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 .
Information Disclosure Statement
Reference 6 associated with “Takagi” is not considered since the entire document has not been submitted. The Examiner requests that the entire document be filed in a re-submission.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The following title is suggested:
METHOD OF EXTRACTING GENE CANDIDATE, METHOD OF UTILIZING GENE CANDIDATE, AND COMPUTER-READABLE MEDIUM COMPRISING ESTIMATING PREDICTION ACCURACY OF THE GENE EXPRESSION LEVEL USING A FUNCTION INVOLVING DEEP LEARNING AND EXTRACTING A GENE BASED ON THE PREDICTION ACCURACY.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 3, 5-10 is/are rejected under 35 U.S.C. 102(a1 and/or a2) as being anticipated by Igartua (US Pub 2020/0210852).
Re claim 1: Igartua discloses a method of extracting a gene candidate related to a feature of a cancer of an individual patient, the method comprising:
(a) acquiring a microscope image of a cultured cell cluster derived from a cancer specimen of the patient (e.g. the invention discloses histopathology slides that are used to contain a sample, which is taught in ¶ [10] and [78].);
[0010] In an example, a computer-implemented method comprises: performing clustering on RNA expression data corresponding to a plurality of samples, where each sample is assigned to at least one of a plurality of clusters; generating a deconvoluted RNA expression data model comprising at least one cluster identified as corresponding to biological indication of one or more pathologies; receiving additional RNA expression data of a sample of tumor tissue; deconvoluting the additional RNA expression data based in part on the deconvoluted RNA expression data model; and classifying the sample of tumor tissue as the biological indication of one or more pathologies.
[0078] In one embodiment, biological validation may include comparing each sample's estimated membership percent in a given cluster with that sample's tumor purity estimate (or 1-tumor purity) to determine whether the cluster is likely to represent the primary cancer cells (or background tissue cells) in the sample. Proportion estimates for other cell types that are known for a mixed sample may be used in a similar fashion to associate a cluster with that cell type. In various examples, tumor purity of a mixed sample may be determined by visual analysis of a histopathology slide or by bioinformatic analysis of DNA data associated with the sample.
(b) acquiring a measured value of a gene expression level of the cancer specimen or the cell cluster cultured from the cancer specimen used in the (a) (e.g. the invention discloses acquiring tissue sample of a patient that is used to obtain gene expression data corresponding to a particular type of cancer, which is taught in ¶ [69]. ¶ [37]-[41] and [54] describe the explanation of expression level and the acquiring of patient samples.);
[0037] “Biological validation” refers to the comparison of a set of identified genes that are correlated with a cluster and genes represented in RNA expression profiles known or likely to be associated with a subset of tissues, including a portion of a tissue sample, a type of cell that may be in a tissue sample, or single cells within a tissue sample and may determine a correlation between the known RNA expression profile genes and the genes correlated with a cluster, associating the cluster with the expression profile of that subset of tissue.
[0038] “Cluster” refers to a set of genes whose expression levels are correlated with a percentage of the variance seen among multiple samples in an RNA expression dataset. The cluster may be said to be driven by this set of genes, where “driven” is a term for describing that the expression levels of the genes in this set explain a percentage of the variance. The expression levels of the genes in this set may have patterns that are consistently associated with the variance. For example, the expression level of a given gene in the set may be higher or lower in samples having one or more characteristics in common, or the expression levels of two or more genes may be directly or inversely correlated with each other in samples having one or more characteristics in common. Sample characteristics may include the collection site of the sample, the type of tissue or combination of tissue types contained in the sample, etc.
[0039] “Bioinformatics pipeline” means a series of processing stages of a pipeline to instantiate bioinformatics reporting regarding next-generation sequencing results of a patient's tumor or normal tissue or bodily fluids to extract and report on variants present in the patient's genome.
[0040] “Deconvolution” refers to a process of resolving expression data from a mixed population of cell types to identify expression profiles of one or more constituent cell types, for example using algorithm processes.
