Scoring method for anti-TROP2 antibody-drug conjugate therapy

Abstract

A method for predicting how a cancer patient will respond to an antibody drug conjugate (ADC) therapy includes computing a predicted response score by performing a statistical operation on the staining of each cancer cell in a tissue sample. The ADC includes an ADC payload and an ADC antibody that targets the trophoblast antigen 2 (TROP2) protein on the cancer cell. The tissue sample is stained using a dye linked to a diagnostic antibody that binds to the protein. The cancer cells in the digital image of the tissue are detected. A statistical operation is performed for each cancer cell based on the staining intensity of the dye in the membrane and / or cytoplasm of each cancer cell and / or in the membrane of adjacent cancer cells. The response of the cancer patient to the ADC therapy is predicted based on whether the results of the statistical operation exceed various clinically derived thresholds.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Application No. 63 / 320,629, filed March 16, 2022, the entirety of which is incorporated herein by reference.

[0002] Reference to sequence listings submitted electronically The sequence listing submitted in electronic form (Name: TROP2_402_Seqlisting.xml; Size: 19,496 bytes; and Creation Date: March 5, 2023) is incorporated by reference in its entirety into this specification.

[0003] The present invention relates to a method for calculating a score indicative of how a cancer patient will respond to treatment using an antibody-drug conjugate having a drug conjugated to an anti-TROP2 antibody via a linker structure. [Background technology]

[0004] Evaluating a cancer patient's likely response to a given treatment is an essential step in determining the patient's treatment regimen. Such evaluation is often based on histological analysis of tissue samples from cancer patients, and involves identifying and classifying the cancer using a standard grading scheme. Immunohistochemical (IHC) staining can be used to distinguish marker-positive cells that express a particular protein from marker-negative cells that do not express the protein. IHC staining usually involves multiple dyes, including one or more dyes linked to protein-specific antibodies, and another dye that is a counterstain. A common counterstain is hematoxylin, which labels DNA and therefore stains the nucleus.

[0005] Protein-specific stains or biomarkers can be used to identify areas of a cancer patient's tissue that are likely to respond to a given treatment. For example, a biomarker that stains epithelial cells can help identify suspicious tumor areas. Other protein-specific biomarkers can then be used to characterize cells within the cancerous tissue. Cells stained by a particular biomarker can be identified and quantified, and a score indicating the number of positively and negatively stained cells can then be visually estimated by a pathologist. This score can then be compared to scores of other cancer patients calculated in the same way. If the response of these other patients to a given cancer treatment is known, the pathologist can predict how well the cancer patient will respond to a given treatment based on a comparison of the score calculated for the cancer patient with the scores of other patients. However, visual assessment by a pathologist is prone to variability and subjectivity. Summary of the Invention [Problem to be solved by the invention]

[0006] One promising cancer treatment involves antibody-drug conjugates (ADCs) that have a cytotoxic drug conjugated to an antibody whose antigen is expressed on the surface of cancer cells. The ADCs bind to the antigen, selectively deliver the drug to cancer cells, and accumulate the drug in those cancer cells, where it undergoes cellular internalization to kill them. There is a need for computer-based methods to generate reproducible objective scores that indicate a cancer patient's response to treatments that include therapeutic TROP2 antibody-drug conjugates. [Means for solving the problem]

[0007] A method for predicting how a cancer patient will respond to a treatment comprising an antibody drug conjugate (ADC) includes computing a response score based on the single-cell ADC score for each cancer cell. The ADC comprises an ADC payload and an ADC antibody that targets a protein on each cancer cell. A tissue sample is immunohistochemically stained using a dye linked to a diagnostic antibody that binds to a protein on the cancer cell in the tissue sample. A digital image of the tissue sample is acquired. Image analysis is performed on the digital image to detect the cancer cells using a convolutional neural network. For each cancer cell, a single-cell ADC score is computed based on the staining intensity of the dye in the membrane and / or cytoplasm of the cancer cell and / or in the membrane and cytoplasm of other cancer cells closer than a predetermined distance to the cancer cell. A response score is generated that predicts the cancer patient's response to the ADC therapy by aggregating all the single-cell ADC scores of the tissue sample using a statistical operation. Patients with a response score higher than a predetermined threshold are recommended for therapy comprising ADC.

[0008] In one embodiment, a method for predicting a cancer patient's response to an ADC includes computing a single-cell ADC score. A cancer patient tissue sample is immunohistochemically stained using a dye linked to a diagnostic antibody. The ADC includes an ADC payload and an ADC antibody that targets trophoblast cell surface antigen 2 (TROP2) protein on the cancer cells. The diagnostic antibody binds to the TROP2 protein on the cancer cells in the tissue sample. A digital image of the tissue sample is acquired, and image analysis is used to detect the cancer cells in the digital image. For each cancer cell, a single-cell ADC score is computed based on the staining intensity of the dye in the membrane. The single-cell ADC score may also optionally be based on the staining intensity of the dye in the cytoplasm of the cancer cell, as well as the staining intensity of the dye in the membrane and in the cytoplasm of other cancer cells that are closer than a predetermined distance to the cancer cell. The staining intensity of each membrane is computed based on the average optical density of the brown diaminobenzidine (DAB) signal in the membrane pixels, and the staining intensity of each cytoplasm is computed based on the average optical density of the brown DAB signal in the cytoplasm pixels. The resulting quantitative continuous score (QCS), which indicates the response of the cancer patient to ADC, is generated by aggregating all single-cell ADC scores of the tissue sample using a statistical operation. The aggregation of all single-cell ADC scores is performed by determining the mean, determining the median, or determining quantiles at a predetermined percentage. In another embodiment, the aggregation of all single-cell ADC scores includes a threshold operation using a predetermined threshold. All cells with single-cell ADC scores greater than the predetermined threshold are labeled as single-cell ADC positive. The aggregation is performed by dividing the number of single-cell ADC positive cells by the number of total cancer cells. In one example, the QCS score is a continuous spatial proximity score.

[0009] In another embodiment, a method for identifying a cancer patient for treatment with an ADC includes determining a binary spatial proximity score of a tissue sample of the cancer patient. The ADC includes an ADC payload and an ADC antibody that targets the TROP2 protein on the cancer cells. A tissue sample from the cancer patient is immunohistochemically stained using a dye linked to a diagnostic antibody that binds to the TROP2 protein on the cancer cells in the tissue sample. A digital image of the tissue sample is obtained and a convolutional neural network is used to detect cancer cells in the digital image. The cancer cells are detected in the digital image. For each cancer cell, the average optical density of staining with the dye at the cell membrane is measured. Each cancer cell is identified as either optical density positive if the average optical density of the cancer cells is equal to or greater than an optical density threshold, or optical density negative if the average optical density of the cancer cells is less than an optical density threshold. A tissue sample is identified as either optical density positive or optical density negative, but as proximity positive if the total percentage of cancer cells within a predetermined distance from an optical density positive cancer cell exceeds a proximity percentage threshold. When the tissue sample is proximity positive, the cancer patient is identified as the patient who is likely to benefit from the administration of ADC.When the tissue sample is proximity positive, the therapy comprising ADC is recommended for the cancer patient.In another embodiment, the method of treating cancer comprises administering ADC to the cancer patient when the tissue sample is proximity positive.

[0010] In another embodiment, a method for identifying a cancer patient for treatment with an ADC includes determining a normalized staining of the cell membrane of stained cancer cells. The ADC includes an ADC payload and an ADC antibody. A cancer patient tissue sample is immunohistochemically stained using a dye linked to a diagnostic antibody. Both the diagnostic antibody and the ADC antibody target the TROP2 protein on the cancer cells. A digital image of the tissue sample is acquired. Image analysis is performed on the digital image to detect cancer cells using a convolutional neural network. For each cancer cell, the average optical density of the dye staining in the membrane of the cancer cell is measured, and the average optical density of the dye staining in the cytoplasm of the cancer cell is measured. A normalized membrane optical density (nmOD) of each cancer cell in the digital image is computed, which is equal to the average optical density of the membrane staining divided by the sum of the average optical density of the membrane staining + the average optical density of the cytoplasmic staining. If the nmOD of the cancer cell is equal to or less than the nmOD threshold, each cancer cell is identified as nmOD positive. When the percentage of cancer cells that are nmOD positive is equal to or greater than the nmOD percentage threshold, the tissue sample is identified as nmOD positive.When the tissue sample is nmOD positive, the cancer patient is identified as the patient that is likely to benefit from the administration of ADC.When the tissue sample is nmOD positive, the therapy that includes ADC is recommended for the cancer patient.In another embodiment, the method of treating cancer comprises administering ADC to the cancer patient when the tissue sample is nmOD positive.

[0011] In another embodiment, the method of identifying cancer patients for treatment with ADC is based on both spatial proximity of differently stained cancer cells and normalized staining of cell membranes of stained cancer cells. The ADC comprises an ADC payload and an ADC antibody. A cancer patient tissue sample is immunohistochemically stained using a dye linked to a diagnostic antibody. Both the diagnostic antibody and the ADC antibody target the TROP2 protein on the cancer cells. A digital image of the tissue sample is obtained. Image analysis is performed on the digital image to detect cancer cells using a convolutional neural network. For each cancer cell, the mean optical density of the membrane staining and the mean optical density of the cytoplasmic staining are measured.

[0012] A normalized membrane optical density (nmOD) is computed for each cancer cell in the digital image, which is equal to the average optical density of the membrane stain divided by the sum of the average optical density of the membrane stain plus the average optical density of the cytoplasmic stain. If the nmOD of the cancer cell is equal to or less than the nmOD threshold, each cancer cell is identified as nmOD positive. If the percentage of cancer cells that are nmOD positive is equal to or greater than the nmOD percentage threshold, the tissue sample is identified as nmOD positive. Each cancer cell is identified as either optical density positive if the average optical density of the membrane stain is equal to or greater than the optical density threshold, or optical density negative if the average optical density of the membrane stain is less than the optical density threshold. A tissue sample is identified as either optical density positive or optical density negative, but as proximity positive if the total percentage of cancer cells within a predetermined distance from an optical density positive cancer cell exceeds a proximity percentage threshold. A cancer patient is identified as a patient who is likely to benefit from administration of an ADC if the tissue sample is both nmOD positive and proximity positive. If the tissue sample is both nmOD positive and proximity positive, a therapy including an ADC is recommended for the cancer patient. In another embodiment, a method of treating cancer comprises administering an ADC to a cancer patient where a tissue sample is both nmOD positive and proximity positive.

[0013] Other embodiments and advantages are described in the detailed description below. This summary does not define the invention. The invention is defined by the claims.

[0014] The accompanying drawings, where like reference numbers indicate like elements, illustrate embodiments of the present invention. [Brief description of the drawings]

