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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1 (and by dependency claims 2-23) are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). In the present instance, claim 1 recites the broad recitation “three or more” [dyes], and the claim also recites “four or more, five or more, or six or more dyes” which is the narrower statement of the range/limitation. The claim(s) are considered indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims.
Claim 4 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). In the present instance, claim 4 recites the broad recitation less than approximately 50 micrometers, and the claim also recites less than 40 micrometers, less than 30 micrometers, less than 25 micrometers, or less than 20 micrometers which is the narrower statement of the range/limitation. The claim(s) are considered indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims.
Claim 5 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). In the present instance, claim 5 recites the broad recitation 1 micrometer and 25 micrometers, and the claim also recites 2 micrometers and 20 micrometers, or 3 micrometers and 15 micrometers which is the narrower statement of the range/limitation. The claim(s) are considered indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims.
Claim 19 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 19 recites calculating a phenotypic distance score for each candidate compound based on the dose-response curve for each of the one or more phenotypic characteristics, optionally wherein the phenotypic distance score is compared to an untreated control; and identifying candidate compounds having a phenotypic distance score above a threshold value for each of the one or more phenotypic characteristics. Examiner is interpreting this step as required, however the claim appears to possibly only be the first part of the claim, meaning the claim would be satisfied by only finding “calculating a phenotypic distance score for each candidate compound based on the dose-response curve for each of the one or more phenotypic characteristics” which is much more broad. Therefore the total content of the claim is ambiguous.
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 following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-5, 10-12, 14-18, 21, 22, and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Larsen et al. (US 20210172931 A1) in view of Clevers et al. (US 20250011722 A1).
Regarding claim 1, Larsen et al. disclose a method of phenotypic characterization of a three-dimensional (3D) target cell culture model, the method comprising: culturing the 3D target cell model in wells of a well plate for a first period of time (After the organoids are cultured, cells from the organoids can be plated into an assay plate (e.g. a 96-well assay plate, a 384-well assay plate, etc.), [0243], FIG. 44 shows an exemplary flow 1200 for conducting drug screens in accordance with systems and methods described herein. In some embodiments, the flow 1200 can include disassociating tumor organoids into single cells, plating the cells (e.g., in a well plate such as a 96-well plate and/or a 384-well plate), growing the cells into organoids over a predetermined time period (e.g., seventy-two hours), [0297]); staining the cultured 3D target cell model with three or more, four or more, five or more, or six or more dyes (fluorescent light microscopy techniques include those that use fluorescent dyes to visualize dead/apoptotic cells and/or total cells, [0205], “TOs may be stained using common vital dyes to measure cellular behaviors amenable for high content fluorescent confocal imaging analysis. In one example, TOs are stained with Hoechst 33342 (Fisher Scientific cat # H3570), IncuCyte® Caspase-3/7 Green Apoptosis Assay Reagent (Essen Biosciences cat #4440), and TO-PRO™-3 Iodide (642/661) (Fisher Scientific cat # T3605) in multi-well tissue culture plates (e.g. 24, 48, 96, 384, etc.)”, [0331]) [this lists three types of dyes]; imaging the stained 3D target cell model (fluorescent light microscopy techniques include those that use fluorescent dyes to visualize dead/apoptotic cells and/or total cells, [0205], live/dead assays readouts can be produced using brightfield and multiplexed fluorescence imaging, [0242], fluorescent imaging can include producing three channels of data for each cell, [0243]); and analyzing the images to quantify one or more phenotypic characteristics of the 3D target cell model (various features, such as cell morphology, growth characteristics, genomic alterations, and/or drug sensitivity, are evaluated, [0115], measuring the fitness of cells, e.g., using a cellular viability assay or cell death assay, in the one or more organoids following the exposure to the one or more amounts of the therapeutic agent, [0156], In particular embodiments, a tumor organoid profile includes a cell viability value, wherein the cell viability value is the percentage of viable cells in a particular tumor organoid. In certain embodiments, the cell viability value is determined by visual detection techniques including, for example, methods that use fluorescent light microscopy and/or compound light microscopy (i.e., brightfield microscopy) techniques. As disclosed herein, fluorescent light microscopy techniques include those that use fluorescent dyes to visualize dead/apoptotic cells and/or total cells, [0205], In some embodiments, the live/dead assays readouts can be produced using brightfield and multiplexed fluorescence imaging. Drug response can be measured via cell viability assays using live/dead fluorescent stains, [0242], The imaging 320 can include brightfield imaging the treated cells, as well as applying fluorescent stains to at least a portion of the cells and fluorescent imaging the cells. In some embodiments, the fluorescent imaging can include producing three channels of data for each cell. The three channels of data can include a blue/all nuclei channel, a green/apoptotic channel, and a red/pink/dead channel, [0243]) [phenotype is interpreted as: cell morphology, cellular viability, percentage of viable cells, dead cells, and apoptotic cells].
Larsen et al. do not disclose a first period of time.
Clevers et al. teach culturing the 3D target cell model in wells of a well plate for a first period of time (When organoids were co-transfected with the piggyBac-Hygromycin B resistance transposon system, drug selection was started when organoids of small size had formed, typically 7-12 days after electroporation, [0311], APOB.sup.−/− and MTTP.sup.−/− organoids were exposed to the drugs or vehicle in 24-well plates for 7 days with 2 medium changes. Wild type organoids were first made steatotic by pre-incubation with 500 μM FFAs (oleic acid and palmitic acid, 1:1 ratio) for 2 days, [0317]); staining the cultured 3D target cell model with dyes (fluorescently labeling endogenous PLIN2, the most abundantly expressed perilipin in liver, would enable to establish an image-based steatosis drug screening system, Counterstaining of the PLIN2 reporters with a lipid droplet dye demonstrated faithful tagging of lipid droplets (FIG. 8f) and confirmed that tagging of PLIN2 did not affect the steatosis phenotype of the organoids (FIG. 8g), [0341]); imaging the stained 3D target cell model (Pictures were taken over a 7-day time course using the EVOS FL Auto Imaging System. Images and fluorescence quantifications are representative of two independent experiments, [0317]); and analyzing the images to quantify one or more phenotypic characteristics of the 3D target cell model (Nile Red lipid stainings of APOB.sup.−/− organoids treated with different drugs for 7 days, [0118], Quantification of the fluorescent signal over a timeframe of 7 days visualized variable drug dynamics with time and demonstrated an identical classification of effective drugs, [0341], Next the orchestrated responses induced by the different drugs were focused on and found a number of DEGs to be conserved across all treatments (excluding DGAT2i) (FIG. 9g-h). In particular, induction of DUSP4 and DUSP5 was noted (with baseline expression near zero, but induced upon drug treatment) (FIG. 9i). DUSPs (dual-specificity phosphatases) regulate MAPK signaling pathway activity, including ERK, JNK, and p38 (Caunt and Keyse, 2013) (FIG. 9j). It was questioned whether interference with these pathways using specific small molecules would have an effect on the steatosis phenotypes of APOB.sup.−/− and MTTP.sup.−/− organoids. Treatment with ERKi and JNKi did not alter the steatosis phenotype, however treatment with p38i reduced the steatosis phenotype of both APOB.sup.−/− and MTTP.sup.−/− organoids (FIG. 9k). Lipid score analysis revealed a reduction (FIG. 9I). These MAPK inhibitors in FFA-loaded wild type organoids were also tested, [0345]) [phenotype interpreted as lipid staining, amount of fluorescent signal, steatosis phenotype].