[0041] “Expression level” means the number of copies of an RNA or protein molecule generated by a gene or other genetic locus, which may be defined by a chromosomal location or other genetic mapping indicator.
[0054] “Targeted Panel” means a combination of probes for next-generation sequencing of a patient's biological specimens (including tumors, biopsies, tumor organoids, blood samples, saliva samples, or other tissues or fluids) which are selected to map one or more loci on one or more chromosomes.
[0069] In some examples, and as discussed further herein, the block 204 analyzes multiple samples of tissue applying the deconvolution model to identify one or more correlated clusters of RNA expression data and the genes corresponding to those clusters for identifying tissue and cancer types in subsequent RNA expression data. After completing the clustering process, the block 204 generates a deconvoluted RNA expression model that is stored (at block 206) for use as a trained model to examine subsequently received RNA expression data, such as RNA expression data generated from a tissue sample from a patient with cancer. For example, the deconvoluted RNA expression model may include regressed out clusters corresponding to latent factors, e.g., clusters of gene expression data corresponding to particular cancer types or cell populations with similar expression profiles, especially clusters that correspond to a cell population that has an effect on the mixed sample RNA expression data that is subtracted from the expression data (for example, regressed out) to generate a deconvoluted RNA expression model. These deconvoluted RNA expression models, as shown by examples below, are able to exhibit overexpressed genes and underexpressed genes different from those of normal or mixed, convoluted RNA expression data and that more accurately predict cancer type based on the list of those overexpressed and underexpressed genes. The generated trained deconvoluted models may then be applied to subsequent RNA expression data, at a block 208.
(c) acquiring a morphological representation identifiably expressing, by a vector quantity of a plurality of dimensions, a morphological difference between a group of a cell cluster cultured from the same cancer specimen and a group of a cell cluster cultured from another cancer specimen based on the microscope image acquired in the (a) (e.g. the invention discloses a vector quantity of the several dimensions of the expression data that is input into the deconvoluted RNA expression data model, which is taught in ¶ [12]. The cluster of a set of genes can be compared with other clusters of genes to determine a common characteristic or difference, which can be used to determine specific cancer cluster types. This is taught in ¶ [37]-[41] above, [63], [69] and [70].);
[0012] In some examples, the generated deconvoluted RNA expression data model comprises a first dimension reflecting a number of samples and a second dimension reflecting a number of genes in the RNA expression data.
[0063] The deconvolution framework 102 may be configured to receive normalized gene expression data and modify such data using a clustering process to optimize the number of clusters, K, such that one or more gene expression clusters associated with one or more cell types of interest are detected. Subsequent analysis of the gene expression clusters may determine cancer-specific cluster types within such data. The deconvolution framework is discussed with more detail with respect to FIG. 2 below.
[0069] In some examples, and as discussed further herein, the block 204 analyzes multiple samples of tissue applying the deconvolution model to identify one or more correlated clusters of RNA expression data and the genes corresponding to those clusters for identifying tissue and cancer types in subsequent RNA expression data. After completing the clustering process, the block 204 generates a deconvoluted RNA expression model that is stored (at block 206) for use as a trained model to examine subsequently received RNA expression data, such as RNA expression data generated from a tissue sample from a patient with cancer. For example, the deconvoluted RNA expression model may include regressed out clusters corresponding to latent factors, e.g., clusters of gene expression data corresponding to particular cancer types or cell populations with similar expression profiles, especially clusters that correspond to a cell population that has an effect on the mixed sample RNA expression data that is subtracted from the expression data (for example, regressed out) to generate a deconvoluted RNA expression model. These deconvoluted RNA expression models, as shown by examples below, are able to exhibit overexpressed genes and underexpressed genes different from those of normal or mixed, convoluted RNA expression data and that more accurately predict cancer type based on the list of those overexpressed and underexpressed genes. The generated trained deconvoluted models may then be applied to subsequent RNA expression data, at a block 208.
