Histochemical systems and methods for evaluating EGFR and EGFR ligand expression in tumor samples

Abstract

A method and system for predictive measures of anti-EGFR therapy response in wild-type RAS / EGFR+ samples, for example, histochemical staining methods for staining EGFR, AREG, and EREG, digital analysis of stained slides, and scoring algorithms that allow predicting response to anti-EGFR therapy.Stained slide analysis and scoring algorithms can include, but are not limited to, tumor cell positivity rate, computerized clustering algorithms, areal density (e.g., the area of ​​tumors positive for one or more markers compared to the total tumor area), mean intensity (e.g., computerized methodology that measures mean grayscale pixel intensity), mean intensity divided according to membrane, cytoplasm, or punctate staining pattern, or any other suitable parameter or combination of parameters.The method of the present invention allows for decomposing the spatial expression patterns of ligands and receptors to determine which patterns predict response to anti-EGFR therapy.

Description

Technical Field

[0001] Cross - reference to Related Applications This application claims the priority and benefit of U.S. Provisional Patent Application No. US63 / 021,627, filed on May 7, 2020.

[0002] Incorporation of Sequence Listing by Reference This application incorporates herein by reference a sequence listing, filed in computer - readable format together with this application, having the file name 34457WO_SEQLIST_ST25, created on April 19, 2021, and a size of 19,551 bytes.

[0003] The present invention relates to a histochemical method, system, and composition for evaluating the expression of human epidermal growth factor receptor (EGFR) protein and the expression of human EGFR ligand protein in colorectal tumors.

Background Art

[0004] Approximately 20% of colorectal cancer patients develop metastatic colorectal cancer (mCRC). More than half (50-60%) of these patients eventually develop an incurable, progressive disease, with a five-year survival rate of approximately 12.5%. In mCRC, two signaling pathways, the vascular endothelial growth factor receptor (VEGFR) pathway and the epidermal growth factor receptor (EGFR) pathway, are the focus of therapeutic development. Currently, the majority of mCRC patients receive cytotoxic chemotherapy in combination with either EGFR-targeted therapy or VEGF-targeted therapy. EGFR is overexpressed in approximately 70% of CRC cases and is associated with poor outcomes. EGFR-targeted inhibition with monoclonal antibodies such as cetuximab or panitumumab was approved by the FDA in 2004 and 2006 for the treatment of mCRC patients. These antibodies target the extracellular domain of EGFR, competing with endogenous ligands to prevent receptor activation. By inhibiting the EGFR signaling pathway, these biological agents inhibit cell proliferation, differentiation, migration, and metastasis. Both drugs have very similar efficacy, with response rates of 10–15%.

[0005] For a long time, there have been no reliable positive predictors regarding the response to EGFR-targeted therapy.

[0006] Clinical trials have shown that EGFR inhibitors are most effective in patients lacking RAS pathway mutations. Point mutations in members of the RAS signaling pathway, such as KRAS, NRAS, and BRAF, result in persistent activation of downstream RAS-MAPK signaling, regardless of whether EGFR is pharmacologically inactivated. In addition to RAS and BRAF mutations, other alternative mechanisms, such as cMET or EGFR amplification, are involved in resistance to cetuximab or panitumumab. Mutations of PI3K or PTEN loss (often occurring with RAS or BRAF mutations) may also be associated with a lack of response. In fact, RAS, BRAF, and PI3K mutations account for over 60% of mCRC patients exhibiting de novo resistance to EGFR-targeted monoclonal antibodies. Of the 40% of patients with KRAS, NRAS, BRAF, and PI3K wild-type tumors (quadruple wild-type patients), only about half (15%) of these patients benefit from anti-EGFR therapy, while over 20% are non-responders. See Perkins et al., Pharmacogenetics, Vol. 15, No. 7, pp. 1043-1052 (2014).

[0007] Overexpression of EGFR ligands, including the ligands epiregulin (EREG) and amphiregulin (AREG), has been suggested as a predictor of anti-EGFR therapy. In one trial involving mCRC patients, the addition of anti-EGFR therapy increased survival in patients with high EREG expression levels from 5.1 months to 9.8 months compared to best supportive care alone. This result suggests that EGFR ligand expression may be a clinically useful biomarker for screening mCRC patients for EGFR inhibitor therapy. However, PCR-based detection systems cannot identify the spatial relationship between ligands and receptors.

