distinct from the first drug therapy and includes at least the second drug ([0061], 'the physiological prediction may include a treatment. The treatment may be one that is predicted to be effective in treating a disease or condition. In one instance, 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.'; (0063], '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.'; (0076]. '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.'; (0077), '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 multivariatA mnciAI integrator 335. The output device may output a physiological prediction determined by the physiological predictor 340.'; (0079), '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 trained predictive model is stored in memory, which is broadly considered a database, for use in predicting a patient response based upon the patient's input for all of the different treatment options).
As per claim 9, Gray and Navalgund teach the invention as claimed, see discussion of claim 8, and further teach:
wherein: storing the trained first model and the trained second model in a database includes storing the trained first model and the trained second model in a database for subsequent use in predicting patient response to a third drug therapy that includes at least the first drug of the first drug therapy and the second drug of the second drug therapy ((0061), 'the physiological prediction may include a treatment. The treatment may be one that is predicted to be effective in treating a disease or condition. In one instance, 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.'; (0063], '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.'; (0076), '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.'; (0077), '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.'; (0079), '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 trained predictive model is stored in memory, which is broadly considered a database, for use in predicting a patient response based upon the patient's input for all of the different treatment options, including a first and second drug therapy).
As per claim 10, Gray and Navalgund teach the invention as claimed, see discussion of claim 1, and further teach:
wherein the respective clinical data includes one or more of: an age of the respective patient; a sex of the respective patient; a weight of the respective patient; a diagnosis date; patient information over time; an indicator regarding whether or not the patient has relapsed; an indicator of the respective patient's response to a second drug therapy; a stage of the respective patient's disease progression (Fig. 18a shows an example of clinical data for the progression of the disease over time based upon different categories of patients; [0155), 'The Kaplan-Meyer plots of progression free survival showed that the 6-gene predictor set stratified 53 patients in the intermediate group into 45 patients predicted to be sensitive compared to 8 patients predicted to be resistant (FIG. 18 a). The median survival was longer for the patients predicted to be sensitive, but shorter for the patients to be resistant.'); a concentration of total protein; a concentration of one or more biochemicals ((0036), 'For example, a Gl50 value (a concentration of the compound or drug that causes 50 percent growth inhibition) or a sensitivity value (equal to the-log(Gl50)) may be determined for each sample.'; (0069), 'The user may also input cell viability value/s associated with a treatment (e.g., for a plurality of drug concentrations)'); an indicator of the drug therapy the respective patient is receiving ((0036), 'The physiological response may comprise a binary indication or a magnitude of response. In some embodiments, each sample is contacted with a compound or a drug.'; [0061), 'The physiological prediction may include a treatment. The treatment may be one that is predicted to be effective in treating a disease or condition. In one instance, 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.'); a tumor size ((0110], 'In some embodiments, the therapeutically effective amount is the amount effective to at least slow the rate of tumor growth, slow or arrest the progression of cancer, or decrease tumor size. Tumor growth and tumor size can be measured using routine methods known to those skilled in the art, including, for example, magnetic resonance imaging and the like.'); and an indication of other health conditions associated with the respective patient ((0034], 'FIG. 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. For example, 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. In some embodiments, the samples comprise a panel of cell lines. This panel may be comprised of cell-lines specific to an organ, e.g. breast cancer cell-lines, pancreatic cancer cell-lines, etc. Alternatively, this panel may comprise of cell-lines from diverse organs, e.g. NCl-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).'.
As per claim 11, Gray and Navalgund teach the invention as claimed, see discussion of claim 1, and further teach:
wherein the one or more drug therapies are one or more chemotherapies, and each chemotherapy includes one or more drugs for treating cancer ([0033). 'methods and systems are provided that use splines to predict the magnitude of response of cells to various treatments'; [0036), 'At step 115 of process 100, a physiological response is determined for each of the samples. The physiological response may comprise a binary indication or a magnitude of response. In some
embodiments, 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.'; (0061), 'the physiological prediction may include a treatment. The treatment may be one that is predicted to be effective in treating a disease or condition. In one instance, 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.'; [0158), 'EGF30001 was a randomized, first-line phase Ill trial of a combination therapy of paclitaxel plus Lapatinib vs. a therapy of paclitaxel plus placebo for patients with metastatic breast cancer.'; paclitaxel is a common chemotherapy drug that is used to treat cancers such as breast cancer).
