DETAILED ACTION
Response to Amendment
Claims 1-26 are pending. Claims 1-25 are amended directly or by dependency on an amended claim. Claim 26 is new.
Response to Arguments
Applicant’s arguments, see page 7, filed 29 April, 2026, with respect to the 35 USC 112 b/2nd rejections of claims 1, 4, 5, and 19 along with accompanying amendments received on the same date, have been fully considered and are persuasive. The 35 USC 112 b/2nd rejections of claims 1, 4, 5, and 19 have been withdrawn.
Applicant's arguments with respect to the 35 USC 103 rejections of claims 1-25 have been fully considered but they are not persuasive.
Applicant appears to be arguing the staining/dyes are specific in Zhao to single cell imaging and would be incompatible with the organoid imaging of Larsen. However many references exist which use a dye to image for each of these components in organoids, for instance:
nuclear DNA - US 20250241906 A1 - Experiment 1A—Determine overall chondrogenic capacity of identified natural products. Human chondrocytes cultured as 3D cartilage spheroids in non-adherent 96-well plates are treated with the candidate drugs and type II collagen promoter-driven Gaussia luciferase are assessed from the media over time as previously shown (FIGS. 2B and 3A) [0171] To assess DNA, samples of the digests (20 μl) were combined with buffered Hoechst dye (#33258, 667 ng/ml, phosphate buffer pH 8, 100 μl) and fluorescence measured at an excitation of 365 nm and emission of 460 nm [0323]
actin, nuclei, nuclear DNA - US 20250207102 A1: FIG. 1—(A) A schematic of a method for co-culturing genotoxic bacteria and organoids consisting of three phases: the first phase (no antibiotic), the second phase (low antibiotic concentration) and the third phase (high antibiotic concentration); (B) Immunofluorescence of organoid fragments after 4 h of co-culture with pks E. coli MOI 100 in the first phase; stained using DAPI (blue, which stains nuclei), Phalloidin (red, which stains actin filaments) and bacteria (green). The DAPI staining shows the presence of the nuclei in the organoids. The phalloidin dye stains the apical side of the organoid fragments. FIG. 1B shows that the apical surface of the organoid fragments is in contact with the bacteria strained in green. (C) Distribution of the organoid single cells derived from the genotoxic bacteria and organoid co-culture as a function of yH2AX levels and at different time points, as obtained by flow cytometry; (D) Representative image of organoids at the end of the recovery period. MOI 0=[Bact]1, MOI 10=[Bact]2, MOI 50=[Bact]3, MOI 100=[Bact]4. [0009] Organoids after the recovery period in the absence of bacteria (MOI 0), in the presence of colibactin producing bacteria (WT MOI 300) and in the presence of colibactin-deficient (ΔClbP MOI 300), stained using DAPI (recognizes cell nuclei, blue) and a yH2AX stain (top row, red) and yH2AX stain only (bottom row, red) and imaged using immunofluorescence (IF). The yH2AX stain shows where the DNA damage occurred in the organoids. [0011]
During any phase, particularly in the second or third phase, the organoid fragments can naturally form one or more organoids. The organoids or organoid fragments produced by these methods may be useful as a screening platform for identifying new drugs or testing drug candidates, or as a model for identifying new biomarkers, particularly in the context of cancer. In addition, the methods of co-culturing bacteria and organoids can be used to test the effect of a candidate compound.[0012]
Lysosome: US 20220378924 A1: The lysosome was stained by using LysoTracker® Green, which is a green-fluorescent dye for labeling and tracking acidic organelles in live cells. With color images (not shown), the CDC appears red, the lysosome appears green, and the nucleus appears blue, due to fluorescence, [0041]
FIG. 18 is an image comparing the Z-stack confocal microscopic imaging of KB tumor spheroids treated with an exemplary folate-receptor (FR)-targeting NDC (NDC D, prepared according to Example 3 using the exatecan-linker conjugate precursor 202), a payload-free FR-targeting nanoparticle (FA-C'Dot), a FR-targeting ADC, or the corresponding payload-free FR-targeting antibody, at 37° C. for 4 hours, followed by washing. Scale bar: 200 um.[0042]
RNA: US 20250011722 A1: FIG. 1 shows generation of CRISPR-engineered human steatosis organoids. (a) Schematic representation of the roles of ApoB (APOB) and MTP (MTTP) in the generation of VLDL particles within the hepatocyte; (b) Brightfield images of wild type and APOB.sup.−/− human hepatocyte organoids; (c) Nile Red lipid staining of wild type and APOB.sup.−/− organoids, cell boundaries are marked by phalloidin staining; (d) Quantifications of the number of lipid droplets and the organoid area covered by the lipid droplets in wild type and APOB.sup.−/− organoids. Dots represent individual organoids and the different colors indicate different lines. Analyses were performed in 3 lines from 3 donors; (e) Transmission electron microscopy of wild type and APOB.sup.−/− organoids. [0112]
FIG. 7 shows anti-NAFLD drug screening in genetic and diet-induced human steatosis organoid models, [0118]
(i) Expression levels of DUSP4 and DUSP5 upon different drug treatments. Normalized transcript levels from bulk RNA sequencing are plotted; (j) Schematic representation of DUSP proteins acting on different MAPK signaling pathways; (k) Nile Red lipid staining of APOB.sup.−/− organoids treated with different MAPK inhibitors; (l) Lipid score analysis of MAPK inhibitor effects in the different steatosis organoid models, [0120]