Larsen et al. and Clevers et al. are in the same art of drug screening (Larsen et al., [0008]; Clevers et al., [0118]). The combination of Clevers et al. with Larsen et al. will enable using a distinct first and second time period. It would have been obvious at the time of filing to one of ordinary skill in the art to combine the time periods of Clevers et al. with the invention of Larsen et al. as this was known at the time of filing, the combination would have predictable results, and as Clevers et al. indicate this allows selecting drugs only when the organoids of a certain size have formed ([0311]) as it would be likely that without waiting for that period of time the cells would be fragile and would all die with the additional treatment, therefore waiting a sufficient amount of time for organelles to form will result in the cost savings of not using drugs on cells that are not viable.
Regarding claim 2, Larsen et al. and Clevers et al. disclose the method of claim 1. Larsen et al. further disclose the imaging comprises capturing using a digital imaging device, a first plurality of vertically spaced-apart images of the wells, each image having a different height along a Z axis, such that a volumetric image stack is generated with respect to the wells (Each channel (i.e. light microscopy, Hoechst nuclear stain, FITC, Cy5) is acquired through an objective lens, in one example a 10 x objective lens is used to take images at two sites per well with a stack of images in the Z plane ranging from 1-100 heights in the Z-plane with increments ranging from submicron to as high as 15 micron per Z-plane height. Z-stack images are projected to 2D and analyzed using image analysis software with parameters to identify TOs based on the pixel intensities in the nuclear stain channel (i.e. Hoechst 33342 channel) and the size of the object by measurements in 2D space of the object as well as number of nuclei, [0331], Images were acquired as 4×15 μm Z-stacks and the 2D projections were analyzed to assess cell viability, [0373]).
Regarding claim 3, Larsen et al. and Clevers et al. disclose the method of claim 2. Larsen et al. further indicate Z-coordinates of sequential images in the volumetric image stack differ by at least approximately 1 micrometer (1-100 heights in the Z-plane with increments ranging from submicron to as high as 15 micron per Z-plane height, [0331], 4×15 μm Z-stacks, [0373]) [at least one micrometer implies anything above this meets this limitation].
Regarding claim 4, Larsen et al. and Clevers et al. disclose the method of claim 3. Larsen et al. further indicate the respective Z-coordinates of sequential images in the volumetric image stack differ by less than approximately 50 micrometers, less than 40 micrometers, less than 30 micrometers, less than 25 micrometers, or less than 20 micrometers (1-100 heights in the Z-plane with increments ranging from submicron to as high as 15 micron per Z-plane height, [0331]) [15 microns is less than 20 micrometers].
Regarding claim 5, Larsen et al. and Clevers et al. disclose the method of claim 4. Larsen et al. further indicate the respective Z-coordinates of sequential images in the volumetric image stack differ by between 1 micrometer and 25 micrometers, 2 micrometers and 20 micrometers, or 3 micrometers and 15 micrometers (1-100 heights in the Z-plane with increments ranging from submicron to as high as 15 micron per Z-plane height, [0331], 4×15 μm Z-stacks, [0373]) [various ranges implied by submicron to 15 microns].
Regarding claim 10, Larsen et al. and Clevers et al. disclose the method of claim 1. As Larsen et al. further indicate the staining comprises three dyes (Larsen et al., [0331]) and Clevers et al. teach sequentially adding to the 3D target cell model (Clevers et al., The agents or compositions described herein and the at least one additional therapy can be administered simultaneously, in the same or in separate compositions, or sequentially, [0307]), together Larsen et al. and Clevers et al. teach sequentially adding one or more of the three or more dyes to the 3D target cell model.
Regarding claim 11, Larsen et al. and Clevers et al. disclose the method of claim 1. As Larsen et al. further indicate the staining comprises three dyes (Larsen et al., [0331]) and Clevers et al. teach simultaneously adding to the 3D target cell model (Clevers et al., The agents or compositions described herein and the at least one additional therapy can be administered simultaneously, in the same or in separate compositions, or sequentially, [0307]), together Larsen et al. and Clevers et al. teach the staining comprises simultaneously adding the three or more dyes to the 3D target cell model.
Regarding claim 12, Larsen et al. and Clevers et al. disclose the method of claim 1. Larsen et al. and Clevers et al. further indicate the one or more phenotypic characteristics are selected from the group consisting of 3D target cell model size; diameter; area; disintegration; density; compactness; texture; integrity; optical density; shape; width; cell viability; ATP level; nuclei count; nuclear area; fluorescence intensity; total cell count; live cell count; dead cell count; cell area; projected cell area; number of viable cells; and cell number positive for a selected biomarker, cellular component, or organelle (Larsen et al., various features, such as cell morphology, growth characteristics, genomic alterations, and/or drug sensitivity, are evaluated, [0115], measuring the fitness of cells, e.g., using a cellular viability assay or cell death assay, in the one or more organoids following the exposure to the one or more amounts of the therapeutic agent, [0156], In particular embodiments, a tumor organoid profile includes a cell viability value, wherein the cell viability value is the percentage of viable cells in a particular tumor organoid. In certain embodiments, the cell viability value is determined by visual detection techniques including, for example, methods that use fluorescent light microscopy and/or compound light microscopy (i.e., brightfield microscopy) techniques. As disclosed herein, fluorescent light microscopy techniques include those that use fluorescent dyes to visualize dead/apoptotic cells and/or total cells, [0205], In some embodiments, the live/dead assays readouts can be produced using brightfield and multiplexed fluorescence imaging. Drug response can be measured via cell viability assays using live/dead fluorescent stains, [0242], The imaging 320 can include brightfield imaging the treated cells, as well as applying fluorescent stains to at least a portion of the cells and fluorescent imaging the cells. In some embodiments, the fluorescent imaging can include producing three channels of data for each cell. The three channels of data can include a blue/all nuclei channel, a green/apoptotic channel, and a red/pink/dead channel, [0243]; Clevers et al., Nile Red lipid stainings of APOB.sup.−/− organoids treated with different drugs for 7 days, [0118], Quantification of the fluorescent signal over a timeframe of 7 days visualized variable drug dynamics with time and demonstrated an identical classification of effective drugs, [0341], Next the orchestrated responses induced by the different drugs were focused on and found a number of DEGs to be conserved across all treatments (excluding DGAT2i) (FIG. 9g-h). In particular, induction of DUSP4 and DUSP5 was noted (with baseline expression near zero, but induced upon drug treatment) (FIG. 9i). DUSPs (dual-specificity phosphatases) regulate MAPK signaling pathway activity, including ERK, JNK, and p38 (Caunt and Keyse, 2013) (FIG. 9j). It was questioned whether interference with these pathways using specific small molecules would have an effect on the steatosis phenotypes of APOB.sup.−/− and MTTP.sup.−/− organoids. Treatment with ERKi and JNKi did not alter the steatosis phenotype, however treatment with p38i reduced the steatosis phenotype of both APOB.sup.−/− and MTTP.sup.−/− organoids (FIG. 9k). Lipid score analysis revealed a reduction (FIG. 9I). These MAPK inhibitors in FFA-loaded wild type organoids were also tested, [0345]).
