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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on July 22, 2026, has been entered.
Response to Amendment
Applicant’s Amendments filed on July 22, 2026, has been entered and made of record.
Currently pending Claim(s) 76-105
Independent Claim(s) 76 and 91
Amended Claim(s) 76 and 91
Response to Arguments
This office action is responsive to Applicant’s Arguments/Remarks Made in an Amendment received on July 22, 2026.
In view of amendments filed on July 22, 2026, to the claims, the Applicant has amended the independent claims 76 and 91 to specify that the image analysis results are a direct input to the trained classification model.
The Applicant argued (Remarks pages 7-9) that the independent claims 76 and 91, as amended, are allowable over Udyavar (US 11,881,286 B2). The Applicant argued that the image analysis results obtained by Udyavar are not a direct input to the classification model for determining a phenotype. Rather, the image analysis results (analyzing CD8+ T-cell quantities in tumor epithelium and stroma and converting the scores into polar coordinates) are utilized for determining quantity and spatial distribution labels for determining genes whose expression levels correlate with the labels.
The Examiner finds this argument to be persuasive. In the previous office action (Final Rejection dated June 03, 2026), the Examiner argued that Udyavar’s classifier (cluster analysis) used genes specific to the quantity and spatial distribution labels derived from the image analysis process; therefore, the labels derived from the image analysis were directly responsible for phenotype predictions output by the classifier. However, claims 76 and 91 are now amended so that the image analysis results must be input directly into a classifier which determines a phenotype prediction from the patterns of stromal and parenchymal CD8+ T-cells. Thus, Udyavar fails to teach every limitation of the amended independent claims, because Udyavar’s classifier determines a phenotype based on correlations between the labels derived from image analysis and gene expression levels rather than receiving the image analysis results directly and determining a phenotype from patterns of stromal and parenchymal CD8+ T-cells.
Additionally, the Applicant argued (Remarks page 10) that Guan (US 11,473,151 B2) fails to make up for the deficiencies of Udyavar discussed above, and the Examiner finds this argument to be persuasive. Guan teaches methods for the treatment of cancer. Guan does not teach a classifier which receives resulting image analysis data from a histology image and outputs a phenotype prediction.
Therefore, the independent claims 76 and 91 are allowable over Udyavar and Guan. However, upon further search and consideration, the Examiner presents new rejections using Yoo (US 2022/0036549 A1) as closest prior art of record. Yoo has foreign application priority to May 8, 2020, and Yoo directly teaches inputting image analysis results of a histology image into a machine learning classifier which outputs an immune phenotype. However, the Examiner does acknowledge that Yoo and Udyavar both fail to teach identifying a polar plot as the image analysis results and utilizing the polar plot for classifier training. See the Allowable Subject Matter section for further details.
Claim Rejections - 35 USC § 102
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.
Claims 76-78, 80, 91-93, and 95 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Yoo et al. (US 2022/0036549 A1), hereafter Yoo.
Regarding claim 76, Yoo teaches a computer-implemented method for identifying a human subject suitable for immunotherapy to treat a tumor of the human subject, the method executing on data processing hardware that causes the data processing hardware to perform operations (See Fig. 2 outlining the information processing system 100.) comprising:
receiving a histology image of a sample of the tumor of the human subject; performing image analysis on the histology image to obtain image analysis results indicating CD8+ T-cell abundance in tumor parenchyma and stroma in the histology image (The target item detection unit 210 of Fig. 2 can receive an image and detect the abundance and distribution of target items (which can be CD8 cells) in the image. [0066] “The target item detection unit 210 may receive the pathology slide image (e.g., H&E-stained pathology slide image, IHC-stained pathology slide image, and the like) and detect one or more target items in the received pathology slide image… the target item detection unit 210 may detect tumor cell, lymphocyte, macrophages, dendritic cell, fibroblast, endothelial cell, blood vessel, cancer stroma, cancer epithelium, cancer area, normal area (e.g., normal lymph node architecture region), and the like, as the target item in the pathology slide image.”);
processing, using a trained tumor topology classification model that comprises a machine learning feature space that includes boundaries for a plurality of possible classifications of CD8 localization, the image analysis results obtained by performing the image analysis on the histology image as a direct input to the trained tumor topology classification model, to determine, from the boundaries of the machine learning feature space for the plurality of possible classifications of CD8 localization, a classification of CD8 localization in the sample of the tumor; and by determining where patterns of stromal CD8+ T-cells and parenchymal CD8+ T- cells in the histology image fall within the boundaries for the plurality of possible classifications in the machine learning feature space (Yoo teaches an immune phenotype determination unit—which may be a neural network in [0069] and [0096]—which receives a region of interest from a histology image along with the quantity and distribution of target items (which can be CD8 cells) detected by the target item detection unit. [0097] “As illustrated, the immune phenotype determination unit 230 may receive one or more ROIs 612, 614, and 616, and generate the immune phenotype determination result 620. In this example, one or more ROIs 612, 614, and 616 may include the detection result for target item for the corresponding ROI. Accordingly, the immune phenotype determination unit 230 may generate the immune phenotype determination result 620 for the ROI that includes the detection result for target item.”
The immune phenotype determination unit determines a phenotype based on the distribution and density of target items (CD8 cells) in the stroma and parenchyma regions. [0094] “…the immune phenotype determination unit 230 may calculate at least one of the number, distribution, or density of target items in one or more ROIs, and may determine an immune phenotype and/or an immune phenotype score of one or more ROIs based on at least one of the calculated number, distribution, or density of the immune cells. For example, the immune phenotype determination unit 230 may calculate, within one or more ROIs, a density of lymphocytes in the cancer area and a density of lymphocytes in the cancer stroma area, and determine the immune phenotype of the one or more ROIs based on at least one of the density of immune cells in the cancer area or the density of immune cells in the cancer stroma.”).