[0070] RNA expression data examined by the deconvoluted RNA expression model may be used to determine which genes, or networks of related genes, have expression levels that differ between tumor and normal tissue. Exemplary differences in expression levels in deconvoluted versus convoluted RNA expression data are depicted in FIG. 12. In various aspects, comparing tumor expression levels with normal tissue levels permits biomarker discovery, by determining which genes or gene networks have a higher or lower expression level in tumor tissue than normal tissue that may be adjusted or targeted by treatment. Such a comparison permits predicting the type of cancer or the origin of the cancer, associating mutations with gene expression patterns, and associating tumor gene expression profiles with a list of cancer treatments that may predict response for a patient with that profile.
[0109] Additionally, we examined the performance of expression calls on the deconvoluted samples. We made expression calls, where each call identifies a gene that has a larger (over expression) or smaller (under expression) amount of RNA copies than the gene would have in non-tumorous tissue, where the difference between the amount in the sample and the non-tumorous amount is greater than a user-defined value. The expression calls were made on the pure breast cancer samples and compared the results to the respective mixture and deconvoluted samples.
(d) estimating prediction accuracy of the gene expression level based on a prediction value of the gene expression level and the measured value of the gene expression level acquired in the (b), the prediction value being acquired by inputting the morphological representation acquired in the (c) to a function obtained by fitting using the morphological representation as input and the measured value of the gene expression level as output (e.g. RNA expression data is input into a deconvolution framework in order to have a deconvoluted RNA expression model that can output an expression level of genes that do not include tissue or areas that are not of interest in the cancer diagnosis, which is taught in ¶ [67]-[69]. The standardized deconvoluted gene expression is output in step 414, which occurs in step 312 and disclosed in ¶ [82], [92]. The deconvoluted output increases the accuracy of the prediction of the cancer type by excluding regions that are not of interest. This aids in system using a lowest sum of square error (SSE) in order to select an appropriate gene for the cancer candidate, which is taught in [104] and [105].); and
[0067] FIG. 2 illustrates a process 200 that may be executed by the system 100, and in particular the deconvolution framework 102, to perform an exemplary deconvolution on RNA expression data. At a block 202, the system 100 receives normalized RNA expression data, e.g., from the normalized RNA sequence database 116. In some examples, the system 100 is configured to generate the normalized RNA expression data, e.g., as described in reference to the normalization framework 104. The RNA expression data may contain data for various tissue samples, including cancer tissue samples and normal tissue samples. The RNA expression data, as described in various examples herein, may include metastatic tissue samples, which contain a mixture of cancer and normal tissue. The samples may be from any tissue type, including by way of example, liver tissue, breast tissue, pancreatic tissue, colon tissue, bone marrow, lymph node tissue, skin, kidney tissue, lung tissue, bladder tissue, bone, prostate tissue, ovarian tissue, muscle tissue, intestinal tissue, nerve tissue, testicular tissue, thyroid tissue, brain tissue, and fluid samples (e.g., saliva, blood, etc.). The sample may also be an organoid, for example, an organoid derived from a tumor and grown in vitro.
[0068] At a block 204, the deconvolution framework 102 analyzes the normalized RNA expression data and applies a deconvolution model to remove expression data from cell populations that are not cell types of interest (e.g. tumor or other types of cancer tissue). In some examples, the block 204 implements the deconvolution model using machine learning algorithms such as unsupervised or supervised clustering techniques to examine gene expression data to quantify the level of tumor versus normal cell populations present in the data. The block 204 may apply any number of machine learning algorithms, such as, for example, anomaly detection, artificial neural networks, expectation-maximization, singular value decomposition, etc. In some examples, the block 204 may apply machine learning techniques. Other example machine learning techniques that may be used in place of clustering include support vector machine learning, decision tree learning, associated rule learning, Bayesian techniques, and rule-based machine learning.