Regarding claim 14, Larsen et al. and Clevers et al. disclose the method of claim 1. Larsen et al. and Clevers et al. further indicate monitoring one or more phenotypic characteristics of the 3D target cell model at one or more, two or more, three or more, or four or more time points during the culturing, optionally wherein the monitoring comprises imaging using transmitted light (TL) (Larsen et al., At 1304, the process 1300 can receive an indication to analyze treated organoids at multiple time points. In some embodiments, the organoids can be plated (e.g., in a well plate such as a 96-well plate and/or a 384-well plate). In some embodiments, the organoids can be plated on multiple well plates. In some embodiments, the organoids can be plated on one or more petri dishes. In some embodiments, the organoids can be treated using a variety of different treatments, which can vary in drug type, drug concentration, and/or other parameters, [0300], In some embodiments, the multiple time points can represent a time after the organoids have been treated. For example, a twelve hour time point can be twelve hours after the time at which the organoids were treated. In some embodiments, the multiple time points can be spaced at regular intervals, [0301]; Clevers et al., Pictures were taken over a 7-day time course using the EVOS FL Auto Imaging System. Images and fluorescence quantifications are representative of two independent experiments, [0317], Quantification of the fluorescent signal over a timeframe of 7 days visualized variable drug dynamics with time and demonstrated an identical classification of effective drugs, [0341]).
Regarding claim 15, Larsen et al. and Clevers et al. disclose the method of claim 1. Larsen et al. and Clevers et al. further indicate fixing and optionally permeabilizing the cultured 3D target cell model (Larsen et al., Organoids were dissociated using TrypLE Express Enzyme (GIBCO) and dissociated cells were immediately lysed in 350 μL of buffer RLT from the Allprep DNA/RNA Micro Kit (Qiagen) and stored at −80° C, [0362]; Clevers et al., Organoids were fixed with 1.5% glutaraldehyde in 0.1 M cacodylate buffer at 4° C. for 24 hours, [0320], Organoids were fixed in 4% formaldehyde at RT for 30 min-1 hour. For immunofluorescence stainings, fixed organoids were first washed twice with PBS, and then simultaneously blocked and permeabilized using 5% BSA and 0.3% Triton-X in PBS at RT for 1 hour, [0321], Organoids from 1 well of a 12-well plate were harvested and washed in cold AdvDMEM+++. Organoid pellets were lysed in 1 ml TRIzol Reagent and subsequently snap-frozen in liquid nitrogen, [0323]).
Regarding claim 16, Larsen et al. and Clevers et al. disclose the method of claim 1. Clevers et al. further indicate the method further comprises measuring one or more secreted factors in 3D target cell model supernatants (Next the intracellular (pellet) and secreted (supernatant) lipid profiles of APOB.sup.−/− organoids was interrogated in comparison to wild type organoids using a lipidomic approach, [0331] To directly probe the origin and to assess changes in lipid profiles, both APOB.sup.−/− and wild type organoids were subjected to lipidomic analyses, this time interrogating both the intracellular lipid profiles (organoid pellets) and the secreted lipid profiles (supernatants) (FIG. 18a), [0377]).
Regarding claim 17, Larsen et al. and Clevers et al. disclose the method of claim 1. Larsen et al. and Clevers et al. further indicate treating the cultured 3D target cell model with one or more candidate compounds over a second period of time, optionally wherein the treating is prior to the staining (Larsen et al., In some embodiments, the multiple time points can represent a time after the organoids have been treated. For example, a twelve hour time point can be twelve hours after the time at which the organoids were treated. In some embodiments, the multiple time points can be spaced at regular intervals, [0301]; Clevers et al., Time-lapse fluorescent images of PLIN2::tdTomato; MTTP.sup.−/− organoids treated with different drugs over a 7 day window, [0119], APOB.sup.−/− and MTTP.sup.−/− organoids were exposed to the drugs or vehicle in 24-well plates for 7 days with 2 medium changes, [0317], This could be visualized in in real-time by live imaging the organoids during drug treatment (FIG. 8b), [0339], Quantification of the fluorescent signal over a timeframe of 7 days visualized variable drug dynamics with time and demonstrated an identical classification of effective drugs, [0341]).
Regarding claim 18, Larsen et al. and Clevers et al. disclose the method of claim 17. Larsen et al. further indicate treating comprises exposing the cultured 3D target cell model to different doses of the one or more candidate compounds to obtain a dose-response curve for each of the one or more phenotypic characteristics, optionally wherein the different doses include two or more, three or more, four or more, or five or more different doses (FIGS. 3A, 3B, 3C, and 3D illustrate dose response curves for the treatment of four patient-derived tumor organoid cell lines with olaparib, in accordance with some embodiments of the present disclosure,
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[0013], Example dose-response curve for staurosporine for both the cystic TO (CRC) and solid TO (gastric) lines for TO viability calculated from TO-PRO-3, Caspase-3/7, and live cells per TO, [0033], Compounds were grouped by their reported targets and are shown ranked by median inverse AUC values calculated from dose-response curves of RCA generated viability values for the CRC (D) and gastric (E) TOs. Dose-response curves for trametinib (D) and afatinib (E) highlighting the correlation between generated, TO-PRO-3 and Caspase-3/7 viabilities. F: Copy-number amplification plot for the Gastric TO exhibiting ERBB2 amplification (arrow). G: AUC ROC curves between fluorescent and generated viabilities to assess sensitivity and specificity of PARPi response to classify organoids as HRD positive or HRD negative as determined by genome-wide LOH proportion (Tempus HRD assay), [0034], The high number of measurements recorded per condition (e.g., dose) allows the use of more complex statistical methods that would otherwise be unable to be used with a low-throughput dose response assay. In some embodiments, the tumor organoid profiles obtained are adjusted for one or more confounding technical effect. Use of a linear model allows for inclusion of covariates to adjust for potential confounding technical effects including initial TO viability, differences in growth rates between TOs derived from different patients, and different cancer types, and leverages all of the TO data to gain better statistical power. In some embodiments, a linear model is applied to determine differences between patients, or between drugs, at equivalent therapeutic concentrations (or doses)”, [0204], In some embodiments, the tumor organoid profiles acquired are used to generate a dose-response curve (see, e.g., Example 2 and FIG. 2, below). In particular embodiments, a particular therapeutic dosage is assigned to a patient based on the dose-response curve, [0206], The mean viability for all organoids at a given drug concentration was used in dose-response curves to calculate AUC, [0374] The sensitivity and specificity in a dose-response series was evaluated, with dosing based on TO viability at 10 μM, [0467]).
Regarding claim 21, Larsen et al. and Clevers et al. disclose the method of claim 17. Larsen et al. and Clevers et al. further indicate the 3D target cell model is selected from the group consisting of spheroid, tumoroid, organoid, and PDX-derived organoids (Larsen et al., organoid, abstract, [0011], [0062]; Clevers et al., organoids, abstract, [0034], [0134]).
Regarding claim 22, Larsen et al. and Clevers et al. disclose the method of claim 21. Larsen et al. and Clevers et al. further indicate the 3D target cell model is derived from a patient tissue, tumor, biopsy sample, or a tumoroid fragment (Larsen et al. organoid cultures, e.g., patient-derived tumor organoid cultures, abstract, In some embodiments, the methods include obtaining a tumor biopsy from the cancer patient, and culturing one or more tumor organoids from one or more cells of the tumor biopsy. The methods then include exposing the one or more tumor organoids to one or more concentrations of the candidate cancer pharmaceutical agent, and measuring the fitness of cells in the one or more tumor organoids following the exposure to the one or more concentrations of the candidate cancer pharmaceutical agent. The methods then include determining whether the cancer patient is eligible for the clinical trial based on at least the measured fitness of the cells in the one or more tumor organoids, wherein reduced fitness of the cells in the one or more tumor organoids is indicative that the cancer patient is eligible for the clinical trial, [0063]; Clevers et al., Liver includes two types of epithelial cells hepatocytes and liver ductal cells. The organoids of the invention are derived from human hepatocyte cells. The hepatocytes may be primary hepatocytes. Primary hepatocytes are hepatocytes directly isolated from liver tissue. For example, hepatocytes obtained by biopsy, [0135]).