As per claim 13, Gray and Navalgund teach the invention as claimed, see discussion of claim 1, and further teach:
wherein the feature vectors are used to train the first model to output a prediction interval corresponding to the predicted individual patient response to the first drug therapy ([0062), 'The physiological prediction may include a number, a percent, a classification, or a description. For example, 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.'; [0066), '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.'; (0122), 'The dose response curves for a total of 40 breast cancer cell lines were determined using the CellTiter Glo assay, which measures cell viability. The response curves were used to estimate the Gl50 value for each cell line, which were then used to perform the correlative analyses to predict sensitivity (=-log(Gl50)). The Gl50 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 (FIG. 7). To comprehensively determine the predictive markers of sensitivity to Lapatinib, from cell-line panel, 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 1 0 cell-lines were used to test the accuracy of the model.'; {the bottom of pg. 14 of the PCT specification defines the
prediction interval as being a confidence interval in the prediction}).
As per claim 14, Gray and Navalgund teach the invention as claimed, see discussion of claim 1, and further teach:
wherein the first drug therapy includes a predefined combination of two or more drugs ((0033], 'methods and systems are provided that use splines to predict the magnitude of response of cells to various treatments'; (0034], 'FIG. 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. For example, 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.'; (0036], 'At step 115 of process 100, a physiological response is determined for each of the samples. The physiological response may comprise a binary indication or a magnitude of response. In some embodiments, 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.'; [0057], 'The physiological prediction may include a prediction related to a patient. For example, 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.'; (0058], 'The physiological prediction may include an efficacy value. For
example, it may be predicted that a treatment may be effective in eliminating 50 percent of a specific tumor (e.g., for a specific person). As another example, it may be predicted that there is a 60 percent probability that a treatment will eliminate a specific tumor type (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. In some embodiments, the prediction may comprise a binary result, e.g. sensitive or resistant to a drug.'; [0061], 'the physiological prediction may include a treatment. The treatment may be one that is predicted to be effective in treating a disease or condition. In one instance, 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.'; (0158], 'EGF30001 was a randomized, first-line phase Ill trial of a combination therapy of paclitaxel plus Lapatinib vs. a therapy of paclitaxel plus placebo for patients with metastatic breast cancer.'; a combination of two or more drugs as described in [0158] may be used as one of a plurality of different drug treatments to create a prediction model).
As per claim 15, Gray and Navalgund teach the invention as claimed, see discussion of claim 1, and further teach:
wherein the first subset of the feature vectors is a subset, less than all, of the feature vectors, the method further comprising: using a second subset of the feature vectors, distinct from the first subset of the feature vectors, to test the trained model ((0058], 'The physiological prediction may include a prediction related to treatment efficacy. In some embodiments, 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. In other embodiments, 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. For example, it may be predicted that a treatment may be effective in eliminating 50 percent of a specific tumor (e.g., for a specific person). As another example, it may be predicted that there is a 60 percent probability that a treatment will eliminate a specific tumor type (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. In some embodiments, the prediction may comprise a binary result, e.g. sensitive or resistant to a drug.'; [0069], 'The input component 305 may be configured to receive data related to training samples and/or to test samples.'; [0120], 'Multivariate models. To obtain a multivariate model, as a start, the most strongly correlated NG univariate predictors were combined using a weighted voting scheme, as described herein. Here, 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.'; [0122], 'To comprehensively determine the predictive markers of sensitivity to Lapatinib, from cell-line panel, 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.'; for a given group of samples, one subset of samples may have feature vectors that are used to train the prediction model, while a second subset of samples may be used to test the prediction model for accuracy).