Additionally, Yoo discusses identifying boundaries for the phenotype classifications based on the density of target items (CD8 cells) in the parenchyma and stroma. In [0094-0095] Yoo provides examples where the density of immune cells in the stoma vs parenchyma region determines the phenotype prediction, and Yoo provides threshold numbers for the cell density, which corresponds to boundaries.); and
generating a recommendation for a treatment option for the human subject based on the classification of the CD8 localization in the sample of the tumor (Yoo teaches in the background section that immune response information allows doctors to predict the response to an immune checkpoint inhibitor; thus, a doctor is informed for generating a recommendation for a treatment option. [0004] “…in order to predict the response to the immune checkpoint inhibitor, a user (e.g., a doctor, a patient, and the like) may be provided with immune response information generated through a pathology slide image of a patient's tissue.” Additionally, Yoo teaches an output unit for predicting if a patient would respond to a checkpoint inhibitor. [0072] “In another embodiment, the information processing system 100 may further include an output unit (not illustrated), and this output unit may output at least one of a detection result for the one or more target items, an immune phenotype of one or more ROIs, a prediction result as to whether or not the patient responds to immune checkpoint inhibitor…”).
Regarding claim 77, Yoo teaches the method of claim 76, wherein the plurality of possible classifications of CD8 localization comprises inflamed, desert, excluded, and balanced ([0094] “…the immune phenotype determination unit 230 may determine the immune phenotype of one or more ROIs as one of immune inflamed, immune excluded, or immune desert, by referring to the number of immune cells included in the specific region in the cancer area.”).
Regarding claim 78, Yoo teaches the method of claim 77. wherein the classification of CD8 localization in the sample of the tumor comprises excluded ([0094] “…the immune phenotype determination unit 230 may determine the immune phenotype of one or more ROIs as one of immune inflamed, immune excluded, or immune desert, by referring to the number of immune cells included in the specific region in the cancer area.”).
Regarding claim 80, Yoo teaches the method of claim 76, wherein the trained tumor topology classification model is trained by a training process that comprises: receiving a plurality of training histology images of tumor samples in a plurality of patients; for each training histology image in the plurality of training histology images, performing image analysis on the training histology image to obtain a CD8+ T-cell abundance in the tumor parenchyma and stroma in the training histology image; training the tumor topology classification model using results of the image analysis and the CD8+ T-cell abundance in the tumor parenchyma and stroma in each of the plurality of training histology images; generating the machine learning feature space comprising the plurality of classifications based on the training (Yoo teaches that the classifier may be an artificial neural network, and supervised learning is utilized for training. This process would include receiving images where the image analysis features (such as CD8 distribution and density) have been determined by the target item detection unit. [0096] “In this example, the artificial neural network immune phenotype classification model may correspond to a classifier that is trained to determine the immune phenotype of the reference ROI as one of immune inflamed, immune excluded, or immune desert upon input of the feature for the reference ROI. In addition, in this example, the feature for each of one or more ROIs may include a statistical feature for one or more target items in each of one or more ROIs (e.g., density, number, and the like, of specific target items in the ROI), a geometric feature for one or more target items (e.g., a feature including relative position information between specific target items, and the like), and/or an image feature, and the like corresponding to each of the one or more ROIs (e.g., a feature extracted from a plurality of pixels included in the ROIs, an image vector corresponding to the ROIs, and the like.” [0113] “For example, the information processing system may train the artificial neural network model 900 by supervised learning to detect one or more target items from the pathology slide image by using a reference pathology slide image and label information for one or more reference target items.”); and
identifying boundaries between the plurality of possible classifications of CD8 localization (Yoo discusses identifying boundaries for the phenotype classifications based on the density of lymphocytes in the parenchyma and stroma. In [0094-0095], Yoo provides examples where the density of immune cells in the stoma vs parenchyma region determines the phenotype prediction, and Yoo provides threshold numbers for the cell density, which corresponds to boundaries.).
Regarding claim 91, Yoo teaches a system comprising: data processing hardware; and memory hardware in communication with the data processing hardware and storing instructions that when executed on the data processing hardware causes the data processing hardware to perform operations for identifying a human subject suitable for immunotherapy to treat a tumor of the human subject, the operations (See Fig. 2 outlining the information processing system 100.) comprising:
receiving a histology image of a sample of the tumor of the human subject; performing image analysis on the histology image to obtain image analysis results indicating CD8+ T-cell abundance in tumor parenchyma and stroma in the histology image (The target item detection unit 210 of Fig. 2 can receive an image and detect the abundance and distribution of target items (which can be CD8 cells) in the image. [0066] “The target item detection unit 210 may receive the pathology slide image (e.g., H&E-stained pathology slide image, IHC-stained pathology slide image, and the like) and detect one or more target items in the received pathology slide image… the target item detection unit 210 may detect tumor cell, lymphocyte, macrophages, dendritic cell, fibroblast, endothelial cell, blood vessel, cancer stroma, cancer epithelium, cancer area, normal area (e.g., normal lymph node architecture region), and the like, as the target item in the pathology slide image.”);
processing, using a trained tumor topology classification model that comprises a machine learning feature space that includes boundaries for a plurality of possible classifications of CD8 localization, the image analysis results obtained by performing the image analysis on the histology image as a direct input to the trained tumor topology classification model to determine, from the boundaries of the machine learning feature space for the plurality of possible classifications of CD8 localization, a classification of CD8 localization in the sample of the tumor; and by determining where patterns of stromal CD8+ T-cells and parenchymal CD8+ T- cells in the histology image fall within the boundaries for the plurality of possible classifications in the machine learning feature space (Yoo teaches an immune phenotype determination unit—which may be a neural network in [0069] and [0096]—which receives a region of interest from a histology image along with the quantity and distribution of target items (which can be CD8 cells) detected by the target item detection unit. [0097] “As illustrated, the immune phenotype determination unit 230 may receive one or more ROIs 612, 614, and 616, and generate the immune phenotype determination result 620. In this example, one or more ROIs 612, 614, and 616 may include the detection result for target item for the corresponding ROI. Accordingly, the immune phenotype determination unit 230 may generate the immune phenotype determination result 620 for the ROI that includes the detection result for target item.”