[0015] [Figure 1] The amino acid sequence of the heavy chain of the anti-TROP2 antibody (SEQ ID NO: 1) is shown. [Diagram 2] The amino acid sequence of the light chain of the anti-TROP2 antibody (SEQ ID NO:2) is shown. [Diagram 3] The amino acid sequence of CDRH1 of the anti-TROP2 antibody (SEQ ID NO:3) is shown. [Figure 4]The amino acid sequence of CDRH2 of the anti-TROP2 antibody (SEQ ID NO:4) is shown. [Diagram 5] The amino acid sequence of CDRH3 of the anti-TROP2 antibody (SEQ ID NO:5) is shown. [Figure 6] The amino acid sequence of CDRL1 of the anti-TROP2 antibody (SEQ ID NO:6) is shown. [Figure 7] The amino acid sequence of CDRL2 of the anti-TROP2 antibody (SEQ ID NO:7) is shown. [Figure 8] The amino acid sequence of CDRL3 of the anti-TROP2 antibody (SEQ ID NO:8) is shown. [Figure 9] The amino acid sequence of the heavy chain variable region of the anti-TROP2 antibody (sequence number 9) is shown. [Figure 10] The amino acid sequence of the light chain variable region of the anti-TROP2 antibody (sequence number 10) is shown. [Figure 11] The amino acid sequence of the heavy chain of the anti-TROP2 antibody (sequence number 11) is shown. [Figure 12] 1 shows datopotamab deruxtecan, an anti-TROP2 antibody-drug conjugate with four drug-linker units. [Figure 13] 1 is a flowchart of steps in which an analysis system analyzes digital images of tissue from a cancer patient to predict how the cancer patient is likely to respond to a treatment that includes an anti-TROP2 antibody-drug conjugate. [Figure 14] 14 shows digital images illustrating the image analysis process of step 12 of FIG. 13. [Figure 15] 1 illustrates the image analysis steps in which nuclear objects of cancer cells are detected. [Figure 16] 1 shows the image analysis steps to detect membranes using nuclear objects. [Figure 17] 13 is a screenshot of the results of the image analysis step in an image analysis software environment. [Figure 18] 1 shows a sample calculation of binary spatial proximity scores of 10 exemplary cells based on cell separation to reflect uptake of ADC payload into neighboring cells. [Figure 19]Demonstrates the mechanism by which anti-TROP2 ADC therapy kills cancer cells. [Figure 20] Sample quantification of staining intensity from image analysis using membrane and cytoplasmic pixel grey values ​​is shown. [Figure 21] Exemplary quantitative staining amounts in the membrane and cytoplasm of the images in FIG. 20 are listed. [Figure 22] Calculation of the contiguous spatial proximity scores for each of the three cells shown in FIG. 21 based on cell separation to reflect uptake of the ADC payload into neighboring cells is shown. [Figure 23] The formula for calculating the continuous spatial proximity score for each cancer cell is shown below. [Figure 24] FIG. 14 is a plot showing the response to ADC administered to 115 patients in a clinical trial in terms of tumor growth / shrinkage compared to the response score (Bystander_Membrane(Mean OD)_Binary_r50_Cut25) of the method of FIG. 13. In the plot, patients are designated as having progressive disease (PD), stable disease (SD), partial response (PR), or not evaluable (NE). [Diagram 25] Graph of Kaplan-Meier curves of progression-free survival for two groups of 115 patients stratified into QCS-positive and QCS-negative patients using the binary spatial proximity score of the method of Figure 13. The graph shows the objective response rate (ORR) for QCS-positive and QCS-negative patients. [Figure 26] Figure 25 shows spider plots of Kaplan-Meier curves of tumor shrinkage over time for 88 bSPS-positive and 27 bSPS-negative patients. [Figure 27] FIG. 26 is a waterfall bar graph showing clinical responses of 105 of the 115 patients in FIG. 25 ordered from greatest tumor growth to greatest tumor shrinkage. [Figure 28] 1 is a flow chart of the steps of a novel method for predicting a cancer patient's response to an ADC based on the normalized membrane optical density of each cancer cell. [Figure 29]FIG. 29 is a plot showing the response to ADC administered to 115 patients in terms of tumor growth / shrinkage compared to the response score (Membrane(Mean OD) / (Membrane(Mean OD)+Cytoplasmic(Membrane OD))_q5) of the method of FIG. 28. The plot identifies patients as having progressive disease (PD), stable disease (SD), partial response (PR), or not evaluable (NE). [Diagram 30] Graph of Kaplan-Meier curves of progression-free survival for two groups of 115 patients stratified into QCS-positive and QCS-negative patients using the response score of the method of Figure 28. The graph shows the objective response rate (ORR) for QCS-positive and QCS-negative patients. [Diagram 31] Figure 30 shows spider plots of Kaplan-Meier curves of tumor shrinkage over time for 84 QCS-positive and 31 QCS-negative patients. [Diagram 32] FIG. 31 is a waterfall bar graph showing clinical responses of 105 of the 115 patients in FIG. 30 ordered from greatest tumor growth to greatest tumor shrinkage. [Diagram 33] 1 is a flow chart of the steps of a novel method for predicting a cancer patient's response to an ADC based on a combination of binary spatial proximity scores and normalized membrane optical density scores. [Diagram 34] Graph of Kaplan-Meier curves of progression-free survival for two groups of 115 patients stratified into QCS-positive and QCS-negative patients using the response score of the method of Figure 33. The graph shows the objective response rate (ORR) for QCS-positive and QCS-negative patients. [Diagram 35] Figure 34 shows spider plots of Kaplan-Meier curves of tumor shrinkage over time for 80 QCS-positive and 35 QCS-negative patients. [Diagram 36] FIG. 35 is a waterfall bar graph showing clinical responses of 105 of the 115 patients in FIG. 34 ordered from greatest tumor growth to greatest tumor shrinkage. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] The present invention relates to a novel method for predicting a cancer patient's response to an antibody-drug conjugate, including an antibody-drug conjugate (ADC) that targets the calcium signal transducer trophoblast antigen 2 (TROP2) protein on cancer cells, where the response is predicted based on a statistical operation using the measured optical density of staining with a dye linked to a diagnostic antibody that also targets the TROP2 protein. Another aspect of the present invention relates to a method for identifying a cancer patient for treatment with an ADC based on a QCS score. Another aspect of the present invention relates to identifying a cancer patient that exhibits a predetermined response to an ADC. Another aspect of the present invention relates to a method for treating a cancer patient by administering a therapy that includes an ADC based on a quantitative continuous score (QCS) that is computerized using one of the statistical operations.

[0017] A first embodiment relates to identifying cancer patients who are likely to benefit from administration of an ADC based on a binary spatial proximity score equal to the percentage of cancer cells in a patient's tissue sample that have an average optical density of staining above an optical density threshold, or have an average optical density of staining below an optical density threshold but are located within a predetermined distance from cells whose average optical density of staining is above the optical density threshold.

[0018] A second embodiment relates to identifying cancer patients who are likely to benefit from administration of an ADC based on the normalized membrane optical density of each cancer cell, which is equal to the mean optical density of the membrane staining divided by the sum of the mean optical density of the membrane staining plus the mean optical density of the cytoplasmic staining.

[0019] A third embodiment relates to identifying cancer patients likely to benefit from administration of an ADC based on both a binary spatial proximity score of normalized membrane optical density: if the binary spatial proximity score exceeds a proximity percentage threshold and the percentage of cancer cells that are normalized membrane positive is equal to or greater than a normalized percentage threshold, the cancer patient is identified as likely to benefit from an ADC.

[0020] I. Definition To facilitate understanding of the present invention, a number of terms and expressions are defined below. The term "cancer" is used synonymously with the term "tumor". In this disclosure, "TROP2" is synonymous with calcium signal transducer trophoblast antigen 2 transmembrane protein. In this disclosure, "TROP2 protein" is used synonymously with TROP2. Expression of TROP2 protein can be detected using methods well known to those skilled in the art, such as immunohistochemistry (IHC) or immunofluorescence (IF).

[0021] In the present invention, "anti-TROP2 antibody" means an antibody that specifically binds to TROP2. The anti-TROP2 antibody has the activity of binding to TROP2, and is thereby internalized in TROP2-expressing cells, and after exhibiting the activity of binding to TROP2, the antibody migrates to TROP2-expressing cells. The anti-TROP2 antibody targets tumor cells, binds to tumor cells, is internalized in tumor cells, exhibits cytocidal activity against tumor cells, and can be conjugated with a drug having antitumor activity via a linker to form an antibody-drug conjugate.

[0022] 1 shows the amino acid sequence of the heavy chain of an exemplary anti-TROP2 antibody (SEQ ID NO: 1). The heavy chain has a signal sequence (1-19), a variable region (20-140), and a constant region (141-470).

[0023] 2 shows the amino acid sequence of the light chain of an exemplary anti-TROP2 antibody (SEQ ID NO: 2). The light chain has a signal sequence (1-20), a variable region (21-129), and a constant region (130-234).

[0024] FIG. 3 shows the amino acid sequence of CDRH1 of an exemplary anti-TROP2 antibody (SEQ ID NO: 3), FIG. 4 shows the amino acid sequence of CDRH2 of an exemplary anti-TROP2 antibody (SEQ ID NO: 4), and FIG. 5 shows the amino acid sequence of CDRH3 of an exemplary anti-TROP2 antibody (SEQ ID NO: 5).

[0025] FIG. 6 shows the amino acid sequence of CDRL1 of an exemplary anti-TROP2 antibody (SEQ ID NO: 6), FIG. 7 shows the amino acid sequence of CDRL2 (SAS) of an exemplary anti-TROP2 antibody (SEQ ID NO: 7), and FIG. 8 shows the amino acid sequence of CDRL3 of an exemplary anti-TROP2 antibody (SEQ ID NO: 8).

[0026] Figure 9 shows the amino acid sequence of the heavy chain variable region of an exemplary anti-TROP2 antibody (SEQ ID NO: 9), Figure 10 shows the amino acid sequence of the light chain variable region of an exemplary anti-TROP2 antibody (SEQ ID NO: 10), and Figure 11 shows the amino acid sequence of another heavy chain of an exemplary anti-TROP2 antibody (SEQ ID NO: 11).

[0027] The anti-TROP2 antibody of the anti-TROP2 antibody-drug conjugate used in the present invention preferably comprises a heavy chain including CDRH1 (amino acid sequence consisting of amino acid residues 50 to 54 of SEQ ID NO: 1) consisting of the amino acid sequence shown in SEQ ID NO: 3, CDRH2 (amino acid sequence consisting of amino acid residues 69 to 85 of SEQ ID NO: 1) consisting of the amino acid sequence shown in SEQ ID NO: 4, and CDRH3 (amino acid sequence consisting of amino acid residues 118 to 129 of SEQ ID NO: 1) consisting of the amino acid sequence shown in SEQ ID NO: 5, and a CDRL1 (amino acid sequence consisting of amino acid residues 44 to 54 of SEQ ID NO: 2) consisting of the amino acid sequence shown in SEQ ID NO: 6. and a light chain comprising a heavy chain variable region comprising an amino acid sequence represented by SEQ ID NO: 1 (amino acid sequence consisting of amino acid residues 20 to 140 of SEQ ID NO: 1) consisting of the amino acid sequence represented by SEQ ID NO: 7, and a light chain comprising a CDRL3 (amino acid sequence consisting of amino acid residues 109 to 117 of SEQ ID NO: 2) consisting of the amino acid sequence represented by SEQ ID NO: 8, and more preferably an antibody comprising a heavy chain variable region comprising an amino acid sequence represented by SEQ ID NO: 9 (amino acid sequence consisting of amino acid residues 20 to 140 of SEQ ID NO: 1) and a light chain variable region comprising an amino acid sequence represented by SEQ ID NO: 10 (amino acid sequence consisting of amino acid residues 21 to 129 of SEQ ID NO: 2). In another preferred embodiment, the antibody comprises a heavy chain comprising an amino acid sequence represented by SEQ ID NO: 11 (amino acid sequence consisting of amino acid residues 20 to 469 of SEQ ID NO: 1) and a light chain comprising an amino acid sequence represented by amino acid residues 21 to 234 of SEQ ID NO: 2. In another embodiment, the antibody comprises a heavy chain comprising an amino acid sequence represented by amino acid residues 20 to 470 of SEQ ID NO: 1 and a light chain comprising an amino acid sequence represented by amino acid residues 21 to 234 of SEQ ID NO: 2.

[0028] In the present invention, the term "QCS positive" (QCS+) refers to cancers that are more likely to respond to anti-TROP2 ADC therapy. The term "QCS negative" (QCS-) refers to cancers that are less likely to respond to anti-TROP2 ADC therapy. The acronym QCS stands for Quantitative Continuous Score. The result of the novel prediction method of the present invention is generally called a quantitative continuous score, which can be an indication of response score, treatment score or predicted survival time. The QCS score is obtained by performing statistical operations on all the single-cell ADC scores obtained for a patient. By applying a predefined threshold to the QCS score, "QCS positive" and "QCS negative" patients are distinguished. Stratification of cancer patients into QCS positive and QCS negative groups allows the identification of QCS+ patients who are more likely to benefit from therapy including ADC.

[0029] II. Anti-TROP2 antibody-drug conjugates. An exemplary antibody-drug conjugate for use in this disclosure has the following formula: [Formula 1] [ka] (wherein A represents the attachment position to the antibody) is conjugated to the anti-TROP2 antibody via a thioether bond.

[0030] In the present disclosure, the partial structure consisting of the linker and the drug in the antibody-drug conjugate is called a "drug-linker". The drug-linker is linked to a thiol group (in other words, a sulfur atom of a cysteine ​​residue) formed at an interchain disulfide bond site (two sites between heavy chains and two sites between heavy and light chains) in the antibody.

[0031] The drug-linker of the present disclosure includes exatecan (IUPAC name: (1S,9S)-1-amino-9-ethyl-5-fluoro-1,2,3,9,12,15-hexahydro-9-hydroxy-4-methyl-10H,13H-benzo[de]pyrano[3',4':6,7]indolizino[1,2-b]quinoline-10,13-dione, (also represented as chemical name: (1S,9S)-1-amino-9-ethyl-5-fluoro-2,3-dihydro-9-hydroxy-4-methyl-1H,12H-benzo[de]pyrano[3',4':6,7]indolizino[1,2-b]quinoline-10,13(9H,15H)-dione) as a component, which is a topoisomerase I inhibitor. Exatecan is a camptothecin derivative with antitumor activity and has the following formula: [Formula 2] [ka] It is expressed as:

[0032] Exemplary anti-TROP2 antibody-drug conjugates for use in the present disclosure also have the following formula: [Formula 3] [ka] It can be expressed as:

[0033] Here, the drug-linker is conjugated to the anti-TROP2 antibody ("antibody-") via a thioether bond. The meaning of n is the same as that of what is called the average number of drug molecules conjugated (DAR; drug-antibody ratio), which refers to the average number of drug-linker units conjugated per antibody molecule.

[0034] Figure 12 shows datopotamab deruxtecan (DS-1062), an anti-TROP2 antibody-drug conjugate with four drug-linker units designated "DL". The portion of datopotamab deruxtecan shown in Figure 12 is a humanized anti-TROP2 IgG1 mAb, where IgG1 indicates the isotype of the anti-TROP2 antibody.