[0069] In some examples, and as discussed further herein, the block 204 analyzes multiple samples of tissue applying the deconvolution model to identify one or more correlated clusters of RNA expression data and the genes corresponding to those clusters for identifying tissue and cancer types in subsequent RNA expression data. After completing the clustering process, the block 204 generates a deconvoluted RNA expression model that is stored (at block 206) for use as a trained model to examine subsequently received RNA expression data, such as RNA expression data generated from a tissue sample from a patient with cancer. For example, the deconvoluted RNA expression model may include regressed out clusters corresponding to latent factors, e.g., clusters of gene expression data corresponding to particular cancer types or cell populations with similar expression profiles, especially clusters that correspond to a cell population that has an effect on the mixed sample RNA expression data that is subtracted from the expression data (for example, regressed out) to generate a deconvoluted RNA expression model. These deconvoluted RNA expression models, as shown by examples below, are able to exhibit overexpressed genes and underexpressed genes different from those of normal or mixed, convoluted RNA expression data and that more accurately predict cancer type based on the list of those overexpressed and underexpressed genes. The generated trained deconvoluted models may then be applied to subsequent RNA expression data, at a block 208.
[0082] The deconvoluted RNA matrix may be validated at a block 314, which may perform an in silico validation (i.e., validation performed on a computer) for example by using in silico mixtures of cancers and background RNA expression data. The validation analyzes whether the deconvoluted RNA matrix properly identifies, from the samples, RNA expressions of known in silico mixtures. In another example, the block 314 performs validation using a machine learning technique, such as analyzing the RNA expression data sets before and after deconvolution using a grouping analysis known as nearest neighbor clustering and comparing the results of the grouping analysis. This validation may be applied to confirm that relevant samples of the deconvoluted RNA matrix will form a group with primary samples of the same cancer type when sorted by a grouping technique.
[0092] At a process 412, corresponding intercept and beta (for example, residual) values may be determined from the linear model and used as correction factors to generate a standardized deconvolution model. At a process 414, the intercept and beta values may be used to adjust each RNA data set that was received, or any additional RNA data set, to remove any gene expression level correlated with the proportion of background tissue associated with that RNA data set.
[0104] In one example, the present techniques may implement a non-negative least squares (NNLS) model, to predict tumor and liver percentages trained on the GoM proportions of the fifth cluster and gene expression profiles from 358 liver metastatic samples. We selected 500 genes with the lowest sum of square error (SSE) in a leave-one-out validation approach applied to all genes. We then validated the selected gene list in a second leave-one-out step that resulted in a correlation of r=0.98 between predicted liver proportions and equivalent performance across cancer types, as shown in FIG. 8.
[0105] In one example, a customized non-negative least squares algorithm estimates cell proportions within a sample and projects them to a probability simplex such that all estimates are non-negative and sum up to one. Optimization of the convex function was done iteratively such that the sum of squares error (SSE) between the model parameters and the sample estimates have a difference of less than 10.sup.−7 between the two most recent runs. To select a set of genes with the highest predictive power in the final non-negative least squares model, we performed a leave-one-out NNLS approach using gene expression of 19,147 genes across 358 liver metastatic samples. We used the GoM proportion of the fifth cluster (liver) and 1 minus this proportion as predictors. The technique may be used to predict origin of cancer. We selected 500 genes with the lowest SSE among the models for our final model implementation. While the number of selected genes is somewhat arbitrary, we selected 500 genes from among a series of gene sets (100, 250, 500) such that GO enrichment associations reached the highest significance.
(e) extracting a gene related to a morphological change of the cell cluster as the gene candidate based on the prediction accuracy estimated in the (d) (e.g. a cancer type or gene can be selected from a list based on the model evaluating the expression data in comparison with other expression data of other cancers, which is taught in ¶ [67]-[70], [104] and [105] above.).
Re claim 3: Igartua discloses the method according to claim 1, further comprising (f) fitting the function that outputs the measured value of the gene expression level acquired in the (b) with respect to the input of the morphological representation acquired in the (c) (e.g. RNA expression data is input into a deconvolution framework in order to have a deconvoluted RNA expression model that can output an expression level of genes that do not include tissue or areas that are not of interest in the cancer diagnosis, which is taught in ¶ [67]-[69] above. The standardized deconvoluted gene expression is output in step 414, which occurs in step 312 and disclosed in ¶ [82], [92] above).