Regarding claim 24, Larsen et al. disclose a method for selecting a drug therapy for therapeutic treatment of a subject in need thereof (Provided herein is a high-throughput drug screening method and system more applicable for the unique characteristics of tumor organoids, [0008]), the method comprising: culturing a 3D target cell model derived from the subject in wells of a well plate over a first period of time (After the organoids are cultured, cells from the organoids can be plated into an assay plate (e.g. a 96-well assay plate, a 384-well assay plate, etc.), [0243], FIG. 44 shows an exemplary flow 1200 for conducting drug screens in accordance with systems and methods described herein. In some embodiments, the flow 1200 can include disassociating tumor organoids into single cells, plating the cells (e.g., in a well plate such as a 96-well plate and/or a 384-well plate), growing the cells into organoids over a predetermined time period (e.g., seventy-two hours), [0297]); treating the cultured 3D target cell model with one or more candidate drugs over a second period of time (The drug screening 316 can include plating the cells and treating the cells with a number of different drugs and/or concentrations. For example, a 384-well plate can include fourteen drugs at seven different concentrations. As another example, a 96-well plate can include six drugs at five different concentrations, [0243], conducting drug screens, treating the organoids with at least one therapeutic technique, and imaging the tumor organoids a predetermined amount of time (e.g., seventy-two hours) after the tumor organoids are treated, [0297], At 1304, the process 1300 can receive an indication to analyze treated organoids at multiple time points. In some embodiments, the organoids can be plated (e.g., in a well plate such as a 96-well plate and/or a 384-well plate). In some embodiments, the organoids can be plated on multiple well plates. In some embodiments, the organoids can be plated on one or more petri dishes. In some embodiments, the organoids can be treated using a variety of different treatments, which can vary in drug type, drug concentration, and/or other parameters, [0300], In some embodiments, the multiple time points can represent a time after the organoids have been treated. For example, a twelve hour time point can be twelve hours after the time at which the organoids were treated. In some embodiments, the multiple time points can be spaced at regular intervals, [0301]); staining the treated 3D target cell model with three or more dyes (fluorescent light microscopy techniques include those that use fluorescent dyes to visualize dead/apoptotic cells and/or total cells, [0205], “TOs may be stained using common vital dyes to measure cellular behaviors amenable for high content fluorescent confocal imaging analysis. In one example, TOs are stained with Hoechst 33342 (Fisher Scientific cat # H3570), IncuCyte® Caspase-3/7 Green Apoptosis Assay Reagent (Essen Biosciences cat #4440), and TO-PRO™-3 Iodide (642/661) (Fisher Scientific cat # T3605) in multi-well tissue culture plates (e.g. 24, 48, 96, 384, etc.)”, [0331]) [this lists three types of dyes]; imaging the stained 3D target cell model (fluorescent light microscopy techniques include those that use fluorescent dyes to visualize dead/apoptotic cells and/or total cells, [0205], live/dead assays readouts can be produced using brightfield and multiplexed fluorescence imaging, [0242], fluorescent imaging can include producing three channels of data for each cell, [0243]); and analyzing the images to quantify one or more phenotypic characteristics of the treated 3D target cell model (various features, such as cell morphology, growth characteristics, genomic alterations, and/or drug sensitivity, are evaluated, [0115], measuring the fitness of cells, e.g., using a cellular viability assay or cell death assay, in the one or more organoids following the exposure to the one or more amounts of the therapeutic agent, [0156], In particular embodiments, a tumor organoid profile includes a cell viability value, wherein the cell viability value is the percentage of viable cells in a particular tumor organoid. In certain embodiments, the cell viability value is determined by visual detection techniques including, for example, methods that use fluorescent light microscopy and/or compound light microscopy (i.e., brightfield microscopy) techniques. As disclosed herein, fluorescent light microscopy techniques include those that use fluorescent dyes to visualize dead/apoptotic cells and/or total cells, [0205], In some embodiments, the live/dead assays readouts can be produced using brightfield and multiplexed fluorescence imaging. Drug response can be measured via cell viability assays using live/dead fluorescent stains, [0242], The imaging 320 can include brightfield imaging the treated cells, as well as applying fluorescent stains to at least a portion of the cells and fluorescent imaging the cells. In some embodiments, the fluorescent imaging can include producing three channels of data for each cell. The three channels of data can include a blue/all nuclei channel, a green/apoptotic channel, and a red/pink/dead channel, [0243]) [phenotype is interpreted as: cell morphology, cellular viability, percentage of viable cells, dead cells, and apoptotic cells].
Larsen et al. do not disclose a first period of time.
Clevers et al. teach culturing a 3D target cell model derived from the subject in wells of a well plate over a first period of time (When organoids were co-transfected with the piggyBac-Hygromycin B resistance transposon system, drug selection was started when organoids of small size had formed, typically 7-12 days after electroporation, [0311], APOB.sup.−/− and MTTP.sup.−/− organoids were exposed to the drugs or vehicle in 24-well plates for 7 days with 2 medium changes. Wild type organoids were first made steatotic by pre-incubation with 500 μM FFAs (oleic acid and palmitic acid, 1:1 ratio) for 2 days, [0317]); treating the cultured 3D target cell model with one or more candidate drugs over a second period of time (“Then, FFA-loaded organoids were treated with the different drugs, still in the presence of FFA, for 7 days with 2 medium changes. All organoids were harvested for subsequent lipid staining and lipid scoring as described below. Drug effects were evaluated by observing the lipid droplet fluorescence characteristics within all organoids within the whole well. Quantitative analyses were performed in representative organoids (n≥3) per drug concentration per steatosis organoid model. Drug effects were validated in at least two independent experiments. For drug screening in PLIN2::tdTomato reporter organoids, the organoids were plated in 96-well black plates and treated with the indicated drugs”, [0317]); staining the treated 3D target cell model with dyes (fluorescently labeling endogenous PLIN2, the most abundantly expressed perilipin in liver, would enable to establish an image-based steatosis drug screening system, Counterstaining of the PLIN2 reporters with a lipid droplet dye demonstrated faithful tagging of lipid droplets (FIG. 8f) and confirmed that tagging of PLIN2 did not affect the steatosis phenotype of the organoids (FIG. 8g), [0341]); imaging the stained 3D target cell model (Pictures were taken over a 7-day time course using the EVOS FL Auto Imaging System. Images and fluorescence quantifications are representative of two independent experiments, [0317]); and analyzing the images to quantify one or more phenotypic characteristics of the treated 3D target cell model (Identifying genes that promote or reduce disease phenotypes may indicate possible targets for drug development. The simplest candidate drugs bind to and interfere with the proteins encoded by these genes, rather than affect the genes directly, [0239], Quantification of the fluorescent signal over a timeframe of 7 days visualized variable drug dynamics with time and demonstrated an identical classification of effective drugs, [0341], Next the orchestrated responses induced by the different drugs were focused on and found a number of DEGs to be conserved across all treatments (excluding DGAT2i) (FIG. 9g-h). In particular, induction of DUSP4 and DUSP5 was noted (with baseline expression near zero, but induced upon drug treatment) (FIG. 9i). DUSPs (dual-specificity phosphatases) regulate MAPK signaling pathway activity, including ERK, JNK, and p38 (Caunt and Keyse, 2013) (FIG. 9j). It was questioned whether interference with these pathways using specific small molecules would have an effect on the steatosis phenotypes of APOB.sup.−/− and MTTP.sup.−/− organoids. Treatment with ERKi and JNKi did not alter the steatosis phenotype, however treatment with p38i reduced the steatosis phenotype of both APOB.sup.−/− and MTTP.sup.−/− organoids (FIG. 9k). Lipid score analysis revealed a reduction (FIG. 9I). These MAPK inhibitors in FFA-loaded wild type organoids were also tested, [0345]) [phenotype interpreted as lipid staining, amount of fluorescent signal, steatosis phenotype].