[0035] After being transferred into the cancer cells, the anti-TROP2 antibody-drug conjugate used in the present disclosure is cleaved at the linker moiety to give the following formula: [Formula 4] [ka] It is expressed as:

[0036] The compounds shown above are the main source of anti-tumor activity of the preferred anti-TROP2 antibody-drug conjugates used in the present invention, and have topoisomerase I inhibitory activity.

[0037] The anti-TROP2 antibody-drug conjugate used in the present invention also has a bystander effect in which the anti-TROP2 antibody-drug conjugate is internalized into cancer cells expressing the target protein TROP2, and then the compound exerts an anti-tumor effect on adjacent cancer cells that do not express the target protein TROP2.

[0038] III. Anti-TROP2 antibodies in antibody-drug conjugates. The anti-TROP2 antibody in the antibody-drug conjugate used in the present invention can be from any species, and is preferably an antibody from human, rat, mouse, or rabbit. If the antibody is derived from a species other than the human species, it is preferably chimerized or humanized using well-known techniques. The antibody of the present invention can be a polyclonal antibody or a monoclonal antibody, and is preferably a monoclonal antibody.

[0039] The antibody in the antibody-drug conjugate used in the present invention is preferably an antibody having characteristics capable of targeting cancer cells, and is preferably an antibody having, for example, the property of being able to recognize cancer cells, the property of being able to bind to cancer cells, the property of being internalized into cancer cells, and / or cytocidal activity against cancer cells.

[0040] The binding activity of the antibody to cancer cells can be confirmed using flow cytometry. The internalization of the antibody into tumor cells can be confirmed using (1) an assay in which the binding of a secondary antibody (fluorescently labeled) to the therapeutic antibody is used to visualize the antibody taken up by the cell under a fluorescent microscope (Cell Death and Differentiation (2008) 15, 751-761), (2) an assay in which the binding of a secondary antibody (fluorescently labeled) to the therapeutic antibody is used to measure the intensity of the fluorescence taken up in the cell (Molecular Biology of the Cell, Vol. 15, 5268-5282, December 2004), or (3) a Mab-ZAP assay in which the binding of an immunotoxin to the therapeutic antibody is used, and the toxin is released upon uptake into the cell to inhibit cell growth (Bio Techniques 28: 162-165, January 2000). As the immunotoxin, a recombinant complex protein of the catalytic domain of diphtheria toxin and protein G can be used.

[0041] The antitumor activity of the antibody can be confirmed in vitro by measuring the inhibitory activity against cell growth.For example, a cancer cell line that overexpresses the target protein against the antibody is cultured, and the antibody is added to the culture system at various concentrations to measure the inhibitory activity against focus formation, colony formation and spheroid growth.For example, the antibody can be administered to nude mice transplanted with a cancer cell line that highly expresses the target protein, and the antitumor activity can be confirmed in vivo by measuring the change in the cancer cells.

[0042] Since the compound conjugated in the antibody-drug conjugate exerts an antitumor effect, it is preferable, but not essential, that the antibody itself has an antitumor effect. For the purpose of specifically and selectively exerting the cytotoxic activity of the antitumor compound on cancer cells, it is important and preferable that the antibody will have the property of being internalized and translocated into the cancer cells.

[0043] The antibody in the antibody-drug conjugate used in the present invention can be obtained by procedures known in the art. For example, the antibody of the present invention can be obtained using the method usually practiced in the art, which involves immunizing animals with antigenic polypeptides and collecting and purifying the antibodies produced in vivo. The source of this antigen is not limited to humans, and animals can be immunized with antigens from non-human animals such as mice and rats. In this case, the obtained antibody that binds to the heterologous antigen can be tested for cross-reactivity with human antigens to screen for antibodies that can be applied to human diseases.

[0044] Alternatively, antibody-producing cells that produce antibodies against an antigen can be fused with myeloma cells according to methods known in the art (e.g., Kohler and Milstein, Nature (1975) 256, p. 495-497; Kennet, R. ed., Monoclonal Antibodies, p. 365-367, Plenum Press, NY (1980)) to establish hybridomas, from which monoclonal antibodies can then be obtained.

[0045] The antigen can be obtained by genetically engineering a host cell to produce a gene encoding an antigen protein. Specifically, a vector capable of expressing the antigen gene is prepared and introduced into a host cell to express the gene. The antigen thus expressed can be purified. The antibody can also be obtained by immunizing an animal with the genetically engineered antigen-expressing cell or cell line expressing the antigen.

[0046] The antibody in the antibody-drug conjugate used in the present invention is preferably a recombinant antibody obtained by artificial modification aimed at reducing xenoantigenicity to humans, such as a chimeric antibody or a humanized antibody, or is preferably an antibody of human origin, i.e., an antibody having only the gene sequence of a human antibody. These antibodies can be produced using known methods.

[0047] An example of a chimeric antibody is an antibody whose variable and constant regions are derived from different species, for example, a chimeric antibody in which the variable region of an antibody derived from a mouse or rat is linked to the constant region of an antibody derived from a human (Proc. Natl. Acad. Sci. USA, 81, 6851-6855, (1984)).

[0048] Examples of humanized antibodies include antibodies obtained by incorporating only the complementarity determining region (CDR) of a heterologous antibody into a human-derived antibody (Nature (1986) 321, pp. 522-525), antibodies obtained by grafting a portion of the amino acid residues of the framework of a heterologous antibody and the CDR sequence of the heterologous antibody into a human antibody by a CDR grafting method (WO 90 / 07861 pamphlet), and antibodies humanized using a gene conversion mutagenesis strategy (U.S. Pat. No. 5,821,337 specification).

[0049] Examples of human antibodies include antibodies produced by using a human antibody-producing mouse having a human chromosome fragment containing the heavy and light chain genes of a human antibody (see, for example, Tomizuka, K. et al., Nature Genetics (1997) 16, p. 133-143; Kuroiwa, Y. et al., Nucl. Acids Res. (1998) 26, p. 3447-3448; Yoshida, H. et al., Animal Cell Technology: Basic and Applied Aspects vol. 10, p. 69-73 (Kitagawa, Y., Matsuda, T. and Iijima, S. eds.), Kluwer Academic Publishers, 1999; Tomizuka, K. et al., Proc. Natl. Acad. Sci. USA (2000) 97, p. 722-727). As alternatives, there can be mentioned antibodies obtained by phage display and antibodies selected from human antibody libraries (see, for example, Wormstone, I Met. al, Investigative Ophthalmology & Visual Science. (2002) 43(7), p. 2301-2308; Carmen, S. et. al., Briefings in Functional Genomics and Proteomics (2002), 1(2), p. 189-203; Siriwardena, D. et. al., Ophthalmology (2002) 109(3), p. 427-431).

[0050] In the antibody in the antibody-drug conjugate used in the present invention, modified variants of the antibody are also included. Modified variants refer to variants obtained by subjecting the antibody according to the present invention to chemical or biological modification. Examples of chemically modified variants include variants including attachment of a chemical moiety to the amino acid backbone, variants including attachment of a chemical moiety to an N-linked or O-linked carbohydrate chain, etc. Examples of biologically modified variants include variants obtained by post-translational modification (e.g., N-linked or O-linked glycosylation, N-terminal or C-terminal processing, deamidation, isomerization of aspartic acid, or oxidation of methionine) and variants in which a methionine residue is added to the N-terminus by expression in a prokaryotic host cell. Furthermore, antibodies labeled to enable detection or isolation of the antibody or antigen according to the present invention, such as enzyme-labeled, fluorescent-labeled, and affinity-labeled antibodies, are also included in the meaning of modified variants. Such modified variants of the antibody according to the present disclosure are useful for improving the stability and blood retention of the antibody, reducing its antigenicity, detection or isolation of the antibody or antigen, etc.

[0051] Furthermore, it is possible to enhance antibody-dependent cellular cytotoxicity by adjusting the modification (glycosylation, defucosylation, etc.) of the glycan linked to the antibody according to the present invention. Techniques for adjusting the modification of the glycan of an antibody are known in WO 99 / 54342, WO 00 / 61739, WO 02 / 31140, WO 2007 / 133855, WO 2013 / 120066, etc. However, this technique is not limited thereto. The antibody according to the present invention also includes an antibody in which the modification of the glycan is adjusted.

[0052] It is known that the lysine residue at the carboxyl terminus of the heavy chain of an antibody produced in cultured mammalian cells is deleted (Journal of Chromatography A, 705:129-134 (1995)), and it is also known that two amino acid residues (glycine and lysine) are deleted at the carboxyl terminus of the heavy chain of an antibody produced in cultured mammalian cells, and a proline residue newly positioned at this carboxyl terminus is amidated (Analytical Biochemistry, 360:75-83 (2007)). However, such deletions and modifications of the heavy chain sequence do not affect the antigen-binding affinity and effector functions (complement activation, antibody-dependent cellular cytotoxicity, etc.) of the antibody. Therefore, the antibody according to the present invention includes an antibody subjected to such modification and a functional fragment of the antibody, and also includes a deletion variant in which one or two amino acids are deleted at the carboxyl terminus of the heavy chain, a variant obtained by amidation of the deletion variant (e.g., a heavy chain in which a proline residue at the carboxyl terminus is amidated), and the like. The type of deletion variant having a deletion at the carboxyl terminus of the heavy chain of the antibody according to the present invention is not limited to the above variants, so long as the antigen-binding affinity and effector function are preserved. The two heavy chains constituting the antibody according to the present invention may be one type selected from the group consisting of a full-length heavy chain and the deletion variants described above, or a combination of two types selected from these. The ratio of the amount of each deletion variant may be influenced by the type and culture conditions of cultured mammalian cells producing the antibody according to the present invention, but an antibody in which one amino acid residue is deleted at the carboxyl terminus of both of the two heavy chains in the antibody according to the present invention may be preferably exemplified.

[0053] Examples of the isotype of the antibody according to the present invention include IgG (IgG1, IgG2, IgG3, IgG4). Preferably, IgG1 or IgG2 is mentioned.

[0054] In the present invention, the term "anti-TROP2 antibody" refers to an antibody that specifically binds to TROP2 (TACSTD2: tumor-associated calcium signal transducer 2; EGP-1), and preferably has internalization activity in TROP2-expressing cells upon binding to TROP2.

[0055] An example of an anti-TROP2 antibody is hTINA1-H1L1 (WO 2015 / 098099).

[0056] IV. Production of anti-TROP2 antibody-drug conjugates. A drug-linker intermediate for use in making antibody-drug conjugates according to the invention has the following formula: [Formula 5] [ka] It is expressed as:

[0057] The drug-linker intermediate has the chemical name N-[6-(2,5-dioxo-2,5-dihydro-1H-pyrrol-1-yl)hexanoyl]glycylglycyl-L-phenylalanyl-N-[(2-{[(1S,9S)-9-ethyl-5-fluoro-9-hydroxy-4-methyl-10,13-dioxo-2,3,9,10,13,15-hexahydro-1H,12H-benzo[de]pyrano[3',4':6,7] It can be represented as indolizino[1,2-b]quinolin-1-yl]amino}-2-oxoethoxy)methyl]glycinamide, and can be produced by reference to the descriptions in International Publication Nos. 2014 / 057687, 2015 / 098099, 2015 / 115091, 2015 / 155998, and 2019 / 044947, etc.

[0058] The antibody-drug conjugates used in the present invention can be prepared by reacting the drug-linker intermediates described above and an anti-TROP2 antibody bearing a thiol group (alternatively referred to as a sulfhydryl group).

[0059] Anti-TROP2 antibodies having sulfhydryl groups can be obtained by methods well known in the art (Hermanson, GT, Bioconjugate Techniques, pp. 56-136, pp. 456-493, Academic Press (1996)). For example, an antibody having sulfhydryl groups with partially or completely reduced interchain disulfides in the antibody can be obtained by reacting the antibody with a reducing agent such as tris(2-carboxyethyl)phosphine hydrochloride (TCEP) in a buffer solution containing a chelating agent such as ethylenediaminetetraacetic acid (EDTA) using 0.3 to 3 molar equivalents per interchain disulfide in the antibody.

[0060] Furthermore, by using 2-20 molar equivalents of drug-linker intermediate per antibody bearing a sulfhydryl group, antibody-drug conjugates in which 2-8 drug molecules are conjugated per antibody molecule can be made.

[0061] The average number of drug molecules conjugated per antibody molecule of the prepared antibody-drug conjugate can be determined, for example, by a calculation method based on measurement of UV absorption for the antibody-drug conjugate and the conjugate precursor at two wavelengths, 280 nm and 370 nm (UV method), or by a calculation method based on quantification through HPLC measurement of fragments obtained by treating the antibody-drug conjugate with a reducing agent (HPLC method).

[0062] Calculations of the conjugate between the antibody and the drug-linker intermediate and the average number of drug molecules conjugated per antibody molecule of the antibody-drug conjugate can be made with reference to the descriptions in, for example, WO 2015 / 098099 and WO 2017 / 002776.

[0063] In the present invention, the term "anti-TROP2 antibody-drug conjugate" refers to an antibody-drug conjugate according to the present invention, in which the antibody in the antibody-drug conjugate according to the present invention is an anti-TROP2 antibody.