As per claim 16, Gray and Navalgund teach the invention as claimed, see discussion of claim 1, and further teach:
wherein at least a first subset of the plurality of patients includes patients that have undergone one or more drug therapies that includes the first drug therapy ([0034), 'FIG. 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. For example, 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. In some embodiments, the samples comprise a panel of cell lines. This panel may be comprised of cell-lines specific to an organ, e.g. breast cancer cell-lines, pancreatic cancer cell-lines, etc. Alternatively, this panel may comprise of cell-lines from diverse organs, e.g. NCl-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).'; (0036), 'At step 115 of process 100, a physiological response is determined for each of the samples. The physiological response may comprise a binary indication or a magnitude of response. In some embodiments, each sample is contacted with a compound or a drug.'; one example may be that there is one subset of patients that received the drug treatment and a second subset that is a control group using healthy patients; however, other examples of subsets of patients may be discussed).
As per claim 17, Gray and Navalgund teach the invention as claimed, see discussion of claim 16, and further teach:
wherein the one or more drug therapies associated with the first subset of the plurality of patients includes one or more drug therapies that are different from the first drug therapy ((0033), 'methods and systems are provided that use splines to predict the magnitude of response of cells to various treatments'; (0034), 'FIG. 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. For example, 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.'; (0036), 'At step 115 of process 100, a physiological response is determined for each of the samples. The physiological response may comprise a binary indication or a magnitude of response. In some embodiments, 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.'; (0057), 'The physiological prediction may include a prediction related to a patient. For example, 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.'; (0058), 'The physiological prediction may include an efficacy value. For
example, it may be predicted that a treatment may be effective in eliminating 50 percent of a specific tumor (e.g., for a specific person). As another example, it may be predicted that there is a 60 percent probability that a treatment will eliminate a specific tumor type (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. In some embodiments, the prediction may comprise a binary result, e.g. sensitive or resistant to a drug.'; (0061), 'the physiological prediction may include a treatment. The treatment may be one that is predicted to be effective in treating a disease or condition. In one instance, 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.'; this process can apply to each treatment in order to create a treatment-specific model, where each treatment uses a different drug; one example means if you have a sample population of patients with the diagnosed disease, a first subset of those diseased patients would use a different drug treatment that a subset of those diseased patients than those that received the first drug in the drug therapy prediction).
As per claim 18, Gray and Navalgund teach the invention as claimed, see discussion of claim 16, and further teach:
wherein the plurality of patients further includes: a second subset of patients that have undergone one or more drug therapies that includes drugs other than the first drug ((0033), 'methods and systems are provided that use splines to predict the magnitude of response of cells to various treatments'; (0034), 'FIG. 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. For example, 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.'; (0036), 'At step 115 of process 100, a physiological response is determined for each of the samples. The physiological response may comprise a binary indication or a magnitude of response. In some embodiments, 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.'; (0057), 'The physiological prediction may include a prediction related to a patient. For example, 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.'; (0058), 'The physiological prediction may include an efficacy value. For example, it may be predicted that a treatment may be effective in eliminating 50 percent of a specific tumor (e.g., for a specific person). As another example, it may be predicted that there is a 60 percent probability that a treatment will eliminate a specific tumor type (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. In some embodiments, the prediction may comprise a binary result, e.g. sensitive or resistant to a drug.'; (0061), 'the physiological prediction may include a treatment. The treatment may be one that is predicted to be effective in treating a disease or condition. In one instance, 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.'; this process can apply to each treatment in order to create a treatment-specific model, where each treatment uses a different drug; another example can mean if you have a sample population of patients with the diagnosed disease, a first subset of those diseased patients would use the initial drug treatment, while a second subset of those diseased patients would receive a different drug treatment).