[0008] Immunohistochemical analysis of EGFR ligands has yielded varied results. For example, Khelwatty et al. (Oncotarget. 2017 / 1 / 31;8(5):7666~7677) reported that co-expression of wild-type EGFR and at least one of its ligands (at a cutoff of EGFR-positive tumor cells >5% and ligand staining intensity 2+) was significantly correlated with shorter progression-free survival and, consequently, a lower response rate to EGFR-targeted therapy. However, in their samples, EGFR staining was primarily observed in the cytoplasm, leading them to the theory that internal translocation of EGFR prevented EGFR therapy from inducing antibody-dependent cell-mediated cytotoxicity (ADCC). They further noted that up to 40% of the patients in their study may have previously received cetuximab therapy, which may have contributed to the downregulation of EGFR from the surface. Therefore, Khelwatty does not describe a clear correlation between EGFR and EGFR ligand expression patterns and responses to EGFR-targeted therapies. On the other hand, Yoshida et al. (Journal of Cancer Research and Clinical Oncology, March 2013, Vol. 139, No. 3, pp. 367-378) found a good correlation between four of the seven ligands (AREG, HB-EGF, TGFα, and EREG) and clinical responses to EGFR therapy, and that the response rate was significantly higher in patients expressing two or more of the four ligands. However, Yoshida did not consider the relationships between EGFR and EGFR ligand expression patterns. Therefore, it is difficult to say that Yoshida fully explains the variable factors that may affect the effectiveness of EGFR-targeted therapy. [Overview of the project]

[0009] This disclosure relates, in general, to methods, systems, and compositions for histochemical staining and evaluation of colorectal tumor specimens for EGFR and EGFR ligand expression. The disclosed methods, systems, and compositions are useful, among other things, for stratifying patients according to their predicted response to anti-EGFR therapy and / or for screening colorectal polyps for their likelihood of progressing to colorectal cancer.

[0010] In one embodiment, a simplex staining methodology is provided, in which a set of stained sections of a colorectal tumor of interest is obtained, the set comprising (a1) a first section histochemically stained for human EGFR protein, and (a2) at least a second section histochemically stained for one or more human EGFR ligands, including human AREG protein and / or human EREG protein. The stained sections can be evaluated for expression patterns that correlate with the likelihood that the tumor will respond to anti-EGFR therapy (e.g., a therapeutic agent that disrupts the association between EGFR and EGFR ligands). In one embodiment, digital images of the sections are obtained and evaluated by a digital pathology methodology, which includes aligning the digital images of the second section with the digital images of the first section (and vice versa), and then evaluating the spatial relationships between human EGFR protein and EGFR ligands. If the expression patterns of human EGFR protein and human EGFR ligand, and / or the spatial relationships between them, indicate a tumor that is likely to respond to anti-EGFR therapy, the subject may be treated with a course of therapy including anti-EGFR therapy.

[0011] In another embodiment, a multiplex methodology is provided in which individual histochemically stained sections of a colorectal tumor of interest are obtained, and each section is differentially stained for (a1) human EGFR protein, and (a2) at least one of human AREG protein and human EREG protein. The stained sections can be evaluated for expression patterns that correlate with the likelihood that the tumor will respond to anti-EGFR therapy (e.g., a therapeutic agent that disrupts the association between EGFR and EGFR ligand). In one embodiment, digital images of the sections are obtained and evaluated by a digital pathology methodology that includes evaluating the expression patterns of human EGFR protein and EGFR ligand and / or the spatial relationships between them. If the expression patterns of human EGFR protein and human EGFR ligand and / or the spatial relationships between them indicate a tumor that is likely to respond to anti-EGFR therapy, the subject may be treated with a course of treatment including anti-EGFR therapy.

[0012] As will be apparent from the context, this specification, and the knowledge of those skilled in the art, any feature or combination of features described herein is within the scope of the present invention, provided that the features included in any combination of features are not contradictory to each other. Further advantages and aspects of the present invention will be apparent in the following detailed description and claims.