[0064] The anti-TROP2 antibody is preferably an antibody comprising a heavy chain including CDRH1 consisting of the amino acid sequence represented by SEQ ID NO:3 [= the amino acid sequence consisting of amino acid residues 50 to 54 of SEQ ID NO:1], CDRH2 consisting of the amino acid sequence represented by SEQ ID NO:4 [= the amino acid sequence consisting of amino acid residues 69 to 85 of SEQ ID NO:1], and CDRH3 consisting of the amino acid sequence represented by SEQ ID NO:5 [= the amino acid sequence consisting of amino acid residues 118 to 129 of SEQ ID NO:1], and a light chain including CDRL1 consisting of the amino acid sequence represented by SEQ ID NO:6 [= the amino acid sequence consisting of amino acid residues 44 to 54 of SEQ ID NO:2], CDRL2 consisting of the amino acid sequence represented by SEQ ID NO:7 [= the amino acid sequence consisting of amino acid residues 70 to 76 of SEQ ID NO:2], and CDRL3 consisting of the amino acid sequence represented by SEQ ID NO:8 [= the amino acid sequence consisting of amino acid residues 109 to 117 of SEQ ID NO:2], Preferably, the antibody comprises a heavy chain comprising a heavy chain variable region consisting of the amino acid sequence represented by SEQ ID NO:9 [= the amino acid sequence consisting of amino acid residues 20 to 140 of SEQ ID NO:1] and a light chain comprising a light chain variable region consisting of the amino acid sequence represented by SEQ ID NO:10 [= the amino acid sequence consisting of amino acid residues 21 to 129 of SEQ ID NO:2]. Even more preferably, the antibody comprises a heavy chain comprising the amino acid sequence represented by SEQ ID NO:12 [= the amino acid sequence consisting of amino acid residues 20 to 470 of SEQ ID NO:1] and a light chain comprising the amino acid sequence represented by SEQ ID NO:13 [= the amino acid sequence consisting of amino acid residues 21 to 234 of SEQ ID NO:2], or an antibody comprises a heavy chain comprising the amino acid sequence represented by SEQ ID NO:11 [= the amino acid sequence consisting of amino acid residues 20 to 469 of SEQ ID NO:1] and a light chain comprising the amino acid sequence represented by SEQ ID NO:13 [= the amino acid sequence consisting of amino acid residues 21 to 234 of SEQ ID NO:2].

[0065] The average number of drug-linker conjugate units per antibody molecule in the anti-TROP2 antibody-drug conjugate is preferably 2 to 8, more preferably 3 to 5, even more preferably 3.5 to 4.5, and even more preferably about 4.

[0066] Anti-TROP2 antibody-drug conjugates can be prepared with reference to the descriptions in WO 2015 / 098099 and WO 2017 / 002776.

[0067] In a preferred embodiment, the anti-TROP2 antibody-drug conjugate is datopotamab deruxtecan (DS-1062).

[0068] V. Therapeutic Uses of Anti-TROP2 ADCs. The antibody-drug conjugates of the present disclosure may be used to treat cancer, preferably breast cancer (including triple-negative breast cancer and hormone receptor (HR)-positive, HER2-negative breast cancer), lung cancer (including small cell lung cancer and non-small cell lung cancer), colorectal cancer (also called colon and rectal cancer, including colon and rectal cancer), gastric cancer (also called gastric adenocarcinoma), esophageal cancer, head and neck cancer (including salivary gland cancer and pharyngeal cancer), esophagogastric junction adenocarcinoma, biliary tract cancer (including cholangiocarcinoma), Paget's disease, pancreatic cancer, ovarian cancer, uterine carcinosarcoma, urothelial carcinoma, prostate cancer, bladder cancer, gastrointestinal stromal tumor, cervical cancer, squamous cell carcinoma, peritoneal cancer, liver cancer, hepatocellular carcinoma, uterine body carcinoma, kidney cancer, vulvar cancer, thyroid cancer, penile cancer, leukemia, malignant lymphoma, plasmacytoma, bone marrow ... It may be used to treat at least one cancer selected from the group consisting of myeloma, glioblastoma multiforme, osteosarcoma, sarcoma, and melanoma, and more preferably, it may be used to treat at least one cancer selected from the group consisting of breast cancer (preferably triple-negative breast cancer and hormone receptor (HR)-positive, HER2-negative breast cancer), lung cancer (preferably non-small cell lung cancer, non-small cell lung cancer with actionable genomic alterations, and non-small cell lung cancer without actionable genomic alterations, the actionable genomic alterations including EGFR, ALK, ROS1, NTRK, BRAF, RET, and MET exon 14 skipping), colorectal cancer, gastric cancer, pancreatic cancer, ovarian cancer, prostate cancer, and renal cancer. Furthermore, the antibody-drug conjugate of the present disclosure may be preferably used to treat cancers that lack homologous recombination (HR)-dependent DNA DSB repair activity or that do not lack homologous recombination (HR)-dependent DNA DSB repair activity.

[0069] The antibody-drug conjugates of the present disclosure may be used preferably in mammals, and more preferably in humans.

[0070] The anti-tumor effect of the antibody-drug conjugate of the present disclosure can be confirmed, for example, by preparing a model in which cancer cells are transplanted into a test animal and measuring the reduction in tumor volume and the life-prolonging effect due to the administration of the antibody-drug conjugate of the present disclosure.

[0071] Furthermore, the anti-tumor effect of the antibody-drug conjugates of the present disclosure may be confirmed in clinical trials using the Response Evaluation Criteria in Solid Tumor (RECIST) evaluation method, the WHO evaluation method, the Macdonald evaluation method, body weight measurement, and other methods; and may be determined by indicators such as complete response (CR), partial response (PR); progressive disease (PD), objective response rate (ORR), duration of response (DoR), progression-free survival rate (PFS), and overall survival rate (OS).

[0072] The antibody-drug conjugate of the present disclosure can delay, inhibit, or even kill cancer cells. These effects can enable cancer patients to be relieved from symptoms caused by cancer, or can achieve an improvement in the QOL of cancer patients, and can play a therapeutic role by prolonging the life of cancer patients. Even if the antibody-drug conjugate does not achieve the death of cancer cells, it can inhibit or control the growth of cancer cells, thereby achieving longer survival and at the same time improving the quality of life (QOL) of cancer patients.

[0073] The antibody-drug conjugate of the present invention is expected to exert a therapeutic effect when applied as a systemic treatment to patients and also when applied locally to cancer tissues.

[0074] In another aspect, the antibody-drug conjugate of the present disclosure is provided for use as an adjuvant in cancer treatment using ionizing radiation or other chemotherapeutic agents. For example, in the treatment of cancer, the treatment may include administering to a subject in need of treatment a therapeutically effective amount of the antibody-drug conjugate, either simultaneously or sequentially with ionizing radiation or other chemotherapeutic agents.

[0075] The antibody-drug conjugates of the present disclosure may be used as adjuvant chemotherapy in combination with surgery. The antibody-drug conjugates of the present disclosure may be administered before surgery to reduce tumor size (referred to as preoperative adjuvant chemotherapy or neoadjuvant therapy) or after surgery to prevent tumor recurrence (referred to as postoperative adjuvant chemotherapy or adjuvant therapy).

[0076] In a further embodiment, the antibody-drug conjugate of the present disclosure can be used for the treatment of cancers with defective homologous recombination (HR)-dependent DNA DSB repair activity. The HR-dependent DNA DSB repair pathway repairs double-strand breaks (DSBs) in DNA through a homologous mechanism and reforms continuous DNA helices (KK Khanna and SP Jackson, Nat. Genet. 27(3):247-254(2001)). Components of the HR-dependent DNA DSB repair pathway include ATM (NM_000051), RAD51 (NM_002875), RAD51L1 (NM_002877), RAD51C (NM_002876), RAD51L3 (NM_002878), DMC1 (NM_007068), XRCC2 (NM_005431), XRCC3 (NM_005432). , RAD52 (NM_002879), RAD54L (NM_003579), RAD54B (NM_012415), BRCA1 (NM_007295), BRCA2 (NM_000059), RAD50 (NM_005732), MRE11A (NM_005590) and NBS1 (NM_002485). Other proteins involved in the HR-dependent DNA DSB repair pathway include regulators such as EMSY (Hughes-Davies, et al., Cell, 115, pp523-535). HR components are also described in Wood, et al., Science, 291, 1284-1289 (2001). A cancer with a defect in HR-dependent DNA DSB repair may comprise or consist of one or more cancer cells that have a reduced or eliminated ability to repair DNA DSBs through the repair pathway compared to normal cells, i.e., the activity of the HR-dependent DNA DSB repair pathway may be reduced or eliminated in one or more cancer cells. The activity of one or more components of the HR-dependent DNA DSB repair pathway may be eliminated in one or more cancer cells of an individual with a cancer with a defect in HR-dependent DNA DSB repair. Components of the HR-dependent DNA DSB repair pathway have been well characterized in the art (see, for example, Wood, et al., Science, 291, 1284-1289 (2001)) and include the components listed above.

[0077] In some embodiments, the cancer cells may have a BRCA1 and / or BRCA2 deficient phenotype, i.e., BRCA1 and / or BRCA2 activity is reduced or absent in the cancer cells. Cancer cells with this phenotype may be BRCA1 and / or BRCA2 deficient, i.e., BRCA1 and / or BRCA2 expression and / or activity may be reduced or absent in the cancer cells, for example, due to a mutation or polymorphism in the coding nucleic acid or an amplification, mutation or polymorphism in a gene encoding a regulator (e.g., the EMSY gene encoding the BRCA2 regulator) (Hughes-Davies, et al., Cell, 115, 523-535). BRCA1 and BRCA2 are known tumor suppressors whose wild-type alleles are frequently lost in tumors of heterozygous carriers (Jasin M., Oncogene, 21(58), 8981-93 (2002); Tutt, et al., Trends Mol Med., 8(12), 571-6, (2002)). The association of mutations in BRCA1 and / or BRCA2 with breast cancer has been well characterized in the art (Radice, PJ, Exp Clin Cancer Res., 21(3 Suppl), 9-12 (2002)). Amplification of the EMSY gene, which encodes a BRCA2 binding factor, is also known to be associated with breast and ovarian cancer. Carriers of mutations in BRCA1 and / or BRCA2 are also at increased risk of certain cancers, including breast, ovarian, pancreatic, prostate, hematological, gastrointestinal and lung cancers. In some embodiments, the individual is heterozygous for one or more variations (eg, mutations and polymorphisms) in BRCA1 and / or BRCA2, or regulators thereof.Detection of variations in BRCA1 and BRCA2 is well known in the art and is described, for example, in EP 699754, EP 705903, Neuhausen, SL and Ostrander, EA, Genet. Test, 1, 75-83 (1992); Chappnis, PO and Foulkes, WO, Cancer Treat Res, 107, 29-59 (2002); Janatova M., et al., Neoplasma, 50(4), 246-505 (2003); Jancarkova, N., Ceska Gynekol., 68{1), 11-6 (2003)). Determination of amplification of the BRCA2 binding factor EMSY is described, for example, in Hughes-Davies, et al., Cell, 115, 523-535).

[0078] Cancer-associated mutations and polymorphisms can be detected at the nucleic acid level, by detecting the presence of a variant nucleic acid sequence, or at the protein level, by detecting the presence of a variant (i.e., mutant or allelic variant) polypeptide.

[0079] The antibody-drug conjugate of the present disclosure may be administered as a pharmaceutical composition containing at least one pharma- ceutical suitable component. The pharma-ceutical suitable component may be appropriately selected and applied from formulation additives commonly used in the art, etc., depending on the dosage, administration concentration, etc., of the antibody-drug conjugate used in the present disclosure. For example, the antibody-drug conjugate used in the present disclosure may be administered as a pharmaceutical composition containing a buffer such as a histidine buffer, an excipient such as sucrose or trehalose, and a surfactant such as polysorbate 80 or 20. The pharmaceutical product containing the antibody-drug conjugate used in the present disclosure may be preferably used as an injection, more preferably used as an aqueous injection or a lyophilized injection, and even more preferably used as a lyophilized injection.

[0080] When the pharmaceutical product containing the anti-TROP2 antibody-drug conjugate used in the present disclosure is an aqueous injection, it can be preferably diluted with a suitable diluent and then given as an intravenous infusion.For the diluent, dextrose solution, saline solution, etc. can be exemplified, dextrose solution can be preferably exemplified, and 5% dextrose solution can be more preferably exemplified.When the pharmaceutical product of the present disclosure is a lyophilized injection, it can be preferably dissolved in water for injection, and then the required amount can be diluted with a suitable diluent and then given as an intravenous drip.For the diluent, dextrose solution, saline solution, etc. can be exemplified, dextrose solution can be preferably exemplified, and 5% dextrose solution can be more preferably exemplified.

[0081] Examples of routes of administration that may be used to administer the pharmaceutical products of the present disclosure include intravenous, intradermal, subcutaneous, intramuscular and intraperitoneal routes, preferably the intravenous route.