Larsen et al. and Clevers et al. are in the same art of drug screening (Larsen et al., [0008]; Clevers et al., [0118]). The combination of Clevers et al. with Larsen et al. will enable using a distinct first and second time period. It would have been obvious at the time of filing to one of ordinary skill in the art to combine the time periods of Clevers et al. with the invention of Larsen et al. as this was known at the time of filing, the combination would have predictable results, and as Clevers et al. indicate this allows selecting drugs only when the organoids of a certain size have formed ([0311]) as it would be likely that without waiting for that period of time the cells would be fragile and would all die with the additional treatment, therefore waiting a sufficient amount of time for organelles to form will result in the cost savings of not using drugs on cells that are not viable.
Claim(s) 6-9 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Larsen et al. (US 20210172931 A1) and Clevers et al. (US 20250011722 A1) as applied to claim 1 above, further in view of Zhao et al. (US 20260002862 A1).
Regarding claim 6, Larsen et al. and Clevers et al. disclose the method of claim 1. Larsen et al. and Clevers et al. do not explicitly disclose the three or more dyes comprise at least three dyes each specific for a different biomarker, cellular component or organelle selected from the group consisting of nuclear DNA, lysosomes, RNA, endoplasmic reticulum (ER), nuclei, nucleoli, cytoplasmic RNA, actin, Golgi apparatus, plasma membrane, mitochondria, and cytoskeleton.
Zhao et al. teach three or more dyes comprise at least three dyes each specific for a different biomarker, cellular component or organelle selected from the group consisting of nuclear DNA, lysosomes, RNA, endoplasmic reticulum (ER), nuclei, nucleoli, cytoplasmic RNA, actin, Golgi apparatus, plasma membrane, mitochondria, and cytoskeleton (According to the embodiments of the present invention, the uniform fluorescent labeling protocol comprises uniformly labeling cell nucleus, nucleolus, endoplasmic reticulum, Golgi apparatus, cell membrane, and mitochondria of all cells treated with the candidate drugs with different fluorescent dyes, [0015], The uniform fluorescent labeling protocol comprises uniformly labeling cell nucleus, nucleolus, endoplasmic reticulum, Golgi apparatus, cell membrane, and mitochondria of all cells treated with the candidate drugs with different fluorescent dyes. It should be noted that the components and organelles labeled for cells in the uniform fluorescent labeling protocol are not limited to those listed above, and may also be other components and organelle types of cells, [0044], Step 1: At the cell incubation stage, it is required to add various drugs, thereby observing the phenotypic changes of cells under the action of different candidate drugs; Step 2: The candidate drug-treated cells that are obtained in step (1) are fluorescently labeled to prepare a cell suspension; wherein the fluorescent labeling is classified into two categories: one category is non-distinctive labeling, i.e., all cells are subjected to an uniform fluorescent labeling protocol, for example, uniformly labeling cell nucleus, nucleolus, endoplasmic reticulum, Golgi apparatus, cell membrane, and mitochondria of all the candidate drug-treated cells with different fluorescent dyes; and the other category is specific labeling, where different cells are subjected to specific fluorescent labeling based on possible phenotypic differences, for example, fluorescently labeling some expressed proteins of cells, [0061]-[0061]).
Larsen et al. and Clevers et al. and Zhao et al. are in the same art of drug screening (Larsen et al., [0008]; Clevers et al., [0118]; Zhao et al., abstract). The combination of Zhao et al. with Larsen et al. and Clevers et al. will enable using dyes each specific for a different biomarker, cellular component or organelle selected from the group consisting of nuclear DNA, lysosomes, RNA, endoplasmic reticulum (ER), nuclei, nucleoli, cytoplasmic RNA, actin, Golgi apparatus, plasma membrane, mitochondria, and cytoskeleton. It would have been obvious at the time of filing to one of ordinary skill in the art to combine the dyes described by Zhao et al. with the invention of Larsen et al. and Clevers et al. as this was known at the time of filing, the combination would have predictable results, and as Zhao et al. indicate “Since the above-mentioned reagents for molecular labeling have different fluorescence wavelengths, the labeled cell structures can be distinguished by different fluorescent channels. Profile information is extracted from each fluorescent image and label-free image, such as the size, shape, granularity, position, center of gravity, texture, and spatial relationship with adjacent cell structures of cells or cell structures, to form an original data pool. Whether a cell is infected is determined by detecting the presence or absence of coronavirus nucleoprotein signals, that is, whether a cell is infected is determined by whether the single cell has a fluorescent signal of a goat anti-mouse IgG H&L secondary antibody. Then, the optimal set of profiles is selected from the original data pool through AI data analysis and an evaluation algorithm is derived. After being processed by the evaluation algorithm, a combination of optimal profiles can most accurately determine whether the cell is infected” ([0066]) thereby allowing each structure to be effectively distinguished, which will improve the accuracy and specificity of the combination of inventions.
Regarding claim 7, Larsen et al. and Clevers et al. and Zhao et al. disclose the method of claim 6. Larsen et al. and Clevers et al. and Zhao et al. further indicate the three or more dyes are selected from the group consisting of a cell-permeant cell viability dye, a cell-impermeant dead cell nucleic acid stain, a bis-benzimide DNA stain, a E-cadherin stain, and a CD cell surface biomarker stain (Larsen et al., Fluorescent labeling (top row) was used to identify all cells (blue, Hoechst 33342), apoptotic cells (green, Caspase-3/7), and dead cells (red, TO-PRO-3)., [0033], Fluorescent labeling (top row) was used to identify all cells (blue, Hoechst 33342), apoptotic cells (green, Caspase-3/7), and dead cells (red, TO-PRO-3), [0208]; Clevers et al., Hygromycin B Gold (50 μg/μl) was kept until selection was visually complete (i.e. clear distinguishment between alive and dead organoids, typically 7-14 days), [0311]; Zhao et al., “The staining reagents in use are Hoechst 33342 for DNA staining (an excitation wavelength of 377 nm, an emission wavelength of 447 nm), a wheat germ agglutinin-Alexa Fluor 555 conjugate for the staining of Golgi apparatus and cell membrane (an excitation wavelength of 562 nm, an emission wavelength of 624 nm), a phalloidin-Alexa Fluor 568 conjugate for the staining of fibrous actin (an excitation wavelength of 578 nm, an emission wavelength of 603 nm), a SYTO14 green fluorescent dye for the staining of nucleolar and cytoplasmic RNAs (an excitation wavelength of 531 nm, an emission wavelength of 593 nm), a concanavalin A-Alexa Fluor 488 conjugate for the staining of endoplasmic reticulum (an excitation wavelength of 482 nm, an emission wavelength of 536 nm), and coronavirus pan monoclonal antibody FIPV3-70 and goat anti-mouse IgG H&L secondary antibody (an excitation wavelength of 628 nm, an emission wavelength of 692 nm) for coupling coronavirus nucleoprotein”, [0065]).