[0013] A patent or application file must include at least one drawing made in color. A copy of the published patent or patent application containing the color drawing will be provided by the Patent Office upon request and payment of the required fees. [Brief explanation of the drawing]

[0014] [Figure 1] This figure illustrates two different methods for calculating feature metrics related to ROIs. The dashed lines in the image indicate ROI boundaries. The "X" symbols in the image represent objects of interest marked within the image. The circles in the image represent reference regions that can be used to calculate global metrics related to these reference regions. [Figure 2]Figure 2A shows the distribution of EREG and AREG mRNA expression (qPCR values) in the cohort. Figure 2B shows that EREG mRNA expression is closely related to AREG mRNA expression. [Figure 3] Figure 3A shows the percentage of IHC-positive tumor cells compared to qPCR for EREG. The positivity rate correlates well with qPCR for EREG. Figure 3B shows the percentage of IHC-positive tumor cells compared to qPCR for AREG. The positivity rate correlates well with qPCR for AREG. [Figure 4A-B] This figure shows the correlation between multiple parameters and qPCR values. Figure 4A shows the percentage of IHC-positive cells at parameter 1 compared to EREG qPCR. Figure 4B shows the percentage of IHC-positive cells at parameter 1 compared to AREG qPCR. [Figure 4C-D] This figure shows the correlation between multiple parameters and qPCR values. Figure 4C shows the percentage of IHC-positive cells for parameter 2 compared to EREG qPCR. Figure 4D shows the percentage of IHC-positive cells for parameter 2 compared to AREG qPCR. [Figure 4E-F] This figure shows the correlation between multiple parameters and qPCR values. Figure 4E shows the percentage of IHC-positive cells for parameter 3 compared to qPCR of EREG. Figure 4F shows the percentage of IHC-positive cells for parameter 3 compared to qPCR of AREG. [Figure 4G-H] This figure shows the correlation between multiple parameters and qPCR values. Figure 4G shows the percentage of IHC-positive cells for parameter 4 compared to qPCR of EREG. Figure 4H shows the percentage of IHC-positive cells for parameter 4 compared to qPCR of AREG. [Figure 5A-B] Figure 5A shows the membrane staining intensity compared to EREG qPCR. Figure 5B shows the membrane staining intensity compared to AREG qPCR. [Figure 5C-D]Figure 5C shows the cytoplasmic staining intensity compared to EREG qPCR. Figure 5D shows the cytoplasmic staining intensity compared to AREG qPCR. [Figure 5E-F] Figure 5E shows the staining intensity of granules / spots compared to EREG qPCR. Figure 5F shows the staining intensity of granules / spots compared to AREG qPCR. [Figure 6A] This figure shows an example of a field of view of a stained tissue section. The method of the present invention can identify any tumor cells and classify them as marker-negative (shown in green and blue) or marker-positive (shown in yellow, orange, red, and magenta). The number of tumor cells in the entire slide can be reported separately for marker-negative and marker-positive cells. [Figure 6B-C] This figure shows an example of a field of view of a stained tissue section. The method of the present invention can identify any tumor cells and classify them as marker-negative (shown in green and blue) or marker-positive (shown in yellow, orange, red, and magenta). The number of tumor cells in the entire slide can be reported separately for marker-negative and marker-positive cells. [Figure 7] This figure shows the staining of two colorectal cases using a multiplex IHC assay targeting EGFR, epiregulin (EREG), and amphiregulin (AREG). In these cases, EGFR is stained with DISCOVERY yellow, EREG with DISCOVERY teal, and AREG with DISCOVERY purple. [Figure 8] This figure shows the analysis of multiplex-stained samples using digital pathology. The first row shows that the multiplex matches the signal of the corresponding DAB simplex assay. The second row shows that digital image analysis allows the multiplex assay to be analyzed into its constituent stains. The third row shows that the analyzed channels can be recombined and restained to create a pseudo-DAB image.

BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The present disclosure generally relates to methods, systems, and compositions for histochemical staining and evaluation of colorectal tumor samples for the expression of EGFR and EGFR ligands. The disclosed methods, systems, and compositions are useful, inter alia, for stratifying colorectal cancer patients according to the likelihood that the tumor will respond to EGFR-directed therapy.

[0016] I. TERMINOLOGY Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the disclosed invention belongs. The singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise.