[0082] The anti-TROP2 antibody-drug conjugate used in the present disclosure may be administered to a human once at intervals of 1 to 180 days, preferably once a week, once every two weeks, once every three weeks, or once every four weeks, and even more preferably once every three weeks. The antibody-drug conjugate used in the present invention may be administered at a dose of about 0.001 to 100 mg / kg, preferably at a dose of 0.8 to 12.4 mg / kg. For example, the anti-TROP2 antibody-drug conjugate may be administered at a dose of 0.27 mg / kg, 0.5 mg / kg, 1.0 mg / kg, 2.0 mg / kg, 4.0 mg / kg, 6.0 mg / kg, or 8.0 mg / kg once every three weeks, preferably at a dose of 6.0 mg / kg once every three weeks.

[0083] VI. Methods for predicting patient response to anti-TROP2 antibody-drug conjugates. 13 is a flow chart of steps 11-17 of method 10 in which an analysis system analyzes digital images of tissue from a cancer patient to predict how the cancer patient is likely to respond to a treatment that includes an anti-TROP2 antibody-drug conjugate (ADC). In one embodiment, the ADC is datopotamab deruxtecan (DS-1062). In one embodiment, the method predicts the response to the ADC in a patient having a cancer selected from the group consisting of breast cancer, gastric cancer, colorectal cancer, lung cancer, esophageal cancer, head and neck cancer, esophagogastric junction cancer, biliary tract cancer, Paget's disease, pancreatic cancer, ovarian cancer, uterine carcinosarcoma, bladder cancer, prostate cancer, urothelial carcinoma, gastrointestinal stromal tumor, cervical cancer, squamous cell carcinoma, peritoneal cancer, liver cancer, hepatocellular carcinoma, endometrial cancer, kidney cancer, vulvar cancer, thyroid cancer, penile cancer, leukemia, malignant lymphoma, plasmacytoma, myeloma, glioblastoma multiforme, sarcoma, osteosarcoma, and melanoma. In one embodiment, the method predicts the response to ADC in a patient having a cancer selected from the group consisting of breast cancer, gastric cancer, colorectal cancer, non-small cell lung cancer, esophageal cancer, head and neck cancer, gastroesophageal junction adenocarcinoma, biliary tract cancer, Paget's disease, pancreatic cancer, ovarian cancer, uterine carcinosarcoma, bladder cancer, and prostate cancer. In one embodiment, the method predicts the response to ADC in a breast cancer patient. In another embodiment, the method predicts the ADC response in a gastric cancer patient. In yet another embodiment, the method predicts the ADC response in a lung cancer patient.

[0084] In a first step 11, high resolution digital images of tissue sections from cancer patients stained with one or more biomarkers or stains are obtained. To predict the efficacy of ADC therapy, a diagnostic antibody (e.g., a diagnostic biomarker) is used that has an attached dye that targets the same protein as that targeted by the ADC therapy. In one embodiment, the anti-TROP2 ADC to be scored comprises an anti-TROP2 antibody conjugated to a drug-linker via a thioether bond, the drug-linker having the following formula: [Formula 3] [ka] (wherein "antibody-" represents the position where the anti-TROP2 antibody is linked). In one embodiment, the anti-TROP2 ADC to be scored is datopotamab deruxtecan (DS-1062). Thus, in various embodiments, the diagnostic biomarker also targets the TROP2 protein.

[0085] In step 12, the pre-trained convolutional neural network processes digital images of cancer patient tissue stained with diagnostic antibodies linked to dyes such as 3,3'-diaminobenzidine (DAB). The staining intensity of the dye in the membrane of the cancer cell is determined based on the average staining intensity of the dye of all pixels associated with the corresponding segmented membrane object. Furthermore, the staining intensity of the dye at a single pixel is computed based on the red, green and blue components of the pixel. The result of the image analysis process is two posterior image layers that represent, for each pixel in the digital image, the probability that the pixel belongs to a cell nucleus and the probability that the pixel belongs to a cell membrane.

[0086] In step 13, individual cancer cells are detected based on heuristic image analysis of the nucleus and the posterior layer of the membrane. Cancer cell objects including cell membrane objects are generated.

[0087] In step 14, each cancer cell is identified as either optical density positive or optical density negative based on the amount of DAB in the cell membrane. The amount of DAB is determined by the staining intensity of each membrane based on the average (mean) optical density of the brown diaminobenzidine (DAB) signal in all the pixels of the membrane. Each cancer cell is identified as either (i) optical density positive if the average optical density of the cancer cells is equal to or greater than the optical density threshold, or (ii) optical density negative if the average optical density of the cancer cells is less than the optical density threshold. In one embodiment, the optical density threshold is in the range of 23-27 on a scale of maximum optical density 255. For example, the optical density threshold is 25. The maximum optical density that can be represented in 8-bit RGB data is 255. The maximum optical density observed with DAB staining of the membrane was about 220.

[0088] In step 15, a binary proximity score is generated for the digital image of the tissue sample equal to the percentage of cancer cells in the digital image that are either optical density positive or optical density negative, but within a predetermined distance from optical density positive cancer cells. In one embodiment, the predetermined distance is 50 microns. In another embodiment, the predetermined distance is 25 microns.

[0089] In step 16, if the proximity score exceeds a predetermined percentage threshold, the cancer patient is identified as a patient likely to benefit from administration of an anti-TROP2 ADC. In one embodiment, the predetermined percentage threshold is in the range of 95% to 100%. For example, the predetermined percentage threshold can be 99.975%. In some embodiments, a patient is called QCS positive if at least 95% of the cancer cells in the digital image are either optical density positive or optical density negative, but are located within a predetermined distance from optical density positive cancer cells, i.e., if the proximity score is at least 95%. In some embodiments, a patient is QCS positive if the proximity score is at least 98%. In some embodiments, a patient is QCS positive if the proximity score is at least 99%. In some embodiments, a patient is QCS positive if the proximity score is at least 99.9%.

[0090] The threshold optical density used in step 14, the predefined distance used in step 15, and the predefined percentage threshold used in step 16 are optimized using a training cohort of patients with known response to ADC therapy. Optimization is performed by finding the lowest log-rank p-value using Kaplan-Meier analysis to stratify patients into groups with tumor shrinkage and tumor growth. Progression-free survival (PFS) is mapped onto the Kaplan-Meier curve, however, PFS does not relate to the patient's survival time, but rather to the time without tumor growth. The patient score indicates how the cancer patient will respond to therapy with anti-TROP ADC.

[0091] In step 17, therapy including an anti-TROP2 ADC is recommended for patients who score positive if the score is greater than a predetermined percentage threshold.

[0092] VII. EXAMPLES OF PREDICTION AND SCORING METHODS A. Image analysis of stained tissue The method of FIG. 13 will now be described with reference to a specific image of stained cancer tissue.

[0093] In step 11, the tissue sample is immunohistochemically stained using a dye linked to a diagnostic antibody that binds to an associated protein on cancer cells in the tissue sample. Figure 14 (top left image) is a digital image 18 of a portion of the stained tissue obtained in step 11. Image 18 shows tissue from a cancer patient that has been immunohistochemically stained with an anti-TROP2 diagnostic antibody linked to a dye. In this example, the diagnostic antibody is an anti-TROP2 generating clone reformatted as a rabbit anti-human TROP2 IgG1 clone. IgG1 indicates the isotype of the anti-TROP2 antibody. The anti-TROP2 antibody binds to the membrane protein TROP2 such that 3,3'-diaminobenzidine (DAB) staining indicates the location of the protein TROP2 in the tissue sample.

[0094] In step 12, image analysis is performed on the digital image 18 to generate a posterior image layer of cancer cell nuclei and membranes using a convolutional neural network. Image analysis is used to detect cancer cells and their components such as nuclei, membranes and cytoplasm. Figure 14 shows the image analysis process of step 12. The convolutional neural network generates a posterior layer (gray value image) that indicates for each pixel of the digital image 18 the probability that each pixel belongs to either the nucleus (Figure 14 top right image) or membrane (Figure 14 bottom left image) of a cell. High probabilities are shown in black and low probabilities in white. The top right image also shows a nuclear center that is first identified to generate a cell membrane object by growing outward from the nuclear center until it meets the growth from another nuclear center.

[0095] In one embodiment, the convolutional neural network includes a series of convolutional layers from the input image 18 directed to a bottleneck layer with a very small spatial size (1-16 pixels), and a series of deconvolutional layers directed to backward layers with the same size as the input image 18. This network architecture is called U-Net. Training of the convolutional neural network weights is performed by generating manual annotation layers of nuclei and membranes in multiple training images, and then adjusting the network weights by an optimization algorithm so that the generated backward layers are most similar to the manually generated annotation layers.

[0096] In another embodiment, annotation layers of nuclei and membranes are automatically generated in multiple training images and manually corrected. Epithelial regions and nuclei centers are manually annotated as regions and points, respectively. For each training image, membrane segmentation is automatically generated by applying a region-growing-like algorithm (e.g., watershed segmentation) seeded by annotation nuclei centers and constrained by the extent of annotation epithelial regions. Given a training image, nuclei segmentation is automatically generated by applying a blob detection algorithm (e.g., by the Maximum Stable Extremum Region MSER algorithm) and selecting as nuclei only those detected blobs that contain annotation nuclei centers. The automatically generated membrane and nuclei segmentations are visually reviewed and manually corrected if necessary. The correction step includes one of the following methods: rejecting incorrectly segmented membranes or nuclei, explicitly accepting correctly annotated membranes or nuclei, or refining the shape of the membrane or nucleus. For each image with annotation membranes or nuclei, an annotation layer is created. In one embodiment, each pixel in the annotation layer is assigned a "1" if it belongs to an annotation object (membrane or nucleus), and a "0" if it does not. In another embodiment, the pixels in the annotation layer represent the distance to the nearest annotation object. The network weights are adjusted by an optimization algorithm to make the generated posterior layers most similar to the automatically generated membrane and nucleus annotation layers.

[0097] FIG. 15 illustrates step 13 where individual cancer cell objects, each containing a cell membrane and cytoplasm, are detected. The heuristic image analysis process uses watershed segmentation to segment cell nuclei using a nuclear posterior layer generated by a convolutional neural network. The segmentation produces nuclear objects. Each nuclear object is assigned a unique identifier (UID). Individually identified nuclei are shown as dark objects in FIG. 15 (bottom right image). Detected nuclei are also displayed as overlays on the input image 18 (top left image) and the posterior layers of the nuclei (top right image) and membrane (bottom left image).

[0098] In one embodiment, the watershed segmentation involves thresholding the nuclear posterior layer at a predefined first size threshold. All single connected pixels above the first size threshold are considered to belong to a nuclear object. Nuclear objects with an area less than 16μm^2 are discarded. Each nuclear object is assigned a UID. In a subsequent step, the nuclear object is grown towards a smaller nuclear posterior where the added nuclear posterior pixels must be greater than a second predefined threshold.

[0099] Figure 16 shows the detection and segmentation of membrane objects, which are segmented by growing a region of boundary pixels of a detected cell outside of a potential membrane layer and to a threshold behind a given membrane layer. Thicker boundary regions become membrane objects. Each membrane object is assigned a UID that is the same as the UID of the associated nucleus object.

[0100] The space between the membrane and the nucleus is assigned to the cytoplasm using the UID of the nucleus. For each membrane (see FIG. 16, top left image) and cytoplasm (see FIG. 16, top left image), the average optical density of the DAB staining is exported to a file on the hard drive along with the UID. For each cell (defined to include nucleus, cytoplasm and membrane), the location of the cell's centroid (x,y) within the slide is also exported. The files may reside on a hard disk, a solid state disk, or a portion of dedicated RAM within the computer system.

[0101] FIG. 17 shows the results of image analysis in an image analysis software environment. FIG. 17 (top left image) shows the segmentation of nuclear and membrane objects as an overlay on a digital image of stained tissue. FIG. 17 (bottom left image) shows the segmentation of nuclear objects as an overlay on an optical density display of the digital image. Dark optical density pixels are associated with large amounts of DAB and light optical density pixels are associated with small amounts of DAB. The DAB optical density of each image pixel is computed from the red-green-blue representation of the image pixel by transforming the red-green-blue color space such that the brown DAB component is a separate color and taking the logarithm of that brown color component. FIG. 17 (top right image) shows the image analysis script used to generate the segmented image. FIG. 17 (bottom right image) shows the exported measurements for all cell membrane and cytoplasmic objects in image 18.

[0102] B. Calculating predictive validity scores A spatial proximity score is determined for the tissue sample shown in the digital image 18 based on the optical density of the DAB staining in the membrane objects and optionally cytoplasmic objects. The spatial proximity score is also based on the staining intensity of the DAB dye in the membrane objects and optionally cytoplasmic objects of neighboring cancer cells that are closer than a predetermined distance to the cancer cell for which the single cell ADC score is computed. There are two types of spatial proximity scores: binary spatial proximity score (bSPS) and continuous spatial proximity score (cSPS). The spatial proximity score estimates the effect of bystander activity of the ADC drug. Bystander activity is characterized by the local toxicity of the ADC payload released from cells that internalize the ADC drug. The effective range of the local toxicity is expressed as a predetermined distance parameter in the binary and continuous spatial proximity scores.