Regarding claim 8, Larsen et al. and Clevers et al. and Zhao et al. disclose the method of claim 6. Larsen et al. and Clevers et al. and Zhao et al. further indicate the three or more dyes are selected from the group consisting of fluorescent dyes, luminescent dyes, and quantum dots, optionally wherein the three or more dyes comprise a dye-antibody conjugate, wherein the antibody is capable of specific binding to the selected biomarker, cellular component, or organelle (Larsen et al., Fluorescent labeling (top row) was used to identify all cells (blue, Hoechst 33342), apoptotic cells (green, Caspase-3/7), and dead cells (red, TO-PRO-3), [0033] fluorescent light microscopy techniques include those that use fluorescent dyes to visualize dead/apoptotic cells and/or total cells, [0205], In exemplary embodiments, the detection agents include fluorescent markers that can be visualized by fluorescent confocal imaging analysis. In particular embodiments, the markers include two or more markers for dead/apoptotic cells. Markers and assays useful for assessing dead apoptotic cells include, but are not limited to, IncuCyte® Caspase-3/7 Green Apoptosis Assay Reagent (Essen Biosciences cat #4440), and TO-PRO™-3 Iodide (642/661) (Fisher Scientific cat # T3605) and Annexin V assay (Abcam, ab14085), [0207], all cells per organoid are measured by Hoechst 33342 staining, apoptotic cells per organoid are measured by Caspase 3/7 staining and dead/dying cells may are by TO-PRO-3 staining. Utilizing fluorescent markers for all cells and two markers for dead/apoptotic cells permits analysis of TOs at the single cell level, [0208]; Clevers et al., visual selection methods (for example based on visualisation of cells that have also been modified to include a reporter gene such as a fluorescent reporter gene), [0182] antibody based techniques, [0260], Upon transfection, the sgRNA targeting the donor plasmid linearizes the donor plasmid to facilitate NHEJ-mediated in-frame gene knock-in of the fluorescent tag into the PLIN2 C-terminus. Upon outgrowth of transfected organoids, fluorescent organoids became apparent. A bulk PLIN2-tagged fluorescence-pure culture was established by sorting single fluorescence-positive cells by FACS, [0312], fluorescent lipid dye, [0373]; Zhao et al.. The present invention can fluorescently label cell components and organelles (e.g., cell membrane, cell nucleus, endoplasmic reticulum, Golgi apparatus, nucleolus, actin, mitochondria), thereby realizing the identification of cell components and organelles. Specifically, the label-free images obtained by non-distinctive labeling comprise a bright field, a dark field and a scattered field, and can measure the overall morphology, size and granularity of cells; through specific labeling, such as fluorescent labeling of certain cell components or organelles, their fluorescent images are available, and the corresponding image information are further available, [0017], The uniform fluorescent labeling protocol comprises uniformly labeling cell nucleus, nucleolus, endoplasmic reticulum, Golgi apparatus, cell membrane, and mitochondria of all cells treated with the candidate drugs with different fluorescent dyes. It should be noted that the components and organelles labeled for cells in the uniform fluorescent labeling protocol are not limited to those listed above, and may also be other components and organelle types of cells., [0044], one category is non-distinctive labeling, i.e., all cells are subjected to an uniform fluorescent labeling protocol, for example, uniformly labeling cell nucleus, nucleolus, endoplasmic reticulum, Golgi apparatus, cell membrane, and mitochondria of all the candidate drug-treated cells with different fluorescent dyes; and the other category is specific labeling, where different cells are subjected to specific fluorescent labeling based on possible phenotypic differences, for example, fluorescently labeling some expressed proteins of cells, [0061], “The staining reagents in use are Hoechst 33342 for DNA staining (an excitation wavelength of 377 nm, an emission wavelength of 447 nm), a wheat germ agglutinin-Alexa Fluor 555 conjugate for the staining of Golgi apparatus and cell membrane (an excitation wavelength of 562 nm, an emission wavelength of 624 nm), a phalloidin-Alexa Fluor 568 conjugate for the staining of fibrous actin (an excitation wavelength of 578 nm, an emission wavelength of 603 nm), a SYTO14 green fluorescent dye for the staining of nucleolar and cytoplasmic RNAs (an excitation wavelength of 531 nm, an emission wavelength of 593 nm), a concanavalin A-Alexa Fluor 488 conjugate for the staining of endoplasmic reticulum (an excitation wavelength of 482 nm, an emission wavelength of 536 nm), and coronavirus pan monoclonal antibody FIPV3-70 and goat anti-mouse IgG H&L secondary antibody (an excitation wavelength of 628 nm, an emission wavelength of 692 nm) for coupling coronavirus nucleoprotein. This set of reagents is used to determine whether MRC-5 human lung fibroblasts are infected by human coronavirus CoV-229E. If infected, the fluorescence transmitted by the secondary antibody will be observed. If not infected, there will be no fluorescence signal of the secondary antibody”, [0065]).
Regarding claim 9, Larsen et al. and Clevers et al. and Zhao et al. disclose the method of claim 8. Zhao et al. further indicate imaging comprises illuminating the stained 3D target cell model with a multiplicity of lasers at different wavelengths suitable for excitation of the fluorescent dyes, optionally wherein images are acquired at different emission wavelengths for each of the fluorescent dyes, and optionally wherein each of the fluorescent dyes are specific for a different phenotypic characteristic (Drug screening for against viral infections in the lungs is studied. The cell model adopts MRC-5 human lung fibroblasts, the virus to be studied is human coronavirus CoV-229E, and the candidate drugs include those coded as Drug 1, Drug 2, . . . , and Drug 100. The staining reagents in use are Hoechst 33342 for DNA staining (an excitation wavelength of 377 nm, an emission wavelength of 447 nm), a wheat germ agglutinin-Alexa Fluor 555 conjugate for the staining of Golgi apparatus and cell membrane (an excitation wavelength of 562 nm, an emission wavelength of 624 nm), a phalloidin-Alexa Fluor 568 conjugate for the staining of fibrous actin (an excitation wavelength of 578 nm, an emission wavelength of 603 nm), a SYTO14 green fluorescent dye for the staining of nucleolar and cytoplasmic RNAs (an excitation wavelength of 531 nm, an emission wavelength of 593 nm), a concanavalin A-Alexa Fluor 488 conjugate for the staining of endoplasmic reticulum (an excitation wavelength of 482 nm, an emission wavelength of 536 nm), and coronavirus pan monoclonal antibody FIPV3-70 and goat anti-mouse IgG H&L secondary antibody (an excitation wavelength of 628 nm, an emission wavelength of 692 nm) for coupling coronavirus nucleoprotein. This set of reagents is used to determine whether MRC-5 human lung fibroblasts are infected by human coronavirus CoV-229E. If infected, the fluorescence transmitted by the secondary antibody will be observed. If not infected, there will be no fluorescence signal of the secondary antibody, [0065], The first step is shown in FIG. 3. First, healthy MRC-5 human lung fibroblasts are infected with human coronavirus CoV-229E, and then different parts of MRC-5 human lung fibroblasts are stained with the above-mentioned Hoechst 33342, the SYTO14 green fluorescent dye, the wheat germ agglutinin-Alexa Fluor 555 conjugate, the phalloidin-Alexa Fluor 568 conjugate, and the concanavalin A-Alexa Fluor 488 conjugate. Then, the coronavirus pan monoclonal antibody FIPV3-70 and goat anti-mouse IgG H&L secondary antibody are added. If MRC-5 human lung fibroblasts are infected with Cov-229E, the coronavirus nucleoprotein will be expressed in the cells and can be labeled. If they are not infected, they will not be labeled. Next, the cells are prepared into a single-cell suspension, and the fluorescent image data and label-free image data of 100,000 cells are quickly collected by imaging flow cytometry. Since the above-mentioned reagents for molecular labeling have different fluorescence wavelengths, the labeled cell structures can be distinguished by different fluorescent channels. Profile information is extracted from each fluorescent image and label-free image, such as the size, shape, granularity, position, center of gravity, texture, and spatial relationship with adjacent cell structures of cells or cell structures, to form an original data pool. Whether a cell is infected is determined by detecting the presence or absence of coronavirus nucleoprotein signals, that is, whether a cell is infected is determined by whether the single cell has a fluorescent signal of a goat anti-mouse IgG H&L secondary antibody. Then, the optimal set of profiles is selected from the original data pool through AI data analysis and an evaluation algorithm is derived. After being processed by the evaluation algorithm, a combination of optimal profiles can most accurately determine whether the cell is infected, [0066]).