As per claim 19, Gray and Navalgund teach the invention as claimed, see discussion of claim 16, and further teach:
wherein the plurality of patients further includes a second subset of patients that have undergone one or more drug therapies that are different from the one or more drug therapies associated with the first subset of patients, and the one or more drug therapies associated with the second subset of patients do not include the first drug therapy ([0033], 'methods and systems are provided that use splines to predict the magnitude of response of cells to various treatments'; [0034), 'FIG. 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. For example, 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.'; [0036], 'At step 115 of process 100, a physiological response is determined for each of the samples. The physiological response may comprise a binary indication or a magnitude of response. In some embodiments, 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.'; (0057], 'The physiological prediction may include a prediction related to a patient. For example, 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.'; [0058], 'The physiological prediction may include an efficacy value. For example, it may be predicted that a treatment may be effective in eliminating 50 percent of a specific tumor (e.g., for a specific person). As another example, it may be predicted that there is a 60 percent probability that a treatment will eliminate a specific tumor type (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. In some embodiments, the prediction may comprise a binary result, e.g. sensitive or resistant to a drug.'; (0061], 'the physiological prediction may include a treatment. The treatment may be one that is predicted to be effective in treating a disease or condition. In one instance, 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.'; this process can apply to each treatment in order to create a treatment-specific model, where each treatment uses a different drug; another example can mean if you have a sample population of patients with the diagnosed disease, a first subset of those diseased patients would use the initial drug treatment, while a second subset of those diseased patients would receive a different drug treatment that does not include the initial drug).
As per claim 20, Gray teaches a method of predicting patient response to one or more drug therapies (Fig. 1; [0034], 'FIG. 1 shows one process 100 for developing a model of a response to a therapeutic treatment.'), performed at a computing device (system 300) having one or more processors and memory storing one or more programs configured for execution by the one or more processors (Fig. 3; [0069]. 'a system 300 (e.g., a computer system) is provided to make a physiological prediction about a treatment response. As shown in FIG. 3, 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).'; [0079], '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.'):
identifying a patient having a first disease condition ([0034], 'In some instances, all patients have been diagnosed with a similar or the same disease or condition (e.g., breast cancer)'; the system can be related to patients with a diagnosed disease);
retrieving a first trained model built to predict response to a first drug therapy for treating the first disease condition (Fig. 1 & 2 show the processes for developing a trained model as a predictor of a response to a therapeutic treatment; [001 OJ, 'a method for predicting a physiological response of a patient to a treatment is provided, the method 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.'; [0036], 'At step 115 of process 100, a physiological response is determined for each of the samples. The physiological response may comprise a binary indication or a magnitude of response. In some embodiments, each sample is contacted with a compound or a drug.'; [0070], '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. For example, the Gl50 may be determined based on cell viability values associated with different drug concentrations.'),
wherein: the first trained model has been trained according to data for a plurality of previous patients ([0034], 'FIG. 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. For example, 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. In some embodiments, the samples comprise a panel of cell lines. This panel may be comprised of cell-lines specific to an organ, e.g. breast cancer cell-lines, pancreatic cancer cell-lines, etc. Alternatively, this panel may comprise of cell-lines from diverse organs, e.g. NCl-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).'; [0070], '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.'; previous patient sample responses are used to train the model);
each previous patient provided medical data during drug therapy that includes one or more drugs ([0034], 'FIG. 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. For example, 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. In some embodiments, the samples comprise a panel of cell lines. This panel may be comprised of cell-lines specific to an organ, e.g. breast cancer cell-lines, pancreatic cancer cell-lines, etc. Alternatively, this panel may comprise of cell-lines from diverse organs, e.g. NCl-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).'; [0036), 'At step 115 of process 100, a physiological response is determined for each of the samples. The physiological response may comprise a binary indication or a magnitude of response. In some embodiments, each sample is contacted with a compound or a drug.'; [0070], '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.'; previous patient sample responses to drug treatment are used to train the model); and