[0103] 18 shows an example of how a binary spatial proximity score (bSPS) is determined. The binary spatial proximity score is a percentage score equal to the sum of (i) the number of cancer cells in digital image 18 that have a staining intensity equal to or greater than the optical density threshold plus (ii) the number of cancer cells in the digital image that have a staining intensity less than the optical density threshold but are located within a predetermined distance of at least one cancer cell that has a staining intensity equal to or greater than the optical density threshold, divided by the total number of cancer cells in digital image 18.

[0104] FIG. 18 shows ten exemplary cancer cells (also called tumor cells) with various optical density staining from the image analysis of steps 12-13. The steps of heuristic image analysis shown in FIG. 14-16 are used to obtain a segmentation into image objects including cancer cell objects, cell membrane objects and cytoplasmic objects. Circles with solid lines around them represent tumor cells whose optical density of the brown DAB staining (corresponding to the amount of the target protein TROP2) is equal to or greater than a predefined optical density threshold. In this example, the optical density threshold is 12 out of a maximum scale of optical density of 255. Three cancer cells 19-21 in FIG. 18 are classified as optical density positive. Circles with dashed circumference represent tumor cells whose optical density of the brown DAB staining is less than the predefined optical density threshold. Seven cancer cells 22-28 are classified as optical density negative. Darker grey circles with dashed circumference represent optical density negative cancer cells located within a predefined distance from at least one optical density positive cancer cell. Two cancer cells 22-23 are optical density negative but are also located within a predetermined distance from an optical density positive cancer cell, in this example the predetermined distance is 25 microns.

[0105] In this example, the binary spatial proximity score is equal to a percentage score of 50%, which is calculated as the sum of the three optical density positive cells and two optical density negative cells that are within a given distance from an optical density positive cell, as shown in FIG. 18, divided by the total of 10 cancer cells ([3+2] / 10=0.5).

[0106] Within the schematic image of Figure 18, three cancer cells 19-21 express high amounts of the target protein TROP2 and are very likely to be killed by the ADC payload (e.g., cytotoxin) that enters the cell linked to the ADC antibody (effect 1 of Figure 19). Two cancer cells 22-23 do not express sufficient amounts of the target protein TROP2 to be directly killed by the anti-TROP2 ADC. However, due to the proximity of at least one of the optical density positive cells 19-21, the toxic payload released from cells 19-21 also kills cancer cells 22-23 (effect 3 of Figure 19). The remaining optical density negative cancer cells 24-28 remain active and may be the origin of drug resistance mechanisms that may ultimately cause the patient's death.

[0107] FIG. 19 shows the mechanism by which anti-TROP2 ADC therapy kills cancer cells. In the first step, the ADC antibody binds to the target protein TROP2 and inhibits the natural function of the target protein, which can lead to cell death. In the second step, the payload (e.g., a type I topoisomerase inhibitor) is internalized into the cell and kills the cell due to the toxicity of the payload. This uptake of the payload also depends on the amount of the target protein on the membrane and the difference in the amount of the target protein on the membrane and in the cytoplasm. After uptake, the payload can be released from the cell into the surrounding tissue. In the third step, the payload can enter nearby cells and kill them. The spatial distribution of the payload in the tissue spreads by passive diffusion.

[0108] Conventional IHC scoring reflects both the effect of target protein inhibition caused by ADC binding and the effect of cytotoxic payload that enters cancer cells together with ADC antibody.Therefore, conventional scoring for ADC therapy does not reflect the importance of target protein presence in cytoplasm and the effect of cytotoxic payload that diffuses into tissue after being released from the first dead cancer cell.In comparison, novel predictive spatial proximity score measures the effect of release of cytotoxic payload on neighboring cancer cells.

[0109] 20-22 show an example of how the continuous spatial proximity score is determined. Although the continuous spatial proximity score applies a predetermined distance, optical density negative cancer cells still receive some weighting even though they are located farther than the predetermined distance from optical density positive cancer cells. The weighting of optical density negative cancer cells located beyond the predetermined distance can be either Gaussian or linear. The continuous spatial proximity score formula shown in FIG. 23 applies a Gaussian weighting. Furthermore, this embodiment of the continuous spatial proximity score also takes into account staining in the cytoplasm. Based on the optical density of the DAB staining within the membrane objects and optionally the cytoplasmic objects, a continuous spatial proximity score is computed for the tissue sample shown in the digital image 18.

[0110] FIG. 20 shows an exemplary quantification result of the optical density of the staining from the image analysis of steps 12-13 in the schematic diagram using the grey values ​​of the membrane and cytoplasmic pixels. The steps of the heuristic image analysis shown in FIG. 14-16 are used to obtain the exemplary segmentation in FIG. 20 into the cell nucleus, cell membrane and cell cytoplasm. The light grey values ​​in FIG. 20 are associated with high DAB optical density and therefore with high amounts of the protein targeted by the diagnostic antibody. The dark grey values ​​are associated with low DAB optical density. The lighter pixels correspond to higher DAB optical density.

[0111] FIG. 21 lists exemplary quantitative staining amounts on the membrane and in the cytoplasm of the images of FIG. 20, which are partially reproduced in FIG. 21. The optical densities of the brown DAB signal from the membrane of the first, second and third cells are 0.949, 0.369 and 0.498, respectively. In this example, the optical densities are expressed as a percentage of the maximum staining of 255. The optical densities of the brown DAB signal from the cytoplasm of the first, second and third cells are 0.796, 0.533 and 0.369, respectively. In the schematic image of FIG. 21, the first cancer cell 29 expresses a large amount of the target protein TROP2 and is very likely to be killed by the ADC payload entering the cell linked to the ADC antibody (effect 1 of FIG. 19). The second cancer cell 30 and the third cancer cell 31 do not express sufficient amounts of the target protein TROP to be directly killed by the anti-TROP2 ADC. However, due to the proximity of the second cancer cell 30 to the first cancer cell 29, the toxic payload released from the first cancer cell also kills the second cancer cell 30 (effect 3 in FIG. 19). The third cancer cell 31 remains active and may be the origin of drug resistance mechanisms that may ultimately cause the patient's death.

[0112] In step 14 of the method of FIG. 13, the average optical density of each cancer cell is determined. Then, in a modification of method 10 for continuous scores, a single cell spatial proximity score is determined. FIG. 22 shows the calculation of the single cell spatial proximity score for each of the three cells shown in FIG. 21 based on cell separation reflecting the uptake of the ADC payload into adjacent cells, incorporating an exponential weighting factor. The single cell score can be calculated based on the formula shown in FIG. 22. The predetermined distance used in the formula is 50 microns. The optical densities listed in FIG. 21 for the DAB signals from the cell membrane (0.949, 0.369, and 0.498) and cytoplasm (0.796, 0.533, and 0.369) are the inputs to the calculation shown in FIG. 22. The first, second, and third cells 29, 30, 31 have single cell scores of 0.145, 0.012, and 0.064, respectively.

[0113] Thus, the continuous spatial proximity score incorporates a measure of the amount of target protein on the cell membrane using DAB optical density and, optionally, an estimate of ADC payload (e.g., cytotoxin) uptake. As shown in FIG. 21, the uptake of the ADC payload (e.g., cytotoxin) for a first cell depends on both the amount of dye in its membrane and in its cytoplasm, and the amount of dye in the membrane and in the cytoplasm of a second cell in the vicinity of the first cell. More specifically, the vicinity may be a disk having a predetermined radius around the first cancer cell. In one embodiment, the continuous spatial proximity score for the first cancer cell is determined by a distance-weighted sum of the powers of the DAB optical density of the membrane and cytoplasmic objects whose associated cancer cell center is closer to the first cancer cell center than a predetermined distance. In one embodiment, the predetermined distance is 50 μm, as used in the calculation of FIG. 22. In another embodiment, the distance is 20 μm. In yet another embodiment, the distance weighting includes computing an exponential function of the adjusted negative Euclidean distance from the center of the first cancer cell to the center of the other cancer cell in the sum. In another embodiment, the powers in the sum are restricted to 0, 1, and 2 (constant, linear term, squared).

[0114] FIG. 23 shows one embodiment of a formula for calculating a single-cell ADC score in the form of a continuous spatial proximity score. kl is the distance from cell j to cell i |r j -r i Depends on |ODM j is the DAB optical density of the membrane of cell j, and ODC j is the DAB optical density of the cytoplasm of cell j. The constants A_ij, r_norm and d are the same for all types of cancer. However, the score threshold for determining whether a patient is eligible for ADC therapy is not the same for different types of cancer.

[0115] The formula of Figure 23 for the continuous spatial proximity score applies a Gaussian weighting to optically density-negative cancer cells located beyond a predetermined distance from the cell for which the single-cell score is calculated. In another embodiment, a linear weighting is given to optically density-negative cancer cells located beyond a predetermined distance. The formula for the single-cell continuous spatial proximity score applying the linear weighting is for the cell i with respect to |r j -r i | < d{membrane OD j x(1 - |r j -r i | / d)} sum by j

[0116] C. Verification of the binary spatial proximity score. Various QCS prediction response scores were verified based on the patient trial (J101 NCT03401385), a Phase I clinical trial using datopotamab deruxtecan (DS-1062). The dataset of the J101 patient trial includes stained tissue images (using Abcam antibody clone EPR 20043) and the treatment response rates of patients with multiple cancer types. The initial verification of the prediction response score was based on data from 115 patients with non-small cell lung cancer (NSCLC). The response score was trained using pathologist annotations, and the performance of the scoring method was verified with unseen data to ensure its generalization and robustness. The response score was blindly applied to the J101 data. The optical density OD (level of brown DAB staining intensity) was computer-processed on the detected membrane to derive the characteristics of the response score related to progression-free survival (PFS) prediction. The characteristics of the response score were selected to maximize the time without tumor growth (or tumor shrinkage).

[0117] The method of Figure 13 includes a first response score feature of the novel QCS method, which is the binary spatial proximity score. The time free of tumor growth (progression-free survival time) was maximized by a parameter of membrane optical density cutoff of 25 and a predefined distance of 50 microns. Thus, a cohort of 115 NSCLC patients was used to validate the binary spatial proximity score, denoted as bystander_membrane(mean OD)_binary_r50_cut25.

[0118] In step 15 of method 13, a binary spatial proximity score is generated for the tissue sample based on the percentage of cancer cells in the digital image that are optical density (OD) positive or optical density negative but within a predetermined distance from an optical density positive cancer cell. In this embodiment of the QCS method, the predetermined distance is 25 microns. Each cancer cell is identified as optical density positive if its mean optical density is equal to or greater than the optical density threshold, and optical density negative if its mean optical density is less than the optical density threshold. In this embodiment, the optical density threshold is 25 on a scale with a maximum optical density of 255.

[0119] In step 15 of method 13, if the binary spatial proximity score exceeds a predefined threshold, the cancer patient is identified as likely to benefit from administration of the ADC (bSPS positive). In this embodiment, the predefined threshold is 99.97%. The predefined distance, optical density threshold, and predefined threshold were correlated with the response of 115 patients in the training cohort.

[0120] FIG. 24 shows how the predefined distances, optical density thresholds, and predefined thresholds correlated with the response of tissue samples of cancer patients treated with anti-TROP2 ADCs. FIG. 24 is a scatter plot showing the correlation between the actual outcomes of 115 NSCLC patients in the J101 study. The actual responses of the patients are shown as progressive disease PD (circles), stable disease SD (triangles), partial response PR (squares), and non-evaluable NE (diamonds). The distribution of the 115 patients among the response groups was PD=21, SD=60, PR=20, and NE=14. Patients whose tumors shrank by less than 30% and grew by less than 30% are considered to have stable disease. Partial response PR is defined as tumor shrinkage of 30% to 100%, and complete response CR is 100% tumor shrinkage and elimination of the tumor. In the scatter plot of FIG. 24, complete response is classified as partial response. Different dosages of ADC were administered to 115 patients (4 mg / kg, 6 mg / kg and 8 mg / kg), but no correlation between different dosages and different responses was observed. Figure 24 shows the relationship between tumor growth rate (%) and predicted response score: Bystander_Membrane(mean OD)_Binary_r50_Cut25 (proportion of cancer cells that are either (i) OD positive or (ii) OD negative and within 50 microns of OD positive cells). X-axis values ​​greater than 0 mean that the tumor increased in size during the observation period, and values ​​less than 0 mean that the tumor size decreased during the observation period.

[0121] The best fit line in Figure 24 shows that there was a negative correlation between the predicted response score bystander_membrane(mean OD)_binary_r50_cut25 and the change in tumor size after treatment. The Spearman correlation rho for the best fit line is R=-0.28. Thus, there was a greater reduction in tumor size associated with higher predicted response scores.