The immune phenotype determination unit determines a phenotype based on the distribution and density of target items (CD8 cells) in the stroma and parenchyma regions. [0094] “…the immune phenotype determination unit 230 may calculate at least one of the number, distribution, or density of target items in one or more ROIs, and may determine an immune phenotype and/or an immune phenotype score of one or more ROIs based on at least one of the calculated number, distribution, or density of the immune cells. For example, the immune phenotype determination unit 230 may calculate, within one or more ROIs, a density of lymphocytes in the cancer area and a density of lymphocytes in the cancer stroma area, and determine the immune phenotype of the one or more ROIs based on at least one of the density of immune cells in the cancer area or the density of immune cells in the cancer stroma.”).
Additionally, Yoo discusses identifying boundaries for the phenotype classifications based on the density of target items (CD8 cells) in the parenchyma and stroma. In [0094-0095] Yoo provides examples where the density of immune cells in the stoma vs parenchyma region determines the phenotype prediction, and Yoo provides threshold numbers for the cell density, which corresponds to boundaries.); and
generating a recommendation for a treatment option for the human subject based on the classification of the CD8 localization in the sample of the tumor (Yoo teaches in the background section that immune response information allows doctors to predict the response to an immune checkpoint inhibitor; thus, a doctor is informed for generating a recommendation for a treatment option. [0004] “…in order to predict the response to the immune checkpoint inhibitor, a user (e.g., a doctor, a patient, and the like) may be provided with immune response information generated through a pathology slide image of a patient's tissue.” Additionally, Yoo teaches an output unit for predicting if a patient would respond to a checkpoint inhibitor. [0072] “In another embodiment, the information processing system 100 may further include an output unit (not illustrated), and this output unit may output at least one of a detection result for the one or more target items, an immune phenotype of one or more ROIs, a prediction result as to whether or not the patient responds to immune checkpoint inhibitor…”).
Regarding claim 92, Yoo teaches the system of claim 91, wherein the plurality of possible classifications of CD8 localization comprises inflamed, desert, excluded, and balanced. ([0094] “…the immune phenotype determination unit 230 may determine the immune phenotype of one or more ROIs as one of immune inflamed, immune excluded, or immune desert, by referring to the number of immune cells included in the specific region in the cancer area.”).
Regarding claim 93, Yoo teaches the system of claim 92, wherein the classification of CD8 localization in the sample of the tumor comprises excluded ([0094] “…the immune phenotype determination unit 230 may determine the immune phenotype of one or more ROIs as one of immune inflamed, immune excluded, or immune desert, by referring to the number of immune cells included in the specific region in the cancer area.”).
Regarding claim 95, Yoo teaches the system of claim 91, wherein the trained tumor topology classification model is trained by a training process that comprises:
receiving a plurality of training histology images of tumor samples in a plurality of patients; for each training histology image in the plurality of training histology images, performing image analysis on the training histology image to obtain a CD8+ T-cell abundance in the tumor parenchyma and stroma in the training histology image; training the tumor topology classification model using results of the image analysis and the CD8+ T-cell abundance in the tumor parenchyma and stroma in each of the plurality of training histology images; generating the machine learning feature space comprising the plurality of classifications based on the training (Yoo teaches that the classifier may be an artificial neural network, and supervised learning is utilized for training. This process would include receiving images where the image analysis features (such as CD8 distribution and density) have been determined by the target item detection unit. [0096] “In this example, the artificial neural network immune phenotype classification model may correspond to a classifier that is trained to determine the immune phenotype of the reference ROI as one of immune inflamed, immune excluded, or immune desert upon input of the feature for the reference ROI. In addition, in this example, the feature for each of one or more ROIs may include a statistical feature for one or more target items in each of one or more ROIs (e.g., density, number, and the like, of specific target items in the ROI), a geometric feature for one or more target items (e.g., a feature including relative position information between specific target items, and the like), and/or an image feature, and the like corresponding to each of the one or more ROIs (e.g., a feature extracted from a plurality of pixels included in the ROIs, an image vector corresponding to the ROIs, and the like.” [0113] “For example, the information processing system may train the artificial neural network model 900 by supervised learning to detect one or more target items from the pathology slide image by using a reference pathology slide image and label information for one or more reference target items.”); and
identifying boundaries between the plurality of possible classifications of CD8 localization (Yoo discusses identifying boundaries for the phenotype classifications based on the density of lymphocytes in the parenchyma and stroma. In [0094-0095], Yoo provides examples where the density of immune cells in the stoma vs parenchyma region determines the phenotype prediction, and Yoo provides threshold numbers for the cell density, which corresponds to boundaries.).
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.
Claims 79 and 94 are rejected under 35 U.S.C. 103 as being unpatentable over Yoo (US 2022/0036549 A1) in view of Guan et al. (US 11,473,151 B2), hereafter Guan.
Regarding claim 79, Yoo teaches the method of claim 76, wherein the operations further comprise: determining that the sample of the tumor exhibits a negative PD-L1 expression status (Yoo teaches that the output statistics displayed to the user may include information about the PD-L1 expression; thus, the system is able to determine the PD-L1 expression status based on results from the classification. [0076] “…the classification map may include a map visualizing a result of classifying a tumor proportion score (TPS) and/or a combined proportion score (CPS), which are information related to the expression of PD-L1, based on a specific threshold.”), but Yoo fails to teach wherein generating the recommendation for the immunotherapy is further based on the determination that the sample of the tumor exhibits the negative PD-L1 expression status.