Regarding claim 13, Larsen et al. and Clevers et al. disclose the method of claim 12. Larsen et al. and Clevers et al. do not explicitly disclose the biomarker, cellular component, or organelle is selected from the group consisting of nuclear DNA, lysosomes, RNA, DNA, endoplasmic reticulum (ER), nuclei, nucleoli, cytoplasmic RNA, actin, Golgi apparatus, plasma membrane, mitochondria, and cytoskeleton.
Zhao et al. teach the biomarker, cellular component, or organelle is selected from the group consisting of nuclear DNA, lysosomes, RNA, DNA, endoplasmic reticulum (ER), nuclei, nucleoli, cytoplasmic RNA, actin, Golgi apparatus, plasma membrane, mitochondria, and cytoskeleton (According to the embodiments of the present invention, the uniform fluorescent labeling protocol comprises uniformly labeling cell nucleus, nucleolus, endoplasmic reticulum, Golgi apparatus, cell membrane, and mitochondria of all cells treated with the candidate drugs with different fluorescent dyes, [0015], The uniform fluorescent labeling protocol comprises uniformly labeling cell nucleus, nucleolus, endoplasmic reticulum, Golgi apparatus, cell membrane, and mitochondria of all cells treated with the candidate drugs with different fluorescent dyes. It should be noted that the components and organelles labeled for cells in the uniform fluorescent labeling protocol are not limited to those listed above, and may also be other components and organelle types of cells, [0044], Step 1: At the cell incubation stage, it is required to add various drugs, thereby observing the phenotypic changes of cells under the action of different candidate drugs; Step 2: The candidate drug-treated cells that are obtained in step (1) are fluorescently labeled to prepare a cell suspension; wherein the fluorescent labeling is classified into two categories: one category is non-distinctive labeling, i.e., all cells are subjected to an uniform fluorescent labeling protocol, for example, uniformly labeling cell nucleus, nucleolus, endoplasmic reticulum, Golgi apparatus, cell membrane, and mitochondria of all the candidate drug-treated cells with different fluorescent dyes; and the other category is specific labeling, where different cells are subjected to specific fluorescent labeling based on possible phenotypic differences, for example, fluorescently labeling some expressed proteins of cells, [0061]-[0061]).
Larsen et al. and Clevers et al. and Zhao et al. are in the same art of drug screening (Larsen et al., [0008]; Clevers et al., [0118]; Zhao et al., abstract). The combination of Zhao et al. with Larsen et al. and Clevers et al. will enable using dyes each specific for a different biomarker, cellular component or organelle selected from the group consisting of nuclear DNA, lysosomes, RNA, endoplasmic reticulum (ER), nuclei, nucleoli, cytoplasmic RNA, actin, Golgi apparatus, plasma membrane, mitochondria, and cytoskeleton. It would have been obvious at the time of filing to one of ordinary skill in the art to combine the dyes described by Zhao et al. with the invention of Larsen et al. and Clevers et al. as this was known at the time of filing, the combination would have predictable results, and as Zhao et al. indicate “Since the above-mentioned reagents for molecular labeling have different fluorescence wavelengths, the labeled cell structures can be distinguished by different fluorescent channels. Profile information is extracted from each fluorescent image and label-free image, such as the size, shape, granularity, position, center of gravity, texture, and spatial relationship with adjacent cell structures of cells or cell structures, to form an original data pool. Whether a cell is infected is determined by detecting the presence or absence of coronavirus nucleoprotein signals, that is, whether a cell is infected is determined by whether the single cell has a fluorescent signal of a goat anti-mouse IgG H&L secondary antibody. Then, the optimal set of profiles is selected from the original data pool through AI data analysis and an evaluation algorithm is derived. After being processed by the evaluation algorithm, a combination of optimal profiles can most accurately determine whether the cell is infected” ([0066]) thereby allowing each structure to be effectively distinguished, which will improve the accuracy and specificity of the combination of inventions.
Claim(s) 19, 20, and 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Larsen et al. (US 20210172931 A1) and Clevers et al. (US 20250011722 A1) as applied to claim 18 above, further in view of Jabs et al. (“Screening drug effects in patient‐derived cancer cells links organoid responses to genome alterations”).
Regarding claim 19, Larsen et al. and Clevers et al. disclose the method of claim 18. Larsen et al. and Clevers et al. do not explicitly disclose the analyzing comprises calculating a phenotypic distance score for each candidate compound based on the dose-response curve for each of the one or more phenotypic characteristics, optionally wherein the phenotypic distance score is compared to an untreated control; and identifying candidate compounds having a phenotypic distance score above a threshold value for each of the one or more phenotypic characteristics.
Jabs et al. teach calculating a phenotypic distance score for each candidate compound based on the dose-response curve for each of the one or more phenotypic characteristics, optionally wherein the phenotypic distance score is compared to an untreated control; and identifying candidate compounds having a phenotypic distance score above a threshold value for each of the one or more phenotypic characteristics (“Drug response curve fitting to determine the LD50 was only performed if there was a significant difference between cell death in drug‐treated and untreated samples. Therefore, an analysis of variance (ANOVA) was performed and LD50 was only calculated for drugs with P‐values < 0.0005”, p12) [cell death = phenotype, “significant difference”/P< 0.0005 = threshold].
Larsen et al. and Clevers et al. and Jabs et al. are in the same art of drug screening (Larsen et al., [0008]; Clevers et al., [0118]; Jabs et al., abstract). The combination of Jabs et al. with Larsen et al. and Clevers et al. will enable calculating a phenotypic distance score for each candidate compound based on the dose-response curve for each of the one or more phenotypic characteristics, optionally wherein the phenotypic distance score is compared to an untreated control. It would have been obvious at the time of filing to one of ordinary skill in the art to combine the distance score described by Jabs et al. with the invention of Larsen et al. and Clevers et al. as this was known at the time of filing, the combination would have predictable results, and as by only needing to further process a subset of results, this will improve the efficiency of the invention.