[0122] Figure 25 is a graph of Kaplan-Meier curves of progression-free survival for two groups of 115 NSCLC patients from the J101 study, stratified using binary spatial proximity scores. Using the method of Figure 13, the 115 patients were divided into QCS-positive and QCS-negative patients. The quality of stratification between the two Kaplan-Meier curves achieved using the QCS feature bystander_membrane(mean OD)_binary_r50_cut25 is shown by a log-rank p-value of 0.00047. There were 88 bSPS-positive (QCS-positive) and 27 bSPS-negative (QCS-negative) patients. The group of 88 bSPS-positive patients showed an objective response rate (ORR) of 14.8% and a mean progression-free survival (mPFS) of 5.15 months. The group of 27 bSPS-negative patients showed an objective response rate (ORR) of 18.1% and a mean progression-free survival of 1.44 months. The top Kaplan-Meier curve shows a group of 88 patients with better survival outcomes, corresponding to patients in whom at least 99.97% of cells either (i) showed an optical density of at least 25 TROP2 staining or (ii) were located within 50 microns of cells showing a minimum staining of 25. Thus, the scoring method in Figure 13 identified 88 of 151 J101 lung cancer patients as likely to benefit from anti-TROP2 ADC therapy.

[0123] FIG. 26 shows spider plots of 88 bSPS-positive and 27 bSPS-negative patients of the Kaplan-Meier curves of FIG. 25. Each dot of the spider plot indicates the percentage of the patient's tumor that has shrunk or grown after the indicated time period. The spider plots of FIG. 26 show time in days, whereas progression-free survival is shown in months in the Kaplan-Meier curves of FIG. 25. For example, the last point of almost 25 months on the upper Kaplan-Meier curve of FIG. 25 corresponds to the bottom right point of about 750 days in the lower spider plot of the bSPS-positive patients. The weight of the distribution of dots in the lower spider plot of the bSPS-positive patients is below the 0% line, which indicates the weight of patients whose tumors have shrunk. On the other hand, the weight of the distribution of dots in the upper spider plot of the bSPS-negative patients is above the 0% line, which indicates the weight of patients whose tumors have grown.

[0124] In Figures 25-26, patients whose tumors shrank by 30% or more are classified as having a positive response to the anti-TROP2 ADC (including both partial response (PR) and complete response (CR) patients). The upper spider plot of bSPS-negative patients shows that only 4 of the 27 bSPS-negative patients had a positive response to the ADC. (The dots below the -30% line, observed only once after 50 days, were considered to be non-evaluable NE and therefore not a positive response.) In the lower spider plot of bSPS-positive patients, there are 16 dots (other than NE dots) below the -30% line that correspond to patients who had a positive response to the ADC.

[0125] FIG. 27 is a waterfall bar graph showing clinical responses of 105 of 115 NSCLC patients in the J101 trial ordered from greatest tumor growth to greatest tumor shrinkage. Tumor growth / shrinkage results are not shown for the 10 non-evaluable (NE) patients. Solid bars indicate bSPS-positive patients with a binary spatial proximity score of 99.975% or greater. Bars with diagonal hatching indicate bSPS-negative patients with a binary spatial proximity score of less than 99.975%. The bar graph shows that the QCS predicted response score bystander_membrane(mean OD)_binary_r50_cut25 identifies the majority of patients whose tumors shrank after ADC treatment as bSPS-positive and the majority of patients whose tumors grew after ADC treatment as bSPS-negative.

[0126] D. Validation of normalized membrane OD scores A variation of the novel method of FIG. 13 was also validated based on a patient study (J101 NCT03401385) of 115 NSCLC patients with the anti-TROP2 ADC datopotamab deruxtecan (DS-1062). FIG. 28 is a flow chart of steps 33-40 of method 32 for predicting a cancer patient's response to an anti-TROP2 ADC. Method 32 is another embodiment of method 10 of FIG. 13, which predicts the efficacy of a therapy including an anti-TROP2 ADC based on the normalized membrane optical density of each cancer cell. The normalized membrane optical density is equal to the mean optical density of the membrane staining divided by the sum of the mean optical density of the membrane staining plus the mean optical density of the cytoplasmic staining.

[0127] In step 33, a digital image 18 of a tissue section from a cancer patient stained with a dye linked to a diagnostic antibody that targets the same protein targeted by the ADC therapy, in this case the TROP2 protein, is obtained. In step 34, image resolution is performed on the digital image to generate image objects of the cancer cells, cell membranes and cytoplasmic objects. In step 35, for each cancer cell, the average optical density (OD) of the dye staining in the cell membrane is measured. In step 36, for each cancer cell, the average optical density (OD) of the dye staining in the cytoplasm is determined.

[0128] In step 37, a normalized membrane optical density of each cancer cell in the digital image is computed. The normalized membrane optical density is equal to the average optical density of the membrane stain divided by the sum of the average optical density of the membrane stain plus the average optical density of the cytoplasmic stain. In step 38, each cancer cell is identified as either (i) normalized membrane positive if the normalized membrane optical density of the cancer cell is equal to or less than the normalized membrane threshold, or (ii) normalized membrane negative if the normalized membrane optical density of the cancer cell is greater than the normalized membrane threshold. In step 39, a predicted response score is generated for the tissue sample based on the percentage of cancer cells in the digital image identified as normalized membrane positive. The predicted response score is positive if the percentage of cancer cells that are normalized membrane positive is equal to or greater than the percentage threshold, and is negative if the percentage of cancer cells that are normalized membrane positive is less than the percentage threshold. In step 40, if the predicted response score is positive, the cancer patient is identified as a patient likely to benefit from administration of an anti-TROP2 ADC.

[0129] The normalized membrane threshold and percentage threshold correlate with the response of a cohort of 115 training patients treated with TROP2 ADC. These parameters are determined by performing a progression-free survival (PFS) analysis on clinical trial data. The time free of tumor growth (progression-free survival) was maximized by a normalized membrane threshold (OD / OD) parameter of 0.486 and a percentage threshold of 5%. Thus, a cohort of 115 NSCLC patients was used to validate the predictive response score, shown as membrane (mean OD) / (membrane (mean OD)+cytoplasm (mean OD))_q5.

[0130] FIG. 29 shows how the normalized membrane threshold and percentage threshold correlated with the response of tissue samples of cancer patients treated with anti-TROP2 ADC. FIG. 29 is a scatter plot showing the correlation between the actual outcomes of 115 NSCLC patients in the J101 study. The actual responses of the patients are shown as progressive disease PD (circles), stable disease SD (triangles), partial response PR (squares), and non-evaluable NE (diamonds). The distribution of the 115 patients between the response groups was PD=21, SD=60, PR=20, and NE=14. Patients whose tumors both shrank by less than 30% and grew by less than 30% are classified as having stable disease SD, while patients whose tumors shrank between 30% and 100% are considered to show partial response PR. In the scatter plot of FIG. 29, patients who have a complete response CR due to disappearance of the tumor (100% shrinkage) are also classified as having a partial response PR.

[0131] FIG. 29 shows the relationship between tumor growth rate (%) and predicted response score: membrane(mean OD) / (membrane(mean OD)+cytoplasm(mean OD))_q5 (normalized membrane OD). Y-axis values ​​greater than 0 mean that the tumor increased in size during observation, and values ​​less than 0 mean that the tumor size decreased during observation. The best-fit line in FIG. 29 shows that there was a positive correlation between the predicted response score membrane(mean OD) / (membrane(mean OD)+cytoplasm(mean OD))_q5 and the change in tumor size after treatment. The Spearman correlation rho of the best-fit line is R=0.14. Thus, there was a greater decrease in tumor size associated with a lower predicted response score.

[0132] Figure 30 is a Kaplan-Meier graph of progression-free survival for 115 NSCLC patients from the J101 study divided into 84 QCS-positive and 31 QCS-negative patients using method 32 from Figure 28. The quality of stratification between the two Kaplan-Meier curves achieved using the QCS feature membrane(mean OD) / (membrane(mean OD)+cytoplasm(mean OD))_q5 is shown by a log-rank p-value of 0.00054. The group of 84 QCS-positive patients showed an objective response rate ORR of 22.6% and a mean progression-free survival (mPFS) of 5.45 months. The group of 31 QCS-negative patients showed an objective response rate of 3.2% and a mean progression-free survival of 2.85 months. The upper Kaplan-Meier curve shows the group of 84 patients with a better survival outcome, which corresponds to patients in whom at least 5% of the cancer cells showed a normalized membrane optical density equal to or less than 0.486. Thus, scoring method 32 in Figure 28 identified 84 of 151 J101 lung cancer patients as likely to benefit from anti-TROP2 ADC therapy.

[0133] FIG. 31 shows spider plots of 84 QCS-positive and 31 QCS-negative patients of the Kaplan-Meier curves of FIG. 30. Each dot of the spider plot indicates the percentage of the patient's tumor shrinking or growing after the indicated time period. The spider plots of FIG. 31 show time in days, while progression-free survival is shown in months in the Kaplan-Meier curves of FIG. 30. For example, the last point of almost 25 months on the upper Kaplan-Meier curve of FIG. 30 corresponds to the bottom right point of about 750 days in the lower spider plot of the QCS-positive patients. The weight of the distribution of dots in the lower spider plot for the QCS-positive patients is below the line for 0% tumor growth, indicating that method 32 identified patients whose tumors mainly shrank. On the other hand, the weight of the distribution of dots in the upper spider plot for the QCS-negative patients is above the 0% line, indicating that method 32 identified patients whose tumors mainly grew.

[0134] FIG. 32 is a waterfall bar graph showing clinical responses of 105 of 115 NSCLC patients in the J101 trial ordered from greatest tumor growth to greatest tumor shrinkage. Tumor growth / shrinkage results are not shown for the 10 non-evaluable (NE) patients. Solid bars indicate QCS-positive patients who had a response score for the normalized membrane OD feature of 0.486 or less. Bars with diagonal hatching indicate QCS-negative patients who had a response score for the normalized membrane OD feature of greater than 0.486. The bar graph shows that the response score for the QCS feature membrane(mean OD) / (membrane(mean OD)+cytoplasm(mean OD))_q5 identifies the majority of patients whose tumors shrank after ADC treatment as QCS-positive and the majority of patients whose tumors grew after ADC treatment as QCS-negative.

[0135] E. Validation of combined bSPS and normalized membrane OD scores Figure 33 is a flow chart of steps 42-50 of an additional method 41 for predicting a cancer patient's response to an anti-TROP2 ADC. Method 41 predicts the efficacy of a therapy that includes an anti-TROP2 ADC based on a combination of binary spatial proximity scores and normalized membrane optical density.

[0136] In step 42, a digital image 18 of a tissue section from a cancer patient is obtained that is stained using a dye linked to a diagnostic antibody that targets the same protein targeted by the ADC therapy, in this case the TROP2 protein. In step 43, image solving is performed on the digital image to generate image objects of the cancer cells, cell membranes and cytoplasmic objects. In step 44, for each cancer cell, the average optical density (OD) of the membrane staining and the average OD of the cytoplasmic staining are determined. In step 45, a normalized membrane optical density is computed for each cancer cell in the digital image 18, which is equal to the average OD of the membrane staining divided by the sum of the average OD of the membrane staining + the average OD of the cytoplasmic staining. In step 46, each cancer cell is identified as either (i) normalized membrane positive if the normalized membrane optical density of the cancer cell is equal to or less than the normalized membrane threshold, or (ii) normalized membrane negative if the normalized membrane optical density of the cancer cell is greater than the normalized membrane threshold. In step 47, the tissue sample is identified as normalized membrane positive if the percentage of cancer cells that are normalized positive is equal to or greater than the normalized percentage threshold, and is identified as normalized membrane negative if the percentage of cancer cells that are normalized positive is less than the normalized percentage threshold.

[0137] In step 48, each cancer cell is identified as optical density positive if the mean OD of the membrane staining is equal to or greater than an optical density threshold, or optical density negative if the mean OD of the membrane staining is less than an optical density threshold. In step 49, a tissue sample is identified as proximity positive if the total percentage of cancer cells that are either optical density positive or optical density negative but within a predetermined distance from an optical density positive cancer cell exceeds a proximity percentage threshold. In step 50, if a tissue sample is both normalized membrane positive and proximity positive, it is identified as a cancer patient that is likely to benefit from administration of an anti-TROP2 ADC.

[0138] The optimal parameters of the QCS score were determined by performing a progression-free survival (PFS) analysis on clinical trial data. The normalized membrane threshold, normalized percentage threshold, optical density threshold and proximity percentage threshold were correlated with clinical response in a cohort of 115 training patients treated with TROP2 ADCs. Therefore, a cohort of 115 NSCLC patients was used to validate the predicted response score from the combined QCS features membrane(mean OD) / (membrane(mean OD)+cytoplasm(mean OD))_q5 and bystander_membrane(mean OD)_binary_r50_cut25.

[0139] Figure 34 is a Kaplan-Meier graph of progression-free survival for 115 NSCLC patients from a clinical trial divided into 80 QCS-positive and 35 QCS-negative patients using method 41 from Figure 33. The quality of stratification between the two Kaplan-Meier curves achieved using a combination of bSPS and normalized membrane OD is shown by a log-rank p-value of 0.00011. The group of 80 QCS-positive patients showed an objective response rate (ORR) of 23.7% and a median progression-free survival (mPFS) of 5.45 months. The group of 35 QCS-negative patients showed an objective response rate (ORR) of 0.03% and a median progression-free survival (mPFS) of 2.85 months. The upper Kaplan-Meier curve shows a group of 80 patients with better survival outcomes, which correspond to patients in whom (i) at least 5% of their cancer cells showed a normalized membrane optical density below 0.4864 and (ii) at least 92% of the cells showed an optical density of at least 25 membrane staining or were located within 50 microns of cells showing a minimum membrane staining of 25. Thus, the scoring method 41 in Figure 33 identified 80 of 151 J101 lung cancer patients as likely to benefit from anti-TROP2 ADC therapy.