[0017] Methods and materials suitable for the practice and / or testing of embodiments of the present disclosure are described below. Such methods and materials are merely examples and are not intended to be limiting. Other methods and materials similar or equivalent to those described herein may be used. For example, conventional methods well known in the technical fields related to the present disclosure are described in various general references and more specific references, including, for example, Sambrook et al., Molecular Cloning: A Laboratory Manual, 2nd edition, Cold Spring Harbor Laboratory Press, 1989; Sambrook et al., Molecular Cloning: A Laboratory Manual, 3rd edition, Cold Spring Harbor Press, 2001; Ausubel et al., Current Protocols in Molecular Biology, Greene Publishing Associates, 1992 (and 2000 supplement); Ausubel et al., Short Protocols in Molecular Biology: A Compendium of Methods from Current Protocols in Molecular Biology, 4th edition, Wiley & Sons, 1999; Harlow and Lane, Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory Press, 1990; and Harlow and Lane, Using Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory Press, 1999, the disclosures of which are hereby incorporated by reference in their entirety.

[0018] All publications, patent applications, patents, and other references mentioned herein are hereby incorporated by reference in their entirety for all purposes. In case of conflict, the present specification, including explanations of terms, shall prevail.

[0019] To facilitate the understanding of the various embodiments of this disclosure, the following explanations of specific terms are provided.

[0020] Administration: Providing or supplying a drug, such as a composition or medicine, to a subject by any effective route. Examples of routes of administration include, but are not limited to, oral, injection (subcutaneous, intramuscular, intradermal, intraperitoneal, and intravenous), sublingual, rectal, transdermal (e.g., topical), intranasal, transvaginal, and inhalation.

[0021] Antibody: A peptide (e.g., polypeptide) that contains at least a light chain or heavy chain immunoglobulin variable region and specifically binds to an antigen epitope. Antibodies include monoclonal antibodies, polyclonal antibodies, or antibody fragments.

[0022] Antibody fragment: A molecule other than an intact antibody that contains a portion of an intact antibody and binds to the antigen to which the intact antibody binds. Examples of antibody fragments include, but are not limited to, Fv, Fab, Fab', Fab'-SH, F(ab')2; diabodies; linear antibodies; single-chain antibody molecules (e.g., scFv); and multispecific antibodies formed from antibody fragments.

[0023] Biomarker: As used herein, the term “biomarker” means any molecule or group of molecules found in a sample that can be used to characterize the sample or subject from which the sample was obtained. For example, a biomarker may be a molecule or group of molecules whose presence, absence, or relative abundance is characteristic of a particular disease condition; indicates the severity or likelihood of a disease, or the progression or regression of a disease; and / or predicts that a pathological condition will respond to a particular treatment.

[0024] Biomarker-specific reagent: A specific binder capable of directly and specifically binding to one or more biomarkers in a cell or tissue sample. The phrase "[target] biomarker-specific reagent" refers to a biomarker-specific reagent capable of specifically binding to the listed target biomarker.

[0025] Counterstaining: Staining of tissue sections with a dye that provides a primary color reference, allowing the overall "appearance" of the tissue section to be seen and used for the detection of tissue targets. Such dyes can stain the cell nucleus, cell membrane, or the entire cell. Examples of dyes include DAPI, which binds to nuclear DNA and emits a strong blue light; Hoechst's blue stain, which binds to nuclear DNA and emits a strong blue light; and propidium iodide, which binds to nuclear DNA and emits a strong red light. Counterstaining of the intracellular cytoskeletal network can be performed using phalloidin conjugated with a fluorescent dye. Phalloidin is a toxin that tightly binds to actin filaments in the cytoplasm of cells, making them clearly visible under a microscope.