at least a first subset of the previous patients underwent one or more drug therapies that include the first drug therapy ([0034], 'FIG. 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. For example, 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. In some embodiments, the samples comprise a panel of cell lines. This panel may be comprised of cell-lines specific to an organ, e.g. breast cancer cell-lines, pancreatic cancer cell-lines, etc. Alternatively, this panel may comprise of cell-lines from diverse organs, e.g. NCl-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).'; [0036], 'At step 115 of process 100, a physiological response is determined for each of the samples. The physiological response may comprise a binary indication or a magnitude of response. In some embodiments, each sample is contacted with a compound or a drug.'; one example may be that there is one subset of patients that received the drug treatment and a second subset that is a control group using healthy patients; however, other examples of subsets of patients may be discussed);
receiving medical data for the patient, the medical data including functional data and clinical data corresponding to features used by the first trained model ([0051], 'where D indicates a data-type, g indicates a prioritized univariate predictor for this data-type, log(Gl50)Dg is the predicted value of log(Gl50) based on the feature g, NG the total number of predictors used, and wDg indicates the normalized weight for this univariate feature for data type D, being proportional to the magnitude of correlation with response'; [0052], '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'; [0120], 'Multivariate models. To obtain a multivariate model, as a start, the most strongly correlated NG univariate predictors were combined using a weighted voting scheme, as described herein. Here, 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.'; (0155], 'The Kaplan-Meyer plots of progression free survival showed that the 6-gene predictor set stratified 53 patients in the intermediate group into 45 patients predicted to be sensitive compared to 8 patients predicted to be resistant (FIG. 18 a). The median survival was longer for the patients predicted to be sensitive, but shorter for the patients to be resistant.'; functional and clinical data is used to train the model),
wherein: the functional data includes initial cell viability (Fig. 1; [0036], 'Al step 115 of process 100, a physiological response is determined for each of the samples. The physiological response may comprise a binary indication or a magnitude of response ... 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(R) Luminescent Cell Viability assay, which may then be used to estimate Gl50 for the sample.'; [0055], 'At step 150 of process 100, 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) of specific, significant or all markers in the sample may be determined. In a first example, 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.'; a response curve for cell viability must include the initial cell viability and then the curve after exposure to the drug therapy over time); and
the clinical data includes patient information over time (Fig. 18a shows an example of clinical data for the progression of the disease over time based upon different categories of patients; [0155], 'The Kaplan-Meyer plots of progression free survival showed that the 6-gene predictor set stratified 53 patients in the intermediate group into 45 patients predicted to be sensitive compared to 8 patients predicted to be resistant (FIG. 18 a). The median survival was longer for the patients predicted to be sensitive, but shorter for the patients to be resistant.');
extracting, from the medical data, features corresponding to the features used by the first trained model ([0051), 'where D indicates a data-type, g indicates a prioritized univariate predictor for this data-type, log(Gl50)Dg is the predicted value of log(Gl50) based on the feature g, NG the total number of predictors used, and wDg indicates the normalized weight for this univariate feature for data type D, being proportional to the magnitude of correlation with response'; (0052], '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'; [0120], 'Multivariate models. To obtain a multivariate model, as a start, the most strongly correlated NG univariate predictors were combined using a weighted voting scheme, as described herein. Here, 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.'; [0155), 'The Kaplan-Meyer plots of progression free survival showed that the 6-gene predictor set stratified 53 patients in the intermediate group into 45 patients predicted to be sensitive compared to 8 patients predicted to be resistant (FIG. 18 a). The median survival was longer for the patients predicted to be sensitive, but shorter for the patients to be resistant.'; various feature vectors values are extracted from the data);
forming a feature vector comprising the extracted features ((0051), 'where D indicates a data-type, g indicates a prioritized univariate predictor for this data-type, log(Gl50)Dg is the predicted value of log(Gl50) based on the feature g, NG the total number of predictors used, and wDg indicates the normalized weight for this univariate feature for data type D, being proportional to the magnitude of correlation with response'; (0052). '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'; [0120), 'Multivariate models. To obtain a multivariate model, as a start, the most strongly correlated NG univariate predictors were combined using a weighted voting scheme, as described herein. Here, 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.'; [0155), 'The Kaplan-Meyer plots of progression free survival showed that the 6-gene predictor set stratified 53 patients in the intermediate group into 45 patients predicted to be sensitive compared to 8 patients predicted to be resistant (FIG. 18 a). The median survival was longer for the patients predicted to be sensitive, but shorter for the patients to be resistant.'; various feature vectors values are formed using the data);
applying the first trained model to the feature vector to generate a prediction of the patient's response to the first drug therapy; and
providing the predicted patient's response to the first drug therapy ((0063), '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.'; (0076), '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.'; (0077), '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.'; [0079), '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 trained predictive model is stored in memory, which is broadly considered a database, for use in predicting a patient response based upon the patient's input, where the prediction may be output on the display).