endoplasmic reticulum (ER): US 20200283735 A1: FIG. 28 is a photograph showing the results of analyzing the microstructure of the mouse ES cell-derived heart organoid of the present invention and a mouse embryonic heart using a transmission electron microscope. In the figure, “HO day11” and the photograph below it together indicate the results of analyzing a heart organoid (day 11 of culture), and “E10.5 heart” and “E11.5 heart” indicate the results of analyzing a mouse embryonic heart (embryonic day 10.5) and a mouse embryonic heart (embryonic day 11.5), respectively. In addition, in these photographs of embryonic hearts, the blue inset photograph at each lower left corner is a photograph showing the result of toluidine blue staining of a section of each heart for electron microscopy and photographing the whole. In each electron micrograph, “Z” indicates a sarcomere structure including the Z line, “ID” indicates an intercalated disc that is a cardiomyocyte-specific structure, “Gly” indicates glycogen, “RER” indicates rough endoplasmic reticulum, “M” indicates mitochondria, “D” indicates desmosome, and “LD” indicates lipid droplet.[0064]
As indicated above, the heart organoid of the present invention has a high degree of maturity. For this reason, it can be an effective material for cardiotoxicity tests and the like for evaluating drug safety. [0228]
cytoplasmic RNA US 20230279329 A1: -Associated Viruses (AAVs) across 3D biofabricated tumoroids glioblastoma spheroids (FIG. 4). These AAVs carried an RNA package which coded for the production of Green Fluorescent Protein (GFP) in the cytoplasm. After 6 days of perfusion a fixation protocol was successfully performed within the imaging perfusion plate and the spheroids were removed using the micromanipulator and stained. Cell nuclei were stained DAPI with intercellular junctions stained TRITC (FIG. 5). [0105]
Golgi apparatus US 20210372994 A1: wherein the test molecule is biologically active against the disease if the probability score of the first organoid is greater than a cutoff probability score of non-disease or lower than a cutoff probability score of disease, abstract
Prior to imaging organoids in the methods of the disclosure, organoids can be stained for one or more markers such as organelle markers, cell differentiation markers, cell compartment markers, or combinations thereof. The one or more markers preferably includes one or more markers that highlight the gross morphology of the organoid and/or a specific population of cells contained in the organoid. Exemplary staining reagents include nuclear stains (e.g., DAPI and Hoechst nuclear stains), BrdU, calcein green, membrane dyes such as DiI, DiO, DiD, and DiR, Golgi staining reagents such as fluorescently labeled antibodies to GM130, ER staining reagents such as ER-Tracker™ Green (Cell Signaling Technology), phalloidin, and fluorescently labeled antibodies to N-Cadherin, E-Cadherin, ZO-1, and collagen. [0174]
Plasma membrane: US 20210169827 A1: FIG. 1F shows immunofluorescence (IF) co-staining of distal tubule in MKD patient kidney organoid for MUC1-wt (red), MUC1-fs (green), E-cadherin (blue) and Na+/K+ ATPase (yellow), which showed that MUC1-fs was localized intracellularly (middle) compared to MUC1-wt, which was apical (left). FIG. 1G depicts IF co-staining in P cells for MUC1-fs (green), MUC1-wt (red) and Hoechst (grey), which showed that MUC1-fs was localized intracellularly (middle) compared to MUC1-wt, which was found on the plasma membrane (left). [0072]
Plasma membrane: US 20220112558 A1 Three experiments performed with independent transductions of HDLECs were quantified for intracellular and plasma membrane staining,[0054]
After 2 days of incubation, z-stack images with a step size of −8.5 μm were taken of the embedded spheroids using an EVOS FL Auto Imaging System (Thermo Fisher Scientific) [0220]
endoplasmic reticulum (ER), Nuclei, Golgi, nucleoli, mitochondria, conjugate membrane: US 20220155281 A1: High-throughput drug screens on these phenotypes uncovers promising drug candidates that rescue disease signatures [0020]
Example Channels Used for Measuring Features Entity Filter Filter component or Channel Dye (excitation; nm) (emission; nm) compartment 1 Hoechst 33342 387/11 417-477 Nucleus 2 Concanavalin A/Alexa 472/30a 503-538a Endoplasmic Fluor 488 conjugate reticulum 3 SYTO 14 green 531/40 573-613 Nucleoli, fluorescent nucleic cytoplasmic acid stain RNAb 4 Phalloidin/Alexa Fluor 562/40 622-662c F-actin 568 conjugate, wheat- cytoskeleton, germ agglutinin/Alexa Golgi, plasma Fluor 555 conjugate membrane 5 MitoTracker Deep Red 628/40 672-712 Mitochondria, TABLE 2
Nuclei, Nucleoli US 20220333200 A1: Using confocal microscopy, we observed that EU-labeled spheroids exhibited the green fluorescence signal of Alexa 488 in the nuclei and especially in the nucleoli of the spheroids, confirming the successful EU incorporation into RNA while unlabeled control spheroids showed virtually no staining (FIGS. 2A, 2A1). [0168]