[0026] Detectable part: A molecule or material that can produce a detectable signal (e.g., visual, electrical, or other signal) indicating the presence and / or concentration of a detectable part or label attached to a sample. The detectable signal may be generated by any known or undiscovered mechanism, including the absorption, emission, and / or scattering of photons (including photons of high frequency, microwave frequency, infrared frequency, visible frequency, and ultraviolet frequency). Exemplary detectable parts include, but are not limited to, chromogenic, fluorescent, phosphorescent, and luminescent molecules and materials, and catalysts (e.g., enzymes) that convert one substance into another, resulting in a detectable difference (e.g., by converting a colorless substance into a colored substance or vice versa, or by producing a precipitate or increasing the turbidity of the sample). In some examples, the detectable part is a fluorophore belonging to several common chemical classes, including coumarin, fluorescein (or fluorescein derivatives and analogs), rhodamine, resolphins, luminescent phores, and cyanines. Further examples of fluorescent molecules can be found in *Molecular Probes Handbook—A Guide to Fluorescent Probes and Labeling Technologies*, *Molecular Probes*, Eugene, OR, ThermoFisher Scientific, 11th edition. In other embodiments, the detectable portion is a molecule detectable by bright-field microscopy, such as dyes containing diaminobenzidine (DAB), 4-(dimethylamino)azobenzene-4'-sulfonamide (DABSYL), tetramethylrhodamine (DISCOVERY Purple), N,N'-biscarboxypentyl-5,5'-disulfonato-indodicarbocyanine (Cy5), and rhodamine 110 (rhodamine).

[0027] Detection reagents: Any reagent used to stain a cell sample by attaching a detectable portion near a biomarker-specific reagent bound to a biomarker in the sample. Non-limiting examples include secondary detection reagents (e.g., secondary antibodies that can bind to a primary antibody, anything that specifically binds to biotin or avidin), tertiary detection reagents (e.g., tertiary antibodies that can bind to a secondary antibody), enzymes that directly or indirectly associate with specific binders, chemicals that are reactive with such enzymes and result in the attachment of fluorescent or chromogenic stains, and washing reagents used between staining steps.

[0028] Monoclonal antibodies are antibodies obtained from a substantially homogeneous population of antibodies, i.e., a population in which the individual antibodies constituting the population are identical and / or bind to the same epitope, except for possible mutant antibodies, such as those containing naturally occurring mutations or those arising during the production of monoclonal antibody preparations, which are usually present in trace amounts. Unlike polyclonal antibodies, each monoclonal antibody in a monoclonal antibody preparation is directed against a single determinant on an antigen. Therefore, the modifier "monoclonal" indicates the characteristic of the antibody being obtained from a substantially homogeneous population of antibodies and should not be interpreted as requiring the production of the antibody by any particular method.

[0029] Multiplexing, multiplexed, to multiplex: Staining a single-cell sample with multiple specific binders in a differentially detectable manner.

[0030] Polyclonal antibodies: Antibody preparations that typically contain different antibodies against different determinants (epitopes).

[0031] Sample: Any material obtained from a subject for diagnostic purposes and processed in a manner suitable for testing for the presence or absence and / or quantity of biomarkers in the material using a specific binder. Examples of diagnostic purposes include diagnosing or prognosing a disease in a subject, and / or predicting the disease's response to a particular treatment regimen, and / or monitoring the subject's response to a treatment regimen, and / or monitoring disease progression or recurrence. (a) Cell specimens: specimens containing intact cells, such as cell cultures, blood or other bodily fluid specimens containing cells, cell smears (e.g., Pap smears and cervical monolayers), fine-needle aspiration (FNA), liquid-based cytological specimens, and surgical specimens taken for pathological, histological, or cytological interpretation. (b) Tissue sample: A cell sample that maintains the cross-sectional spatial relationships between cells as they were present in the subject from which the sample was obtained. "Tissue sample" shall include both primary tissue samples (i.e., cells and tissues produced by the subject) and xenografts (i.e., foreign cell samples implanted in the subject).

[0032] Section: When used as a noun, it refers to a thin section of a tissue sample suitable for microscopic analysis, usually cut using a microtome. When used as a verb, it refers to the act of preparing sections of a tissue sample, usually using a microtome.

[0033] Serial section: Any one of a series of sections cut sequentially from a tissue sample. For two sections to be considered "serial sections" of each other, they do not necessarily have to be consecutive sections from the tissue, but they should usually contain the same tissue structure in the same cross-sectional relationship so that the structures can be matched with each other after histological staining.

[0034] Specific binding: As used herein, the terms “specific binding,” “specifically binding to,” or “specific to” refer to measurable and reproducible interactions, such as the binding between a target and a specific binder that determines the presence of the target in the presence of a heterogeneous population of molecules, including biological molecules. For example, a binding entity that specifically binds to a target may be an antibody that binds to the target with higher affinity, higher avidity, more readily, and / or for a longer duration than it would to bind to other targets.