However, Guan teaches wherein generating the recommendation for the immunotherapy is further based on the determination that the sample of the tumor exhibits the negative PD-L1 expression status (([Col. 149, lines 4-8] “PD-L1 scores in patients with multiple specimens from different time points or samples were based on the highest score. This assay was validated for investigational use in clinical trials at the IC1 and IC2 cutoff.” [Col. 162, lines 18-21] “Assessment of the association of PD-L1 expression on tumor-infiltrating immune cells (IC) on baseline tumors with response (objective response rate) was a co-primary endpoint.” [Col. 162, lines 25-31] “As found previously in a smaller cohort of patients, PD-L1 expression on IC was significantly associated with response, and tumors with high PD-L1 expression (IC2+) displayed the highest CR rate (FIG. 1A). Improved OS benefit from atezolizumab was observed in patients with high PD-L1 IC scores (IC2/3) relative to patients with lower PD-L1 IC scores (IC0/1) (FIG. 8A).”)).
Yoo and Guan are analogous to the claimed invention, because both teach methods of determining the localization and spatial distribution of CD8 cells for determining a phenotype and informing treatment plans. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yoo’s invention by considering the PD-L1 expression status when determining potential treatment options. This modification would allow for PD-L1 expression to impact the decision-making process, which is important since PD-L1 expression levels impact a patient’s complete response rate to treatments proposed by the claimed invention, such as atezolizumab (Guan [Col. 162, lines 25-31] “As found previously in a smaller cohort of patients, PD-L1 expression on IC was significantly associated with response, and tumors with high PD-L1 expression (IC2+) displayed the highest CR rate (FIG. 1A). Improved OS benefit from atezolizumab was observed in patients with high PD-L1 IC scores (IC2/3) relative to patients with lower PD-L1 IC scores (IC0/1) (FIG. 8A).”).
Regarding claim 94, Yoo teaches the system of claim 91, wherein the operations further comprise: determining that the sample of the tumor exhibits a negative PD-L1 expression status (Yoo teaches that the output statistics displayed to the user may include information about the PD-L1 expression; thus, the system is able to determine the PD-L1 expression status based on results from the classification. [0076] “…the classification map may include a map visualizing a result of classifying a tumor proportion score (TPS) and/or a combined proportion score (CPS), which are information related to the expression of PD-L1, based on a specific threshold.”), but Yoo fails to teach wherein generating the recommendation for the immunotherapy is further based on the determination that the sample of the tumor exhibits the negative PD-L1 expression status.
However, Guan teaches wherein generating the recommendation for the immunotherapy is further based on the determination that the sample of the tumor exhibits the negative PD-L1 expression status (([Col. 149, lines 4-8] “PD-L1 scores in patients with multiple specimens from different time points or samples were based on the highest score. This assay was validated for investigational use in clinical trials at the IC1 and IC2 cutoff.” [Col. 162, lines 18-21] “Assessment of the association of PD-L1 expression on tumor-infiltrating immune cells (IC) on baseline tumors with response (objective response rate) was a co-primary endpoint.” [Col. 162, lines 25-31] “As found previously in a smaller cohort of patients, PD-L1 expression on IC was significantly associated with response, and tumors with high PD-L1 expression (IC2+) displayed the highest CR rate (FIG. 1A). Improved OS benefit from atezolizumab was observed in patients with high PD-L1 IC scores (IC2/3) relative to patients with lower PD-L1 IC scores (IC0/1) (FIG. 8A).”)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yoo’s invention by considering the PD-L1 expression status when determining potential treatment options. This modification would allow for PD-L1 expression to impact the decision-making process, which is important since PD-L1 expression levels impact a patient’s complete response rate to treatments proposed by the claimed invention, such as atezolizumab (Guan [Col. 162, lines 25-31] “As found previously in a smaller cohort of patients, PD-L1 expression on IC was significantly associated with response, and tumors with high PD-L1 expression (IC2+) displayed the highest CR rate (FIG. 1A). Improved OS benefit from atezolizumab was observed in patients with high PD-L1 IC scores (IC2/3) relative to patients with lower PD-L1 IC scores (IC0/1) (FIG. 8A).”).
Claims 81, 84-87, 89, 96, 99-102, and 104 are rejected under 35 U.S.C. 103 as being unpatentable over Yoo (US 2022/0036549 A1) in view of Udyavar et al. (US 11,881,286 B2), hereafter Udyavar.
Regarding claim 81, Yoo teaches the method of claim 80. Yoo further teaches identifying each target item (which can be CD8 cells) and the densities of the target items in the parenchyma and stroma areas. In [0094-0095] Yoo provides examples where the density of immune cells in the stoma vs parenchyma region determines the phenotype prediction, and Yoo provides threshold numbers for the cell density, which corresponds to boundaries. However, Yoo fails to teach wherein the CD8+ T-cell abundance comprises a graphical representation of a relationship between percentages of stromal CD8+ T-cells and percentage of parenchymal CD8+ T-cells with respect to a total number of T-cells present in the histology image.
However, Udyavar teaches wherein the CD8+ T-cell abundance comprises a graphical representation of a relationship between percentages of stromal CD8+ T-cells and percentage of parenchymal CD8+ T-cells with respect to a total number of T-cells present in the histology image (When defining boundaries between phenotype classifications, Udyavar represents the findings as a polar plot. [Col. 22, lines 4-11] “Metrics were defined to include a total CD8+ T cell count, a CD8+ T cell count per tumor epithelium and/or a CD8+ T cell count stroma area (See FIG. 4 a ). To better capture and quantify the CD8 infiltration patterns, the CD8 scores were converted into polar coordinates defining two new quantitative metrics: 1) the quantity of CD8+ T cells (R=squareroot [(CD8 tumor)2+(CD8 stroma)2]) and 2) the spatial distribution of CD8+ T cells (θ=atan(CD8 stroma/CD8 tumor)).”).