Gray fails to specifically teach that the first trained model includes a plurality of decision trees…an aggregate prediction…using a random forest of the plurality of decision trees in the first trained model; however, Navalgund teaches one or more machine learning models (e.g. random forest) may be deployed to operate by constructing a multitude of decision tree, and training data may also include patient response data to particular medications (see: Navalgund, paragraph 44, 130, 186, and 251).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the training and model as taught by Gray to include one or more machine learning models (e.g. random forest) may be deployed to operate by constructing a multitude of decision tree, and training data may also include patient response data to particular medications, so that during the testing or use phase, the ML model may be able to predict which medications a patient will respond to, as taught by Navalgund, with the motivation of, during the testing or use phase, the ML model may be used to predict which medications a patient will respond to (see: Navalgund, paragraph 251).
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 2009/0177450 to Gray in view of U.S. Patent Application Publication 2020/0107781 to Navalgund further in view of U.S. Patent Application Publication 2010/0280987 to Loboda.
As per claim 3, Gray and Navalgund teach the invention as claimed, see discussion of claim 2, and further teach:
wherein the respective genetic data also includes one or more selected from: (i-1) information obtained from a DNA sequence extracted from non-cancerous cells obtained from a healthy site of the respective patient ((0034). 'FIG. 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. For example, 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.'; (0035). '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. In some embodiments, 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.'; genetic data related to both DNA and mRNA in the non-cancerous cells of the control group patients can be determined from the analysis); and (i-2) information obtained from an RNA sequence extracted from non-cancerous cells obtained from a healthy site of the respective patient ((0034). 'FIG. 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. For example, 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.'; (0035), '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. In some embodiments, 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.'; genetic data related to both DNA and mRNA in the non-cancerous cells of the control group patients can be determined from the analysis); (ii) information regarding: RNA transcripts ((0081], 'Techniques and systems to measure expression levels are well known by persons skilled in the art. For example, quantitative mRNA levels of the transcripts may be monitored using a quantitative PCR-analysis with primer combinations to amplify said gene specific sequences from cDNA obtained by reverse transcription of RNA extracted from a sample obtained from a subject.'); DNA variants (10124], 'Based on univariate analysis, a total of 155 significant mRNA markers were identified (p<=5e-04, FDR=1.5 percent), 45 DNA markers from copy number variations (p<=5e-03, FDR=5 percent) and 9 protein markers (p<=0.01, FDR=1 percent) (Table 3).'; DNA variants may be used); genes ((0080], 'As used herein, an increased or decreased expression level is an expression level of a gene that is more than or less than, respectively, the expression level of the same gene in a normal tissue or cell sample. For example, the normal cell or tissue may be a cell or tissue sample of non-cancerous cells from a patient or another person that does not have cancer. In some embodiments, an increased or decreased expression level is an expression level of a gene that is more than or less than, respectively, the average expression level of the same gene in a panel of normal cell lines or cancer cell lines. In some embodiments, an increased or decreased expression level is an expression level that is relatively more than or less than, respectively, the expression of a housekeeping gene, such as a gene encoding GAPDH.'); and pathways (10127], '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').
Gray and Navalgund fail to specifically teach the following limitations met by Loboda as cited:
the respective genetic data includes information measuring one or more of: presence of genetic mutations ((0267), 'We derived our own RAS signature using supervised analysis of the Nevins, Blum, Jacks, and Jacks123 signatures and their consensus prediction of RAS mutation status generated in lung cell lines. Specifically, we used the consensus prediction of KRAS mutation status generated in lung cell lines'; [0269), 'Initial signature coherence analysis and pairwise comparison of cell lines was performed on cell lines (CMTI portion of the Cell Line Atlas (breast, colon, lung, lymphoma)). Prediction of RAS mutation status was also performed on cell lines from the cell lines atlas for which RAS mutation data was available.'; the genetic data can include gene mutation data); variant allele frequency; and a number of variant alleles.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify Gray and Navalgund with the teaching of Loboda for the purpose of using gene mutations as genetic data for a prediction model, thereby using the known gene activation pathways to aid in determining if a the tumor with these pathways will respond when to a particular drug treatment (Loboda; [0005]-[00071).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 2009/0177450 to Gray in view of U.S. Patent Application Publication 2020/0107781 to Navalgund further in view of U.S. Patent Application Publication 2010/0106475 to Smith.