Re claim 5: The method according to claim 1, wherein in the (c), the morphological representation identifiably expressing a morphological difference between a plurality of groups classifying a plurality of cancer specimens by using clinical data acquired in process of pathological diagnosis is acquired (e.g. the system expresses the difference between groups of clusters of cancer specimens in order to determine a type of cancer by acquiring data sets with clusters corresponding to a pathological diagnosis, which is taught in ¶ [74].).
[0074] The number of clusters may be predetermined or dynamically set by the block 304. For example, the number of clusters may be dependent upon the type of tissue being sampled in the RNA expression data, the type and heterogeneity of cancer types or cell populations to be examined, or the sample size distribution of the reference samples and the type of sequencing technology. An exemplary training dataset may include RNA expression data from tissue normal samples, primary samples, and metastatic samples. An alternative training set may also include labels, annotations, or classifications identifying each of the samples as the respective type of tissue, in addition to other biological indicators (such as cancer site, metastasis, diagnosis, etc.) or pathology classifications (such as diagnosis, heterogeneity, carcinoma, sarcoma, etc.).
Re claim 6: The method according to claim 1, wherein the acquiring the morphological representation in the (c) is carried out by using a deep learning technique (e.g. the training data set to determine the cluster corresponds to a particular pathological diagnosis is used by a machine learning algorithm, such as a CNN. This is taught in ¶ [75].).
[0075] A machine learning algorithm (MLA) or a neural network (NN) may be trained from the training data set. MLAs include supervised algorithms (such as algorithms where the features/classifications in the data set are annotated) using linear regression, logistic regression, decision trees, classification and regression trees, Naíve Bayes, nearest neighbor clustering; unsupervised algorithms (such as algorithms where no features/classification in the data set are annotated) using Apriori, means clustering, principal component analysis, random forest, adaptive boosting; and semi-supervised algorithms (such as algorithms where certain features/classifications in the data set are annotated) using generative approach (such as mixture of Gaussian distributions, mixture of multinomial distributions, hidden Markov models), low density separation, graph-based approaches (such as mincut, harmonic function, manifold regularization), heuristic approaches, or support vector machines. NNs include conditional random fields, convolutional neural networks, attention based neural networks, long short term memory networks, or other neural models where the training data set includes a plurality of samples and RNA expression data for each sample. While MLA and neural networks identify distinct approaches to machine learning, the terms may be used interchangeably herein. Thus, a mention of MLA may include a corresponding NN or a mention of NN may include a corresponding MLA.
Re claim 7: The method according to claim 1, wherein the fitting of the function in the (f) is carried out by using a deep learning technique (e.g. the system uses a machine learning algorithm that can be an artificial neural network, where neural networks can be a CNN, which is taught in ¶ [68]-[70] and [75] above.).
Re claim 8. The method according to claim 1, wherein the (e) includes
statistically estimating variation in the measured value of the gene expression level (e.g. the variance of a value of the gene expression levels is calculated, which is taught in ¶ [37]-[41] above, [97]-[99].), and
[0097] In this example, a validation step was performed that uses principal component analysis (PCA) to assess groupings based on RNA gene expression profiles among the primary cancer samples, healthy tissue samples, and the deconvoluted metastatic samples. PCA, performed by computing devices such as that of FIG. 1, is a dimension reduction technique for comparing data sets from multiple samples or a single data set containing multiple samples, especially where each sample may be associated with multiple values, such as an expression level value for each expressed gene for tens of thousands of expressed genes or more. PCA may be used on all expressed genes to determine which genes in conjunction have the greatest variance in expression levels among samples.