cytoskeleton: US 20250263729 A1: In addition, to assess the effect of aptamers on the architecture of spheroids, markers were used for the nucleus (DAPI), actin filaments (Phalloidin-FITC) and lysosomes (LysoTracker-Red); the latter to assess whether the addition of aptamers would increase lysosomal activity, since aptamers can be internalized via lysosomes. In the ovarian tumor spheroids, an increase in actin filament staining was observed, showing a change in the cytoskeleton structure and an increase in lysosomal activity observed by the higher intensity of staining by Lysotracker after incubation with the aptamer pool (FIG. 27). In prostate tumor spheroids, the aptamer pool also appears to increase labeling for actin filaments and also causes an increase in lysosomal activity observed by Lysotracker labeling. In addition, rupture of the microsphere was observed, where it was possible to see evident extravasation of cells (FIG. 27). [0184]
Therefore examiner notes that while applicant believes the use of these different stains with organoids to be novel, and points to the type of imaging having differences with the cited references, hundreds of references using fluorescent dyes with organoids as well as single cell imaging exist.
Larsen is used to disclose the concept of imaging a stained component of an organoid (although any of these preceding references could also have also been used to indicate that concept). Combining Zhang is only done to indicate specifically the dye target is “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”. Further, as technically Hoechst 33342 binds DNA/ all nuclei, TO-PRO™-3 Iodide (642/661) binds DNA in cells that have lost membrane integrity, and Larsen explicitly discloses using Hoechst 33342 and TO-PRO™-3 Iodide (642/661), Larsen already discloses staining DNA and nuclei.
As noted, reasons for allowance are: 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.
Applicant further argues:
The Office Action concedes, at page 22, that Larsen and Clevers together do not disclose "the three or more dyes comprise at least three dyes each specific for a different biomarker, cellular component or organelle." Clevers is cited solely for its teaching of a distinct culturing time period. Office Action at page 7. Clevers itself discloses only lipid-droplet and PLIN2- reporter fluorescent staining in the context of steatosis modeling in liver organoids. See, e.g., Clevers at [0112-30]. Clevers contributes nothing to the multi-organelle staining limitation, and it cannot satisfy the deficiencies of Larsen and Zhao. Rather than curing the multi-organelle staining deficiency left open by Larsen and Zhao, Jabs actually reinforces it. The DeathPro assay uses only two dyes, generating a binary readout.
As an initial matter, the claims do not use the language “multi-organelle staining”. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).The language of claim 6 is “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.”
Examiner notes the office action at page 22 states: “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.”
The examiner notes Larsen does disclose “the three or more dyes comprise at least three dyes each specific for a different biomarker” as follows: 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, these each broadly stain for a different “biomarker” as Hoechst 33342 binds DNA/ all nuclei, TO-PRO™-3 Iodide (642/661) binds DNA in cells that have lost membrane integrity, and IncuCyte® Caspase-3/7 Green Apoptosis Assay Reagent reports apoptosis, and apoptosis can also be interpreted as a biomarker.
Applicant further argues on pages 10-11 Jabs and Satchi-Fairnaro do not cure the deficiencies with regard to the multi organelle profiling. Again, the phrase “multi organelle profiling” is not in the claim language. Further, Jabs and Satchi-Fairnaro are not used to teach the three dye limitation. Therefore examiner agrees it does not teach details of staining, but these references are not relied upon for that.
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 dyes wherein the three or more dyes comprise at least three dyes each specific for a different biomarker, cellular component, or organelle (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, these each broadly stain for a different “biomarker” as Hoechst 33342 binds DNA/ all nuclei, TO-PRO™-3 Iodide (642/661) binds DNA in cells that have lost membrane integrity, and IncuCyte® Caspase-3/7 Green Apoptosis Assay Reagent reports apoptosis, and apoptosis can also be interpreted as a biomarker]; 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 one of 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 one of 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 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, 25 and 26 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; 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; 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 identifying candidate compounds having a phenotypic distance score above a threshold value for each of the one or more phenotypic characteristics. It would have been obvious at the time of filing to one of ordinary skill in the art to combine the identifying 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.
Regarding claim 26, Larsen et al. and Clevers et al. and Jabs et al. disclose the method of claim 19. Jabs et al. further teach a phenotypic distance score is compared to an untreated control (“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].
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
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/MICHELLE M ENTEZARI HAUSMANN/Primary Examiner, Art Unit 2671
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