Regarding claim 20, Larsen et al. and Clevers et al. and Jabs et al. disclose the method of claim 19. Jabs et al. further teach the analyzing further comprises clustering the identified candidate compounds based on the phenotypic distance score for one or more phenotypic characteristics to create a phenotypic profile for the 3D target cell model (Having compared drug effects generally and separately, we inspected differences and similarities of patient cell responses in 2D and 3D culture by hierarchical clustering. Interestingly, drug response profiles tended to cluster based on the patients as well as the culture format (Fig 4A), indicating that culture type can influence patient cell responses to the same extent as intrinsic tumour heterogeneity. Most 2D patient profiles clustered together homogenously, with the exception of OC12 and OC18 which showed comparable response profiles in 2D and 3D. In total, we found 2D drug profiles in four subclusters while 3D drug profiles occurred in eight subclusters, demonstrating once more that drug profiles appear more diverse in organoids, p6).
Regarding claim 25, Larsen et al. and Clevers et al. disclose the method of claim 24. Larsen et al. and Clevers et al. do not explicitly disclose calculating a phenotypic distance score for each candidate drug for the one or more phenotypic characteristics compared to an untreated control; and selecting one or more of the candidate drugs having a phenotypic distance score above a threshold value for therapeutic treatment of the subject.
Jabs et al. teach calculating a phenotypic distance score for each candidate drug for the one or more phenotypic characteristics compared to an untreated control; and selecting one or more of the candidate drugs having a phenotypic distance score above a threshold value for therapeutic treatment of the subject (“Drug response curve fitting to determine the LD50 was only performed if there was a significant difference between cell death in drug‐treated and untreated samples. Therefore, an analysis of variance (ANOVA) was performed and LD50 was only calculated for drugs with P‐values < 0.0005”, p12) [cell death = phenotype, “significant difference”/P< 0.0005 = threshold]
Larsen et al. and Clevers et al. and Jabs et al. are in the same art of drug screening (Larsen et al., [0008]; Clevers et al., [0118]; Jabs et al., abstract). The combination of Jabs et al. with Larsen et al. and Clevers et al. will enable calculating a phenotypic distance score for each candidate compound based on the dose-response curve for each of the one or more phenotypic characteristics, optionally wherein the phenotypic distance score is compared to an untreated control. It would have been obvious at the time of filing to one of ordinary skill in the art to combine the distance score described by Jabs et al. with the invention of Larsen et al. and Clevers et al. as this was known at the time of filing, the combination would have predictable results, and as by only needing to further process a subset of results, this will improve the efficiency of the invention.
Claim(s) 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Larsen et al. (US 20210172931 A1) and Clevers et al. (US 20250011722 A1) as applied to claim 22 above, further in view of Satchi-Fainaro et al. (US 20190367884 A1).
Regarding claim 23, Larsen et al. and Clevers et al. disclose the method of claim 22. Larsen et al. and Clevers et al. do not explicitly disclose obtaining isolated cells from a primary tumor of a patient; cultivating the isolated cells to obtain two-dimensional (2D) cultivated cancer cells or passaging into murine models for expansion to provide xenograft cancer cells; and forming the 3D target cell model from the 2D or xenograft cancer cells.
Satchi-Fainaro et al. teach obtaining isolated cells from a primary tumor of a patient; cultivating the isolated cells to obtain two-dimensional (2D) cultivated cancer cells or passaging into murine models for expansion to provide xenograft cancer cells; and forming the 3D target cell model from the 2D or xenograft cancer cells (The present invention, in some embodiments thereof, relates to tumor modeling and, more particularly, but not exclusively, to three-dimensional tumor models featuring structural and functional properties in high match of a respective tumor in a subject, to methods of manufacturing same and to uses thereof in, for example, research, surgery simulation and personalized therapy, [0001], FIG. 16 presents a schematic illustration of a drug screening array with a 3D-printed tumor model according to some embodiments of the present invention. FIGS. 17A-B present comparative plots of primary osteogenic sarcoma cells: Saos-2-Dormant (D) and Saos-2-E (fast-growing) proliferation in a 3D tumor model (FIG. 17A) and in 2D model (FIG. 17B). Dormant and aggressive osteosarcoma cells at 10.sup.6 cells/ml were seeded in fibrin hydrogels (gelatin 15% w/v, Th 1 U/ml) for 14 days and measured with PrestoBlue (Thermo Fisher Scientific), [0114], Such 3D tumor models may find various uses in drug screening and personalized therapy, [0126], A 3D tumor model as described herein is populated with living cells, [0289], Achieving vascularization of the desired 3D tumor model, for example, in order to test different drugs on it, is considered a major challenge in bioprinting. Several 3D printers are capable to build tiny, hierarchical networks of blood vessels to supply blood, [0292], According to an aspect of some embodiments of the present invention there is provided a method of screening for an anti-cancer treatment regimen, the method comprising: subjecting a 3D model or system as described herein (with or without perfusion as described herein) of a tumor as described herein to the anti-cancer treatment regimen; and determining a presence of an anti-cancer effect (e.g., inhibition of tumor growth, killing of cancer cells, inducing apoptosis of cancer cells, anti-angiogenic effect) of the anti-cancer treatment regimen on the tumor, [0379], According to an aspect of some embodiments of the present invention there is provided a method of characterizing a tumor, the method comprising: providing the 3D model of the tumor as described herein (e.g., using a bioprinting method as described herein); isolating cells of the tumor model; and in vitro or in vivo culturing the cells. The cultured cells can thereafter be subjected to a variety of methodologies for characterizing the tumor, [0409]-[0410], Since every patient's tumor is unique, and patients with the same type of cancer will often respond differently to the same treatment, the main advantage of 3D-printed tumors for ex vivo simulation is the rapid screening of the patient's tumor-properties and its responsiveness to different drugs compared to the current available methods. It is believed that creating the 3D-printed tumor model with cells from a biopsy of the patient, constructed according to the patient's μCT or μMRI gives more reliable results in shorter time, stating which treatment demonstrated the best results for the specific patient's tumor, [0474]).
Larsen et al. and Clevers et al. and Satchi-Fainaro et al. are in the same art of drug screening (Larsen et al., [0008]; Clevers et al., [0118]; Satchi-Fainaro et al., [0002], [0292]). The combination of Satchi-Fainaro et al. with Larsen et al. and Clevers et al. will enable cultivating the isolated cells to obtain two-dimensional (2D) cultivated cancer cells or passaging into murine models for expansion to provide xenograft cancer cells; and forming the 3D target cell model from the 2D or xenograft cancer cells. It would have been obvious at the time of filing to one of ordinary skill in the art to combine the formation described by Satchi-Fainaro et al. with the invention of Larsen et al. and Clevers et al. as this was known at the time of filing, the combination would have predictable results, and as Satchi-Fainaro et al. indicate “Since every patient's tumor is unique, and patients with the same type of cancer will often respond differently to the same treatment, the main advantage of 3D-printed tumors for ex vivo simulation is the rapid screening of the patient's tumor-properties and its responsiveness to different drugs compared to the current available methods. It is believed that creating the 3D-printed tumor model with cells from a biopsy of the patient, constructed according to the patient's μCT or μMRI gives more reliable results in shorter time, stating which treatment demonstrated the best results for the specific patient's tumor” ([0474]) demonstrating a medical efficacy and time improvement to the combination of inventions.
Conclusion
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/MICHELLE M ENTEZARI HAUSMANN/Primary Examiner, Art Unit 2671