[0098] The principal components may be sorted according to the largest percent of variance explained by the contributions of those genes to demonstrate the greatest differences among samples, and the principal component that makes the largest contribution to variance may be designated principal component 1 (PC1). The principal component that makes the second largest contribution to variance (after regressing out the contribution of PC1) may be designated principal component 2 (PC2). The samples may be spatially arranged according to the extent of contribution principal components that contribute the largest percentage of the variance in the dataset. In the example shown in FIG. 5 generated by the computing device, the expression levels of the group of genes represented by PC1 distinguishes samples with a low proportion of liver cells (in the example, primary non-liver cancers) from samples with a high proportion of liver cells (in the example, liver cancer and healthy liver samples). The expression levels of the group of genes represented by PC2 distinguishes samples based on differences caused by primary cancer types. As expected, liver specific cancers and liver tissue do not contain this type of variance and there is not a large degree of separation along the y-axis for these groups.
[0099] The groups of sample data can be visually represented in a chart such as the one shown in FIG. 5. Samples are colored by their tissue or origin. As shown, PC1 explained 10.5% of the variance and separated the TCGA liver hepatocellular carcinoma (lihc) and GTEx normal liver from the other non-liver primary cancers. Rather than forming a group with their cancer type of origin, in this unsupervised grouping example, principal component analysis grouped the liver metastatic samples together as a continuum between the TCGA cancers and liver normal (GTEx) and cancer samples (lihc TCGA). Metastatic liver samples (meaning, tumor cells from another organ which are found in the liver) are represented with larger circles and formed groups away from their respective TCGA primary cancers. As shown in FIG. 5, small circles to the left of liver metastases represent non-liver primary cancers, while liver primary cancers and liver normal samples are represented by small circles that group to the right of the metastases. This variation in expression separating metastatic liver samples from primary samples is attributable to the expression of the normal background liver tissue in the sample. As shown, rather than grouping with their cancer type of origin, liver metastatic samples grouped together as a continuum between the TCGA cancers, on the left, and both liver normal (GTEx liver) and liver cancer samples (TCGA liver hepatocellular carcinoma (lihc)) on the right.
extracting the gene candidate based on the prediction accuracy and magnitude of the variation (e.g. a gene candidate is chosen or determined based on the prediction based on the expression level and the percentage related to the variance, which is taught in ¶ [97]-[99] above.).
Re claim 9: A method of utilizing a gene candidate extracted by using the method of extracting the gene candidate according to claim 1, the method comprising
supporting classification or diagnosis of a cancer of a patient or predicting an effect of medication with respect to the patient based on the extracted gene candidate (e.g. predicting the type of cancer is performed or a pathology classification, which is taught in ¶ [70] and [74].).
[0070] RNA expression data examined by the deconvoluted RNA expression model may be used to determine which genes, or networks of related genes, have expression levels that differ between tumor and normal tissue. Exemplary differences in expression levels in deconvoluted versus convoluted RNA expression data are depicted in FIG. 12. In various aspects, comparing tumor expression levels with normal tissue levels permits biomarker discovery, by determining which genes or gene networks have a higher or lower expression level in tumor tissue than normal tissue that may be adjusted or targeted by treatment. Such a comparison permits predicting the type of cancer or the origin of the cancer, associating mutations with gene expression patterns, and associating tumor gene expression profiles with a list of cancer treatments that may predict response for a patient with that profile.
[0074] The number of clusters may be predetermined or dynamically set by the block 304. For example, the number of clusters may be dependent upon the type of tissue being sampled in the RNA expression data, the type and heterogeneity of cancer types or cell populations to be examined, or the sample size distribution of the reference samples and the type of sequencing technology. An exemplary training dataset may include RNA expression data from tissue normal samples, primary samples, and metastatic samples. An alternative training set may also include labels, annotations, or classifications identifying each of the samples as the respective type of tissue, in addition to other biological indicators (such as cancer site, metastasis, diagnosis, etc.) or pathology classifications (such as diagnosis, heterogeneity, carcinoma, sarcoma, etc.).