Yoo and Udyavar are analogous in the art to the claimed invention because both teach methods of using a classifier to determine an immune phenotype for a histology image. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yoo’s invention by graphically representing CD8 infiltration patterns. This modification would improve Yoo’s invention by allowing for the patterns to be better captured and quantified visually compared to Yoo’s methods of identifying thresholds ([Udyavar Col. 22, lines 4-11] “To better capture and quantify the CD8 infiltration patterns, the CD8 scores were converted into polar coordinates defining two new quantitative metrics: 1) the quantity of CD8+ T cells (R=squareroot [(CD8 tumor)2+(CD8 stroma)2]) and 2) the spatial distribution of CD8+ T cells (θ=atan(CD8 stroma/CD8 tumor)).”).
Regarding claim 84, Yoo teaches the method of claim 76. Yoo further teaches in the background section that immune response information allows doctors to predict the response to an immune checkpoint inhibitor; thus, the doctor is informed for providing a recommendation for a treatment option. Additionally, Yoo teaches an output unit for predicting if a patient would respond to a checkpoint inhibitor in [0072]. However, Yoo does not provide additional details about specific treatment options and how they are selected; thus, Yoo fails to teach wherein the treatment option comprises immunotherapy.
However, Udyavar teaches wherein the treatment option comprises immunotherapy ([Col. 3, lines 5-14] “In some embodiments, a method of treatment is provided that includes targeting the TGFβ pathway… Thus, targeting the TGFβ pathway may overcome T cell exclusion from tumors and improve subjects' response to cancer immunotherapy.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yoo’s invention by selecting immunotherapy as a treatment option by considering the classification of the phenotype. Udyavar teaches that different CD8+ T-cell distributions elicit different responses to immunotherapy, and Udyavar motivates classifying the phenotype to inform treatment options, including immunotherapy ([Col. 1, lines 51-55] “Given that effective cytotoxic killing requires direct contact between CD8+ T cells and tumor cells, it has been increasingly recognized that different CD8+ T cell distributions in the tumor microenvironment (TME) may elicit different responses to immunotherapies.” [Col. 1, lines 16-18] “Systems and methods relate to using expression levels for a set of genes in order to identify a phenotype of a tumor and to identify a treatment candidate based on the phenotype.”).
Regarding claim 85, Yoo teaches the method of claim 76 but fails to teach wherein the immunotherapy comprises an anti-PD-1/PD-L1 antagonist therapy.
However, Udyavar teaches wherein the immunotherapy comprises an anti-PD-1/PD-L1 antagonist therapy ([Col. 2, lines 36-41] “The label identified by the classifier for a given data set can be used to identify a treatment candidate. For example, the treatment candidate may include anti-TGFβ (and potentially also a checkpoint inhibitor, such as anti-PD-L1) when the phenotype is identified as an immune-excluded phenotype.” [Col. 4, line 5] “The checkpoint inhibitor includes anti-PD-L1.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yoo’s invention by selecting anti-PD-1/PD-L1 antagonist therapy as a treatment option. Udyavar teaches that this modification would provide an appropriate treatment option when the patent exhibits an excluded phenotype ([Col. 2, lines 36-41] “The label identified by the classifier for a given data set can be used to identify a treatment candidate. For example, the treatment candidate may include anti-TGFβ (and potentially also a checkpoint inhibitor, such as anti-PD-L1) when the phenotype is identified as an immune-excluded phenotype.”). Furthermore, Udyavar motivates determining a treatment option, including immunotherapy, based on a phenotype classification for a histology image ([Col. 1, lines 16-18] “Systems and methods relate to using expression levels for a set of genes in order to identify a phenotype of a tumor and to identify a treatment candidate based on the phenotype.”).
Regarding claim 86, Yoo and Udyavar teach the method of claim 85. Udyavar further teaches wherein the anti-PD-1/PD-L1 antagonist comprises an antibody or antigen-binding fragment thereof that specifically binds a target protein selected from programmed death 1 (PD-1; an "anti-PD-1 antibody") or programmed death ligand 1 (PD-L1; an "anti-PD-L1 antibody”) ([Col. 12, lines 22-29] “A tumor-immune phenotype can be used to inform treatment decisions and/or generate predictions as to whether and/or a degree to which a particular subject will respond to a particular treatment. For example: an immune checkpoint inhibitor therapy may be recommended, more likely to be recommended and/or predicted to be more effective for the inflamed/infiltrated phenotype (e.g., relative to the other phenotypes);” [Col. 4, line 5] “The checkpoint inhibitor includes anti-PD-L1.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yoo’s invention by selecting an antibody or antigen-binding fragment as a treatment option. Udyavar teaches that this modification would provide an appropriate treatment option when the patent exhibits an inflamed/infiltrated phenotype ([Col. 12, lines 22-29] “A tumor-immune phenotype can be used to inform treatment decisions and/or generate predictions as to whether and/or a degree to which a particular subject will respond to a particular treatment. For example: an immune checkpoint inhibitor therapy may be recommended, more likely to be recommended and/or predicted to be more effective for the inflamed/infiltrated phenotype (e.g., relative to the other phenotypes);”). Furthermore, Udyavar motivates determining a treatment option, including immunotherapy, based on a phenotype classification for a histology image ([Col. 1, lines 16-18] “Systems and methods relate to using expression levels for a set of genes in order to identify a phenotype of a tumor and to identify a treatment candidate based on the phenotype.”).