As per claim 12, Gray and Navalgund teach the invention as claimed, see discussion of claim 1, and further teach:
determining that each of the respective functional data and respective clinical data is complete ([0051), 'where D indicates a data-type, g indicates a prioritized univariate predictor for this data-type,
log(Gl50)Dg is the predicted value of log(Gl50) based on the feature g, NG the total number of predictors used, and wDg indicates the normalized weight for this univariate feature for data type D, being proportional to the magnitude of correlation with response'; [0052), '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'; [0120], 'Multivariate models. To obtain a multivariate model, as a start, the most strongly correlated NG univariate predictors were combined using a weighted voting scheme, as described herein. Here, 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.'; [0155), 'The Kaplan-Meyer plots of progression free survival showed that the 6-gene predictor set stratified 53 patients in the intermediate group into 45 patients predicted to be sensitive compared to 8
patients predicted to be resistant (FIG. 18 a). The median survival was longer for the patients predicted to be sensitive, but shorter for the patients to be resistant.'; functional and clinical data is used to train the model; if the training of the model has occurred, the data is assumed to be complete enough).
Gray and Navalgund fail to explicitly disclose in accordance with a determination that at least one of the respective functional data and respective clinical data includes one or more missing values, replacing at least one of the one or more missing values with an inferred value.
However, Smith is in the field of modeling systems in biology ([01591) and teaches in accordance with a determination that at least one of a respective functional data and respective clinical data includes one or more missing values, replacing at least one of the one or more missing values with an inferred value. ([0070). 'Data analysis as described can be used to infer a likely value for a missing field. In this respect, it is preferable to process the population data in a manner that recognizes the cross correlation of certain data values.'; [0097], 'The model of the invention may be embodied to include aspects of social typing as well as physical measurements, images, biological function and the like. In view of the depth of information available, detailed matching may be performed provided the fields are maintained, or if field values are missing, values can be inferred according to probabilities based on the population data.'; [0159], 'According to certain advantageous embodiments, data is applied and/or viewed using at least one model relating to at least a category of the subjects. The model comprises a process for manipulating the biophysical parameter values and associated information to deduce how the biophysical parameters affect the structure and functioning of the physical subsystem. Various models can be defined or hypothesized, which models reflect how the data values that concern one or more physical subsystems affect how the physical subsystems work. Models can be based on a deep analysis of applicable biology including the interaction of cells and blood circulation or neural function down to a microscopic scale.'; missing data, especially functional data, may be replaced with inferred data values)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify Gray and Navalgund with the teaching of Smith with the motivation of replacing at least one of the one or more missing values with an inferred value, thereby using probabilities of the missing value based on population data in order to maintain the overall accuracy to within reason in the model (Smith; [0070], [0097]). For example, by using population data, a missing value will not skew the model results in a manner that could significantly decrease its accuracy.
Response to Arguments
Applicant’s arguments from the response filed on 10/10/2025 have been fully considered and will be addressed below in the order in which they appeared.
In the remarks, Applicant argues in substance that (1) the 35 U.S.C. 101 rejections should be withdrawn in view of the amendments because “claim 1 does not recite any limitation that covers methods of organizing human activity. Therefore, the examiner's determination that the claims recite an abstract idea because the recited limitations cover certain methods of organizing human activity is misguided. The Applicant respectfully requests that the rejection under 35 U.S.C. § 101 be withdrawn under Step 2A, Prong One. In addition, amended claim 1 recites additional elements that sufficiently integrate the claimed subject matter into a practical application. For example, amended claim 1 recites that "the first model includes a plurality of decision trees for use by a random forest to form an aggregate prediction for the first drug therapy." The claimed subject matter as a whole, including the specific data structure, enhances the performance in training and using the first model. Therefore, the Applicant respectfully requests that the rejection under 35 U.S.C. § 101 be withdrawn under Step 2A, Prong Two. Furthermore, amended claim 1 recites additional elements that amount to significantly more than any judicial exception. As explained above, amended claim 1 recites elements that enhance the performance in training and using the first model. This, in tum, reduces the computational resources required for training and using the first model. "Claims that are determined to improve computer capabilities or improve technology or a technical field support a finding that the claim integrates the judicial exception into a practical application or amounts to significantly more than the judicial exception itself" USPTO Memorandum "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101" dated August 4, 2025; MPEP § 2106.05(a), citing Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016). Therefore, the Applicant respectfully requests that the rejection under 35 U.S.C. § 101 be withdrawn under Step 2B.”