[0140] While the scoring method 41 in FIG. 33 identified only 80 patients for ADC therapy, the bSPS scoring method 10 in FIG. 13 identified 88 of the 151 J101 patients as likely to benefit from ADC therapy. This indicates that the combined bSPS and normalized membrane OD QCS features are more selective than the individual features and identify patients more likely to benefit from ADC therapy. Note that the proximity percentage threshold of at least 92% used in the combined QCS features is more inclusive than the predetermined threshold of at least 99.97% used in the individual normalized membrane OD features, and yet 35 QCS-negative patients were excluded from ADC therapy by the combined QCS features compared to only 27 QCS-negative patients who were excluded from ADC therapy by individual binary spatial proximity features. Thus, a higher percentage of patients is identified as more likely to benefit from ADC based on each individual QCS feature than based on the combined features of bSPS and normalized membrane OD.

[0141] Figure 35 shows spider plots of 80 QCS-positive and 35 QCS-negative patients of the Kaplan-Meier curves of Figure 34. Each dot in the spider plot represents the percentage of the patient's tumor that has grown or shrunk after the indicated number of days. The spider plot shows that the distribution of dots in the upper plot for QCS-negative patients is weighted above the 0% line, whereas significantly more dots in the lower plot for QCS-positives are below the line for 0% tumor growth.

[0142] FIG. 36 is a waterfall bar graph showing clinical responses of 105 of 115 NSCLC patients in the J101 study ordered from greatest tumor growth to greatest tumor shrinkage. Tumor growth / shrinkage results are not shown for the 10 non-evaluable (NE) patients. Solid bars indicate QCS-positive patients whose stained tissue samples were both normalized membrane positive with normalized membrane OD score and proximity positive with binary spatial proximity score. Bars with diagonal hatching indicate QCS-negative patients whose stained tissue samples were not both normalized membrane positive and proximity positive. Most of the bars above the 0% tumor growth line have diagonal hatching indicating QCS-negative patients, while most of the bars below the 0% tumor growth line are solid bars indicating QCS-positive patients. Thus, the bar graph shows that the combined QCS signature of bSPS and normalized membrane OD identifies the majority of patients whose tumors shrink after ADC treatment as QCS-positive and the majority of patients whose tumors grow after ADC treatment as QCS-negative.

[0143] Although the present invention has been described in connection with certain specific embodiments for instructional purposes, the invention is not limited thereto. Various modifications, adaptations, and combinations of various features of the described embodiments may be practiced without departing from the scope of the invention as set forth in the claims.

Claims

1. A method for identifying cancer patients for treatment with an antibody-drug conjugate (ADC) payload comprising an ADC payload and an ADC antibody targeting a protein on cancer cells, wherein the protein is calcium signaling transducer trophoblast antigen 2 (TROP2). The method involves immunohistochemically staining a tissue sample from the cancer patient using a dye linked to a diagnostic antibody, wherein the diagnostic antibody binds to the protein on the cancer cells in the tissue sample. To acquire a digital image of the aforementioned tissue sample, To detect cancer cells in the aforementioned digital image, For each cancer cell, the average optical density of staining by the dye in the membrane of the cancer cell is determined, For each cancer cell, the average optical density of the staining by the dye in the cytoplasm of the cancer cell is determined. The normalized optical density (nmOD) of each cancer cell in the aforementioned digital image is processed by computer, Each cancer cell is identified as nmOD-positive if the nmOD of the cancer cell is below the nmOD threshold, The aforementioned tissue sample is identified as nmOD-positive if the percentage of nmOD-positive cancer cells is above the nmOD percentage threshold. The aforementioned cancer patients are identified as patients who are likely to benefit from the administration of the ADC if the tissue sample is nmOD positive. Methods that include...

2. A pharmaceutical composition for a method of treating cancer, comprising an antibody-drug conjugate (ADC) payload and an ADC antibody that targets a protein on cancer cells, The aforementioned protein is calcium signal transducer trophoblast antigen 2 (TROP2), The aforementioned method, The method involves immunohistochemically staining a tissue sample from the cancer patient using a dye linked to a diagnostic antibody, wherein the diagnostic antibody binds to the protein on the cancer cells in the tissue sample. To acquire a digital image of the aforementioned tissue sample, To detect cancer cells in the aforementioned digital image, For each cancer cell, the average optical density of staining by the dye in the membrane of the cancer cell is determined, For each cancer cell, the average optical density of the staining by the dye in the cytoplasm of the cancer cell is determined. The normalized optical density (nmOD) of each cancer cell in the aforementioned digital image is processed by computer, Each cancer cell is identified as nmOD-positive if the nmOD of the cancer cell is below the nmOD threshold, The aforementioned tissue sample is identified as nmOD-positive if the percentage of nmOD-positive cancer cells is above the nmOD percentage threshold. If the tissue sample is nmOD positive, the cancer patient is to be administered the therapy containing the ADC. including, Pharmaceutical composition.

3. A pharmaceutical composition for a method of treating cancer, comprising an antibody-drug conjugate (ADC) payload and an ADC antibody that targets a protein in cancer cells, The aforementioned protein is calcium signal transducer trophoblast antigen 2 (TROP2), The aforementioned method, If a tissue sample from the cancer patient is determined to be normalized membrane optical density (nmOD) positive, the therapy comprising administering the ADC to the cancer patient, wherein the tissue sample is stained using a diagnostic antibody that binds to the protein in the membrane and cytoplasm of cancer cells in the tissue sample; the average optical density of membrane staining and the average optical density of cytoplasmic staining are determined for each cancer cell in the digital image of the tissue sample; the normalized membrane optical density (nmOD) is computer-processed for each cancer cell in the digital image; each cancer cell in the digital image is identified as nmOD positive if the normalized membrane optical density of the cancer cell is less than or equal to the nmOD threshold; and the tissue sample is identified as nmOD positive if the percentage of nmOD-positive cancer cells is greater than or equal to the nmOD percentage threshold. Pharmaceutical composition.

4. The method according to claim 1, or the pharmaceutical composition according to claim 2 or 3, wherein the detection of cancer cells includes (or includes) detecting, for each cancer cell, pixels belonging to the membrane and pixels belonging to the cytoplasm.

5. The method according to claim 1, further comprising recommending a therapy including the ADC to the cancer patient if the tissue sample is nmOD positive.

6. The method according to claim 1, or the pharmaceutical composition according to claim 2 or 3, wherein the normalization film threshold is (or was) within the range of 0.45 to 0.

50.

7. The method according to claim 1, or the pharmaceutical composition according to claim 2 or 3, wherein the normalized film threshold is (or was) 0.

486.

8. The method according to claim 1, or the pharmaceutical composition according to claim 2 or 3, wherein the percentage threshold is (or was) within the range of 3% to 7%.

9. The method according to claim 1, or the pharmaceutical composition according to claim 2 or 3, wherein the percentage threshold is (or was) 5%.

10. The method according to claim 1, or the pharmaceutical composition according to claim 2 or 3, wherein the detection of cancer cells comprises (or includes) detecting pixels belonging to the membrane using a cell center determined for each cancer cell.

11. The method according to claim 1, or the pharmaceutical composition according to claim 2 or 3, wherein the staining intensity of each membrane is computer-processed (or computer-processed) based on the average optical density of the brown diaminobenzidine (DAB) signal in the pixels of the membrane, and the staining intensity of each cytoplasm is computer-processed based on the average optical density of the brown DAB signal in the pixels of the cytoplasm.

12. The method according to claim 1, or the pharmaceutical composition according to claim 2 or 3, wherein the dye is 3,3'-diaminobenzidine (DAB).

13. The method according to claim 1, or the pharmaceutical composition according to claim 2 or 3, wherein the ADC is datopotamab deruxtecan (DS-1062).

14. The method according to claim 1, or the pharmaceutical composition according to claim 2 or 3, wherein the ADC antibody is hTINA1-H1L1.

15. The method according to claim 1, or the pharmaceutical composition according to claim 2 or 3, wherein the ADC antibody is a humanized IgG1 monoclonal antibody.

16. The method according to claim 1, or the pharmaceutical composition according to claim 2 or 3, wherein the ADC payload is a topoisomerase I inhibitor.

17. The topoisomerase I inhibitor is given by the following formula: 【Chemistry 1】 A method or pharmaceutical composition according to claim 16, as represented by [the specified formula].

18. The method according to claim 1, or the pharmaceutical composition according to claim 2 or 3, wherein the diagnostic antibody is Abcam EPR 20043.

19. The method according to claim 1, or the pharmaceutical composition according to claim 2 or 3, wherein the cancer patient has a cancer selected from the group consisting of breast cancer, gastric cancer, colorectal cancer, lung cancer, esophageal cancer, head and neck cancer, gastroesophageal junction cancer, biliary tract cancer, Paget's disease, pancreatic cancer, ovarian cancer, uterine carcinosarcoma, bladder cancer, prostate cancer, urothelial carcinoma, gastrointestinal stromal tumor, cervical cancer, squamous cell carcinoma, peritoneal cancer, liver cancer, hepatocellular carcinoma, endometrial cancer, kidney cancer, vulvar cancer, thyroid cancer, penile cancer, leukemia, malignant lymphoma, plasmacytoma, myeloma, glioblastoma multiforme, sarcoma, osteosarcoma, and melanoma.

20. The method according to claim 1, or the pharmaceutical composition according to claim 2 or 3, wherein the cancer patient has a cancer selected from the group consisting of breast cancer, gastric cancer, colorectal cancer, lung cancer, pancreatic cancer, ovarian cancer, prostate cancer, and kidney cancer.

21. The method according to claim 1, or the pharmaceutical composition according to claim 2 or 3, wherein the cancer patient has breast cancer.

22. The method according to claim 1, or the pharmaceutical composition according to claim 2 or 3, wherein the cancer patient has lung cancer.

23. The method according to claim 1, or the pharmaceutical composition according to claim 2 or 3, wherein the cancer patient has non-small cell lung cancer.

24. The ADC is an anti-TROP2 antibody conjugated to a drug-linker via a thioether bond, and the drug-linker has the following formula: 【Chemistry 2】 It is represented as, In the formula, A represents the connection site with the anti-TROP2 antibody. The method according to claim 1, or the pharmaceutical composition according to claim 2 or 3.

25. The ADC, A heavy chain comprising CDRH1 consisting of the amino acid sequence represented by SEQ ID NO: 3, CDRH2 consisting of the amino acid sequence represented by SEQ ID NO: 4, and CDRH3 consisting of the amino acid sequence represented by SEQ ID NO: 5, A light chain comprising CDRL1 consisting of the amino acid sequence represented by SEQ ID NO: 6, CDRL2 consisting of the amino acid sequence represented by SEQ ID NO: 7, and CDRL3 consisting of the amino acid sequence represented by SEQ ID NO:

8. The method according to claim 1, or the pharmaceutical composition according to claim 2 or 3, comprising an anti-TROP2 antibody containing the above.

26. The ADC, A heavy chain variable region consisting of the amino acid sequence represented by Sequence ID No. 9, A light chain variable region consisting of the amino acid sequence represented by Sequence ID No. 10, The method according to claim 1, or the pharmaceutical composition according to claim 2 or 3, comprising an anti-TROP2 antibody containing the above.

27. The ADC, A heavy chain consisting of the amino acid sequence represented by Sequence ID No. 11, A light chain consisting of the amino acid sequence represented by Sequence ID No. 2, The method according to claim 1, or the pharmaceutical composition according to claim 2 or 3, comprising an anti-TROP2 antibody containing the above.

28. The ADC, A heavy chain consisting of the amino acid sequence represented by Sequence ID No. 1, A light chain consisting of the amino acid sequence represented by Sequence ID No. 2, The method according to claim 13, or a pharmaceutical composition comprising an anti-TROP2 antibody containing the above.

29. A pharmaceutical composition for a method of treating cancer, comprising an antibody-drug conjugate (ADC) payload and an ADC antibody that targets a protein on cancer cells, The aforementioned protein is calcium signal transducer trophoblast antigen 2 (TROP2), The aforementioned method, If a tissue sample from the cancer patient is positive for normalized film optical density (nmOD), the therapy comprising administering the ADC to the cancer patient, Pharmaceutical composition.

30. A pharmaceutical composition for a method of treating cancer, comprising an antibody-drug conjugate (ADC) payload and an ADC antibody that targets a protein in cancer cells, The aforementioned protein is calcium signal transducer trophoblast antigen 2 (TROP2), The aforementioned method, If a tissue sample from the cancer patient is determined to be positive for normalized film optical density (nmOD), the therapy comprising administering the ADC to the cancer patient, Pharmaceutical composition.

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