Regarding claim 87, Yoo and Udyavar teach the method of claim 85. Udyavar further teaches wherein the anti-PD-1/PD-L1 antagonist comprises an anti- PD-1 antibody ([Col. 4, line 5] “The checkpoint inhibitor includes anti-PD-L1.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yoo’s invention by selecting anti-PD-1/PD-L1 antagonist therapy as a treatment option. Udyavar teaches that this modification would provide an appropriate treatment option when the patent exhibits an excluded phenotype ([Col. 2, lines 36-41] “The label identified by the classifier for a given data set can be used to identify a treatment candidate. For example, the treatment candidate may include anti-TGFβ (and potentially also a checkpoint inhibitor, such as anti-PD-L1) when the phenotype is identified as an immune-excluded phenotype.”).
Regarding claim 89, Yoo and Udyavar teach the method of claim 85. Udyavar further teaches wherein the anti-PD-1/PD-L1 antagonist comprises an anti- PD-L1 antibody ([Col. 4, line 5] “The checkpoint inhibitor includes anti-PD-L1.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yoo’s invention by selecting anti-PD-1/PD-L1 antagonist therapy as a treatment option. Udyavar teaches that this modification would provide an appropriate treatment option when the patent exhibits an excluded phenotype ([Col. 2, lines 36-41] “The label identified by the classifier for a given data set can be used to identify a treatment candidate. For example, the treatment candidate may include anti-TGFβ (and potentially also a checkpoint inhibitor, such as anti-PD-L1) when the phenotype is identified as an immune-excluded phenotype.”).
Regarding claim 96, Yoo teaches the system of claim 95. Yoo further teaches identifying each target item (which can be CD8 cells) and the densities of the target items in the parenchyma and stroma areas. In [0094-0095] Yoo provides examples where the density of immune cells in the stoma vs parenchyma region determines the phenotype prediction, and Yoo provides threshold numbers for the cell density, which corresponds to boundaries. However, Yoo fails to teach wherein the CD8+ T-cell abundance comprises a graphical representation of a relationship between percentages of stromal CD8+ T-cells and percentage of parenchymal CD8+ T-cells with respect to a total number of T-cells present in the histology image.
However, Udyavar teaches wherein the CD8+ T-cell abundance comprises a graphical representation of a relationship between percentages of stromal CD8+ T- cells and percentage of parenchymal CD8+ T-cells with respect to a total number of T-cells present in the histology image (When defining boundaries between phenotype classifications, Udyavar represents the findings as a polar plot. [Col. 22, lines 4-11] “Metrics were defined to include a total CD8+ T cell count, a CD8+ T cell count per tumor epithelium and/or a CD8+ T cell count stroma area (See FIG. 4 a ). To better capture and quantify the CD8 infiltration patterns, the CD8 scores were converted into polar coordinates defining two new quantitative metrics: 1) the quantity of CD8+ T cells (R=squareroot [(CD8 tumor)2+(CD8 stroma)2]) and 2) the spatial distribution of CD8+ T cells (θ=atan(CD8 stroma/CD8 tumor)).”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yoo’s invention by graphically representing CD8 infiltration patterns. This modification would improve Yoo’s invention by allowing for the patterns to be better captured and quantified visually compared to Yoo’s methods of identifying thresholds ([Udyavar Col. 22, lines 4-11] “To better capture and quantify the CD8 infiltration patterns, the CD8 scores were converted into polar coordinates defining two new quantitative metrics: 1) the quantity of CD8+ T cells (R=squareroot [(CD8 tumor)2+(CD8 stroma)2]) and 2) the spatial distribution of CD8+ T cells (θ=atan(CD8 stroma/CD8 tumor)).”).
Regarding claim 99, Yoo teaches the system of claim 91. Yoo further teaches in the background section that immune response information allows doctors to predict the response to an immune checkpoint inhibitor; thus, the doctor is informed for providing a recommendation for a treatment option. Additionally, Yoo teaches an output unit for predicting if a patient would respond to a checkpoint inhibitor in [0072]. However, Yoo does not provide additional details about specific treatment options and how they are selected; thus, Yoo fails to teach wherein the treatment option comprises immunotherapy.
However, Udyavar teaches wherein the treatment option comprises immunotherapy ([Col. 3, lines 5-14] “In some embodiments, a method of treatment is provided that includes targeting the TGFβ pathway… Thus, targeting the TGFβ pathway may overcome T cell exclusion from tumors and improve subjects' response to cancer immunotherapy.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yoo’s invention by selecting immunotherapy as a treatment option by considering the classification of the phenotype. Udyavar teaches that different CD8+ T-cell distributions elicit different responses to immunotherapy, and Udyavar motivates classifying the phenotype to inform treatment options, including immunotherapy ([Col. 1, lines 51-55] “Given that effective cytotoxic killing requires direct contact between CD8+ T cells and tumor cells, it has been increasingly recognized that different CD8+ T cell distributions in the tumor microenvironment (TME) may elicit different responses to immunotherapies.” [Col. 1, lines 16-18] “Systems and methods relate to using expression levels for a set of genes in order to identify a phenotype of a tumor and to identify a treatment candidate based on the phenotype.”).
Regarding claim 100, Yoo teaches the system of claim 91 but fails to teach wherein the immunotherapy comprises an anti-PD-1/PD-L1 antagonist therapy.