Re claim 10: Igartua discloses a non-transitory computer-readable medium storing a program that causes a computer to execute:
(a) acquiring a microscope image of a cultured cell cluster derived from a cancer specimen of a patient (e.g. the invention discloses histopathology slides that are used to contain a sample, which is taught in ¶ [10] and [78] above.);
(b) acquiring a measured value of a gene expression level of the cancer specimen or the cell cluster cultured from the cancer specimen used in the (a) (e.g. the invention discloses acquiring tissue sample of a patient that is used to obtain gene expression data corresponding to a particular type of cancer, which is taught in ¶ [69] above. ¶ [37]-[41] and [54] above describe the explanation of expression level and the acquiring of patient samples.);
(c) acquiring a morphological representation identifiably expressing, by a vector quantity of a plurality of dimensions, a morphological difference between a group of a cell cluster cultured from the same cancer specimen and a group of a cell cluster cultured from another cancer specimen based on the microscope image acquired in the (a) (e.g. the invention discloses a vector quantity of the several dimensions of the expression data that is input into the deconvoluted RNA expression data model, which is taught in ¶ [12] above. The cluster of a set of genes can be compared with other clusters of genes to determine a common characteristic or difference, which can be used to determine specific cancer cluster types. This is taught in ¶ [37]-[41], [63], [69] and [70] above.);
(d) estimating prediction accuracy of the gene expression level based on a prediction value of the gene expression level and the measured value of the gene expression level acquired in the (b), the prediction value being acquired by inputting the morphological representation acquired in the (c) to a function obtained by fitting using the morphological representation as input and the measured value of the gene expression level as output (e.g. RNA expression data is input into a deconvolution framework in order to have a deconvoluted RNA expression model that can output an expression level of genes that do not include tissue or areas that are not of interest in the cancer diagnosis, which is taught in ¶ [67]-[69] above. The standardized deconvoluted gene expression is output in step 414, which occurs in step 312 and disclosed in ¶ [82], [92] above. The deconvoluted output increases the accuracy of the prediction of the cancer type by excluding regions that are not of interest. This aids in system using a lowest sum of square error (SSE) in order to select an appropriate gene for the cancer candidate, which is taught in [104] and [105] above.); and
(e) extracting a gene related to a morphological change of the cell cluster as the gene candidate related to a feature of a cancer of the patient based on the prediction accuracy estimated in the (d) (e.g. a cancer type or gene can be selected from a list based on the model evaluating the expression data in comparison with other expression data of other cancers, which is taught in ¶ [67]-[70], [104] and [105] above.).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 2 and 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Igartua in view of Newman (US Pub 2019/0338364).
Re claim 2: Igartua discloses the method according to claim 1, wherein the (a) includes acquiring the microscope image of the cell cluster before administering medication to the cell cluster (e.g. the invention discloses acquiring histopathology slides of a sample associated with cells, which is taught in ¶ [10] and [78] above.).
However, Igartua fails to specifically teach the features of acquiring the microscope image of the cell cluster after administering the medication to the cell cluster.
However, this is well known in the art as evidenced by Newman. Similar to the primary reference, Newman discloses acquiring an image of a sample before and after administering medicine (same field of endeavor or reasonably pertinent to the problem).
Newman discloses acquiring the microscope image of the cell cluster after administering the medication to the cell cluster (e.g. the invention discloses acquiring an image of a sample of cels before and after therapy to analyze the post treatment results for the patient, which is taught in ¶ [35] and [200].).
[0035] FIGS. 6a-6c: Resolution of well-defined mixtures with CIBERSORT. Analysis of CIBERSORT performance using different signature matrices (top) applied to different mixtures (bottom). Top: Cell population reference expression signatures for (FIG. 6a) purified blood cancer cell line expression profiles in GSE11103.sup.5, (FIG. 6b) neural gene expression profiles in GSE19380.sup.6, and (FIG. 6c) LM22 (FIGS. 16a-16k). Bottom: Comparison of known and inferred fractions for defined mixtures of (FIG. 6a) blood cancer cell lines (GSE11103.sup.5) and (FIG. 6b) neural cell types (GSE19380.sup.6). (FIG. 6c) CIBERSORT analysis of pre- and post-Rituximab therapy PBMC samples, including one paired sample, from four Non-Hodgkin's lymphoma patients using LM22 (pooled into 11 leukocyte types for clarity; see FIGS. 16a-16k).