The Examiner respectfully disagrees. Applicant’s arguments are not persuasive.
The claims are always and necessarily read in view of the specification. The recited limitations, as explicitly outlined in the rejection above, under their broadest reasonable interpretation, cover certain methods of organizing human activity as is reflected in the specification, which states that the invention is “to providing predicted patient response to drug therapies and more specifically to systems and methods for predicting a patient's response to chemotherapy drug therapies” (see: specification paragraph 2). This is further supported by the specification because the claims address a problem where “the efficacy of specific drugs is assessed based on disease progression in diseased sites before and after treatments…a time consuming and financially costly approach that can take up to weeks, if not months, before returning results” (see: specification paragraph 3), which presents “a need for tools that can accurately calculate and predict a patient's in-vivo response to different drug therapies (e.g., a likelihood that a patient will have a positive response to dmg therapies), including single agent drug therapies and multiple agent dmg therapies” (see: specification paragraph 6), and which the present invention addresses with a “solution [] to train predictive models to provide predicted patient response to different dmg therapies (including single agent dmg therapies and multiple agent drug therapies). For each patient, functional, genetic, and clinical data is used to provide predicted in-vivo response in a cost effective and timely manner” (see: specification paragraph 7).
The claims are not entirely abstract, but they do recite abstract ideas. The identification of an abstract idea within the claims does not automatically render the claims as a whole abstract, but instead merely indicates that additional analysis must further take place. It is further because the claims do not amount to significantly more than the abstract idea that that the claims are rendered nonstatutory. In this case, inclusion of the additional elements does not meaningfully transform the abstract idea into a patent eligible application that is significantly more than the abstract idea itself. For example, it is argued that the amendment provides an additional element which renders the claims statutory by providing a practical application. The amendment does improve the claims by providing additional elements; however, the argued additional elements are still recited at a high level of generality such that they amount to no more than mere instruction to apply the exception using generic computer components. For example, in claim 1, the “trained” model(s) is configured though no more than a statement than that data has been used to “train” said model(s), and that the trained model(s) “include” a plurality of decision trees such that a random forest of the decision trees can be “us[ed]” in the training. The configuration of the random forest, in what way it is used, how exactly the training takes place, is not claimed, and therefore cannot be said to “enhance[] the performance in training and using the first model” as argued. Further, a random forest is a well-known, routine, and conventional machine learning algorithm that utilizes decision trees to improve prediction accuracy, and does not necessarily “reduce[] the computational resources required for training and using the first model” as argued as random forests train many decision trees independently, then aggregate their results, which often increase training complexity and thus CPU and memory usage resources.
As per Enfish, the claims of Enfish were directed to a specific improvement to computer functionality by describing a specific type of data structure designed to improve the way a computer stores and retrieves data in memory, where the specific data structure in question was a self-referential table that functions differently than convention data structures by storing all entity types in a single table defining the table's columns by rows in that same table. The present claims are directed to an abstract idea, as shown above, and do not recite comparable features to that of Enfish.
In the remarks, Applicant argues in substance that (2) the 35 U.S.C. 102/103 rejections should be withdrawn because “[a]mended claim 1 recites "the first model includes a plurality of decision trees for use by a random forest to form an aggregate prediction for the first drug therapy." Gray does not disclose this feature. The Applicant respectfully requests that the rejection of claim 1 and its dependent claims be withdrawn. Amended claim 20 recites analogous features, and thus, amended claim 20 is also patentable over Gray for analogous reasons.”
Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument – see application of prior art Navalgund.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ROBERT A SOREY/ Primary Examiner, Art Unit 3682