However, Udyavar teaches wherein the immunotherapy comprises an anti-PD-1/PD-L1 antagonist therapy ([Col. 2, lines 36-41] “The label identified by the classifier for a given data set can be used to identify a treatment candidate. For example, the treatment candidate may include anti-TGFβ (and potentially also a checkpoint inhibitor, such as anti-PD-L1) when the phenotype is identified as an immune-excluded phenotype.” [Col. 4, line 5] “The checkpoint inhibitor includes anti-PD-L1.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yoo’s invention by selecting anti-PD-1/PD-L1 antagonist therapy as a treatment option. Udyavar teaches that this modification would provide an appropriate treatment option when the patent exhibits an excluded phenotype ([Col. 2, lines 36-41] “The label identified by the classifier for a given data set can be used to identify a treatment candidate. For example, the treatment candidate may include anti-TGFβ (and potentially also a checkpoint inhibitor, such as anti-PD-L1) when the phenotype is identified as an immune-excluded phenotype.”). Furthermore, Udyavar motivates determining a treatment option, including immunotherapy, based on a phenotype classification for a histology image ([Col. 1, lines 16-18] “Systems and methods relate to using expression levels for a set of genes in order to identify a phenotype of a tumor and to identify a treatment candidate based on the phenotype.”).
Regarding claim 101, Yoo and Udyavar teach the system of claim 100. Udyavar further teaches wherein the anti-PD-1/PD-L1 antagonist comprises an antibody or antigen-binding fragment thereof that specifically binds a target protein selected from programmed death 1 (PD-1; an "anti-PD-1 antibody") or programmed death ligand 1 (PD-L1; an "anti-PD-L1 antibody”) ([Col. 12, lines 22-29] “A tumor-immune phenotype can be used to inform treatment decisions and/or generate predictions as to whether and/or a degree to which a particular subject will respond to a particular treatment. For example: an immune checkpoint inhibitor therapy may be recommended, more likely to be recommended and/or predicted to be more effective for the inflamed/infiltrated phenotype (e.g., relative to the other phenotypes);” [Col. 4, line 5] “The checkpoint inhibitor includes anti-PD-L1.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yoo’s invention by selecting an antibody or antigen-binding fragment as a treatment option. Udyavar teaches that this modification would provide an appropriate treatment option when the patent exhibits an inflamed/infiltrated phenotype ([Col. 12, lines 22-29] “A tumor-immune phenotype can be used to inform treatment decisions and/or generate predictions as to whether and/or a degree to which a particular subject will respond to a particular treatment. For example: an immune checkpoint inhibitor therapy may be recommended, more likely to be recommended and/or predicted to be more effective for the inflamed/infiltrated phenotype (e.g., relative to the other phenotypes);”). Furthermore, Udyavar motivates determining a treatment option, including immunotherapy, based on a phenotype classification for a histology image ([Col. 1, lines 16-18] “Systems and methods relate to using expression levels for a set of genes in order to identify a phenotype of a tumor and to identify a treatment candidate based on the phenotype.”).
Regarding claim 102, Yoo and Udyavar teach the system of claim 101. Udyavar further teaches wherein the anti-PD-1/PD-L1 antagonist comprises an anti- PD-1 antibody ([Col. 4, line 5] “The checkpoint inhibitor includes anti-PD-L1.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yoo’s invention by selecting anti-PD-1/PD-L1 antagonist therapy as a treatment option. Udyavar teaches that this modification would provide an appropriate treatment option when the patent exhibits an excluded phenotype ([Col. 2, lines 36-41] “The label identified by the classifier for a given data set can be used to identify a treatment candidate. For example, the treatment candidate may include anti-TGFβ (and potentially also a checkpoint inhibitor, such as anti-PD-L1) when the phenotype is identified as an immune-excluded phenotype.”).
Regarding claim 104, Yoo and Udyavar teach the system of claim 100. Udyavar further teaches wherein the anti-PD-1/PD-L1 antagonist comprises an anti- PD-L1 antibody ([Col. 4, line 5] “The checkpoint inhibitor includes anti-PD-L1.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yoo’s invention by selecting anti-PD-1/PD-L1 antagonist therapy as a treatment option. Udyavar teaches that this modification would provide an appropriate treatment option when the patent exhibits an excluded phenotype ([Col. 2, lines 36-41] “The label identified by the classifier for a given data set can be used to identify a treatment candidate. For example, the treatment candidate may include anti-TGFβ (and potentially also a checkpoint inhibitor, such as anti-PD-L1) when the phenotype is identified as an immune-excluded phenotype.”).
Claims 88, 90, 103, and 105 are rejected under 35 U.S.C. 103 as being unpatentable over Yoo (US 2022/0036549 A1) in view of Udyavar (US 11,881,286 B2) and further in view of Guan (US 11,473,151 B2).
Regarding claim 88, Yoo and Udyavar teach the method of claim 87, but both fail to teach wherein the anti-PD-1 antibody comprises nivolumab or pembrolizumab.
However, Guan teaches wherein the anti-PD-1 antibody comprises nivolumab or pembrolizumab ([Col. 40, lines 24-27] “In a specific aspect, a PD-1 binding antagonist is MDX-1106 (nivolumab). In another specific aspect, a PD-1 binding antagonist is MK-3475 (pembrolizumab).”).
Yoo, Udyavar, and Guan are analogous to the claimed invention, because all teach methods of determining the localization and spatial distribution of CD8 cells for classifying tumors into phenotype groups for informing treatment plans. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention taught by Yoo and Udyavar by using nivolumab or pembrolizumab as the anti-PD-1 antibody. This modification would allow for the use of an antibody which is sufficiently capable of binding PD-L1 (Guan [Col. 40, lines 35-40] “The terms “anti-PD-L1 antibody” and “an antibody that binds to PD-L1” refer to an antibody that is capable of binding PD-L1 with sufficient affinity such that the antibody is useful as a diagnostic and/or therapeutic agent in targeting PD-L1.”).
Regarding claim 90, Yoo and Udyavar teach the method of claim 89 but both fail to teach wherein the anti-PD-L1 antibody comprises avelumab, atezolizumab, or durvalumab.