[0200] CIBERSORT was next benchmarked on idealized mixtures with well-defined composition, in which the majority of the mixture can be accounted for by highly distinct (uncorrelated) reference profiles of purified cell types, and the contribution from unknown cell content and noise is minimal.sup.4,11,12. CIBERSORT results were compared with six GEP deconvolution methods—four that take reference expression profiles as input: PERT.sup.6, quadratic programming (QP).sup.5, linear least squares regression (LLSR).sup.4, and robust linear regression (RLR); and two that take genes uniquely expressed in a given cell type as input (i.e., marker genes): MMAD.sup.7 and DSA.sup.8 (FIG. 18). To the best of our knowledge, it is noted that RLR was first applied to GEP deconvolution in this work. CIBERSORT, like other methods, achieved accurate results on idealized mixtures, whether for in vitro mixtures of blood cancer cell lines.sup.4 and neural cell types.sup.12 (FIG. 6a,b), or whole blood.sup.11 (FIG. 1d) (FIG. 19). Consequently, it was asked whether CIBERSORT might be useful for immune monitoring with LM22, and profiled peripheral blood in patients immediately before and after receiving rituximab monotherapy for Non-Hodgkin's lymphoma. CIBERSORT analysis of post-treatment peripheral blood mononuclear cells (PBMCs) with LM22 revealed a selective depletion of B cells targeted by rituximab in four patients (FIG. 6c), suggesting utility for immune monitoring during immunotherapy, especially when specimens cannot be immediately processed.
Therefore, in view of Newman, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of acquiring the microscope image of the cell cluster after administering the medication to the cell cluster, incorporated in the device of Igartua, in order to acquire an image of a cell cluster within a sample before and after administering medication, which aids in monitoring the immune system during immunotherapy or the gauge the effectiveness of a treatment (as stated in Newman ¶ [200]).
Re claim 4: Igartua discloses the method according to claim 2, further comprising (g) acquiring biochemical data of the cancer specimen or the cell cluster cultured from the cancer specimen used in the (a), the biochemical data being other than the gene expression level, or acquiring clinical data acquired in process of diagnosis or treatment of the patient (e.g. the expression levels are acquired of genes as well as determining which cellular activity pathways are dysregulated, which is a form of biochemical data. This is used in the gene expression data analysis process to further refine the expression data, which is taught in ¶ [64].),
[0063] The deconvolution framework 102 may be configured to receive normalized gene expression data and modify such data using a clustering process to optimize the number of clusters, K, such that one or more gene expression clusters associated with one or more cell types of interest are detected. Subsequent analysis of the gene expression clusters may determine cancer-specific cluster types within such data. The deconvolution framework is discussed with more detail with respect to FIG. 2 below.
[0064] Deconvoluted gene expression data may be used in downstream gene expression data analyses and may yield more accurate results than analyzing mixed sample gene expression data. For example, analyses of the mixed sample gene expression data may return results that reflect the background tissue instead of the cancer tissue in the mixed sample. Examples of downstream gene expression data analyses include determining which genes are overexpressed or underexpressed, determining consensus molecular subtypes, predicting a cancer type present in the sample (especially for tumors of unknown origin), detecting infiltrating lymphocytes, determining which cellular activity pathways are dysregulated, discovering biomarkers, matching therapies or clinical trials based on the results of any of these downstream analyses, and designing clinical trials or organoid experiments based on the results of any of these downstream analyses.
wherein,
in the (f), the function is subjected to fitting so that the measured value of the gene expression level acquired in the (b) is output with respect to the input of a combination of the data acquired in the (g) and the morphological representation acquired in the (c) (e.g. the output of the deconvolution model is an expression level. However, the deconvoluted gene expression model analyzes the incoming sample gene expression data with the determining cellular activity pathways, which is taught in ¶ [63] and [64] above. The further analysis can lead to the output of expression of genes of the deconvolution model, which is taught in ¶ [82] and [92] above.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chang discloses predicting PD-L1 status of cancer cell sample.
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/CHAD DICKERSON/ Primary Examiner, Art Unit 2683