However, Guan teaches wherein the anti-PD-L1 antibody comprises avelumab, atezolizumab, or durvalumab ([Col. 10, lines 25-28] “In some embodiments, the anti-PD-L1 antibody is selected from the group consisting of: MPDL3280A (atezolizumab), YW243.55.S70, MDX-1105, MED14736 (durvalumab), and MSB0010718C (avelumab).”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention taught by Yoo and Udyavar by using avelumab, atezolizumab, or durvalumab as the anti-PD-1 antibody. This modification allow for the use an antibody which is sufficiently capable of binding PD-L1 (Guan [Col. 40, lines 35-40] “The terms “anti-PD-L1 antibody” and “an antibody that binds to PD-L1” refer to an antibody that is capable of binding PD-L1 with sufficient affinity such that the antibody is useful as a diagnostic and/or therapeutic agent in targeting PD-L1.”).
Regarding claim 103, Yoo and Udyavar teach the system of claim 100, but both fail to teach wherein the anti-PD-1 antibody comprises nivolumab or pembrolizumab.
However, Guan teaches wherein the anti-PD-1 antibody comprises nivolumab or pembrolizumab ([Col. 40, lines 24-27] “In a specific aspect, a PD-1 binding antagonist is MDX-1106 (nivolumab). In another specific aspect, a PD-1 binding antagonist is MK-3475 (pembrolizumab).”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention taught by Yoo and Udyavar by using nivolumab or pembrolizumab as the anti-PD-1 antibody. This modification would allow for the use of an antibody which is sufficiently capable of binding PD-L1 (Guan [Col. 40, lines 35-40] “The terms “anti-PD-L1 antibody” and “an antibody that binds to PD-L1” refer to an antibody that is capable of binding PD-L1 with sufficient affinity such that the antibody is useful as a diagnostic and/or therapeutic agent in targeting PD-L1.”).
Regarding claim 105, Yoo and Udyavar teach the system of claim 104 but both fail to teach wherein the anti-PD-L1 antibody comprises avelumab, atezolizumab, or durvalumab.
However, Guan teaches wherein the anti-PD-L1 antibody comprises avelumab, atezolizumab, or durvalumab ([Col. 10, lines 25-28] “In some embodiments, the anti-PD-L1 antibody is selected from the group consisting of: MPDL3280A (atezolizumab), YW243.55.S70, MDX-1105, MED14736 (durvalumab), and MSB0010718C (avelumab).”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention taught by Yoo and Udyavar by using avelumab, atezolizumab, or durvalumab as the anti-PD-1 antibody. This modification allow for the use an antibody which is sufficiently capable of binding PD-L1 (Guan [Col. 40, lines 35-40] “The terms “anti-PD-L1 antibody” and “an antibody that binds to PD-L1” refer to an antibody that is capable of binding PD-L1 with sufficient affinity such that the antibody is useful as a diagnostic and/or therapeutic agent in targeting PD-L1.”).
Allowable Subject Matter
Claim 82-83 and 97-98 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claim 82, Yoo and Udyavar teach the method of claim 81. Udyavar further teaches wherein the operations further comprise: applying a polar coordinate transformation on the graphical representation, resulting in a polar plot ([Col. 22, lines 4-11] “Metrics were defined to include a total CD8+ T cell count, a CD8+ T cell count per tumor epithelium and/or a CD8+ T cell count stroma area (See FIG. 4 a ). To better capture and quantify the CD8 infiltration patterns, the CD8 scores were converted into polar coordinates defining two new quantitative metrics: 1) the quantity of CD8+ T cells (R=squareroot [(CD8 tumor)2+(CD8 stroma)2]) and 2) the spatial distribution of CD8+ T cells (θ=atan(CD8 stroma/CD8 tumor)).”).
However, neither Yoo nor Udyavar teach wherein training the tumor topology classification model is further based on using the polar plot. Yoo teaches identifying boundaries for the phenotype classifications based on the density of target items (which can be CD8 cells) in the parenchyma and stroma. In [0094-0095] Yoo provides examples where the density of immune cells in the stoma vs parenchyma region determines the phenotype prediction, and Yoo provides threshold numbers for the cell density, which corresponds to boundaries. However, Yoo does not teach a polar plot.
Although Udyavar teaches identifying boundaries for phenotypes using a polar plot of CD8+ T-cell localization, Udyavar does not teach utilizing the polar plot for training of a classification model. Furthermore, as discussed in the Response to Arguments section, Udyavar’s classifier differs from the claimed invention. Udyavar’s classifier makes correlations between gene expression data and CD8+ T-cell localization rather than training directly on CD8+ T-cell localization patterns derived from the image analysis. Additionally, Guan fails to make up for the deficiencies of Yoo and Udyavar regarding claim 82. Guan does not teach a classifier which receives resulting image analysis data from a histology image and outputs a phenotype prediction.
Claim 83 is dependent upon claim 82, so it would be allowable if claim 82 were rewritten in independent form including all of the limitations of its base claim and any intervening claims. Claims 97 and 98 are objected to for the same reasons as claims 82 and 83, respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Szabo et al. (Tumor CD8+ T-Cell Infiltration Assessed by Gene Expression Profiling Alone or by Immunohistochemistry Plus Epithelial-Mesenchymal Transition Gene Expression in Urothelial Carcinoma in CheckMate 275. Poster Presented at the Society for Immunotherapy of Cancer (SITC) 34th Annual Meeting, November 6–10, 2019.) teaches classifying immune phenotypes based on parenchymal and stromal abundance of T-cells. Although this reference teaches the limitations of the independent claims, this reference names the same Applicant and Inventors of the claimed invention and was made publicly available within one year before the earliest priority date (provisional application August 31, 2020). Therefore, this reference is made of record but not used for any rejections.
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/Eric Shoemaker/
Patent Examiner
/XIAO LIU/Primary Examiner, Art Unit 2664