[0035] Specific binding agent: Any composition of a substance that can specifically bind to a target chemical structure associated with a cell or tissue sample (e.g., a biomarker expressed by the sample, or a biomarker-specific reagent bound to the sample). Examples include nucleic acid probes specific to particular nucleotide sequences; antibodies and their antigen-binding fragments; as well as ADNECTIN (a scaffold based on the 10th FN3 fibronectin; Bristol-Myers-Squibb Co.), AFFIBODY (a scaffold based on the Z domain of protein A from Staphylococcus aureus (S. aureus); Affibody AB, Solna, Sweden), AVIMER (a scaffold based on the A / LDL receptor domain; Amgen, Thousand Oaks, CA), dAb (a scaffold based on the VH or VL antibody domain; GlaxoSmithKline PLC, Cambridge, UK), DARPin (a scaffold based on ankyrin repeat protein; Molecular Partners AG, Zurich, CH), ANTICALIN (a scaffold based on lipocalin; Pieris AG, Freising, DE), and NANOBODY (a scaffold based on VHH (Ig of the camelid family); Ablynx Examples include, but are not limited to, engineered specific binding structures including N / V, Ghent, BE), TRANS-BODY (transferrin-based scaffold; Pfizer Inc., New York, NY), SMIP (Emergent Biosolutions, Inc., Rockville, MD), and TETRANECTIN (type C lectin domain (CTLD), tetranectin-based scaffold; Borean Pharma A / S, Aarhus, DK).Such engineered, specifically binding structures are outlined in Wurch et al., *Development of Novel Protein Scaffolds as Alternatives to Whole Antibodies for Imaging and Therapy: Status on DISCOVERY Research and Clinical Validation*, *Current Pharmaceutical Biotechnology*, Vol. 9, pp. 502-509 (2008), and their contents are referenced therein.

[0036] Staining: When used as a noun, the term “staining agent” refers to any substance that may be used to visualize specific molecules or structures in a cell sample for microscopic analysis, including bright-field microscopy, fluorescence microscopy, and electron microscopy. When used as a verb, the term “staining” refers to any process that results in the adhesion of a staining agent to a cell sample.

[0037] Subject: The mammal from which the sample was obtained or derived. Mammals include, but are not limited to, domesticated animals (e.g., cattle, sheep, cats, dogs, and horses), primates (e.g., humans and non-human primates such as monkeys), rabbits, and rodents (e.g., mice and rats). In certain embodiments, the subject is human.

[0038] II. Histochemical methods for labeling colorectal samples with EGFR and EGFR ligands The present method, system, and composition are based on staining colorectal tumor specimens for EGFR protein and one or more of EREG and AREG.

[0039] In one embodiment, staining of colorectal tumor specimens is performed by the simplex method. Simplex histochemical staining is a staining method in which a single biomarker-specific reagent (or group of biomarker-specific reagents) is applied to a single section and stained with a single color stain. The simplex method allows users to avoid the complex multiplex staining process and analysis methods. If the spatial relationships between different biomarkers are important, digital analysis, including the alignment of stained images with each other, may be used.

[0040] In one embodiment, a simplex histochemical staining method is provided, which yields a set of stained colorectal tumor samples comprising: (a) a first sample derived from a colorectal tumor and histochemically stained for human EGFR protein; and (b) a second sample derived from the same colorectal tumor as the first sample and histochemically stained for one or more of human EREG protein and human AREG protein. In one embodiment, the set of stained colorectal tumor samples comprises: (a) a first sample derived from a colorectal tumor and histochemically stained for human EGFR protein; and (b) a second sample derived from the same colorectal tumor as the first sample and histochemically stained for human EREG protein. In one embodiment, the set of stained colorectal tumor samples includes: (a) a first sample derived from a colorectal tumor and histochemically stained for human EGFR protein; and (b) a second sample derived from the same colorectal tumor as the first sample and histochemically stained for human AREG protein. In one embodiment, the set of stained colorectal tumor samples includes: (a) a first sample derived from a colorectal tumor and histochemically stained for human EGFR protein; (b) a second sample derived from the same colorectal tumor as the first sample and histochemically stained for human EREG protein; and (c) a third sample derived from the same colorectal tumor as the first sample and histochemically stained for human AREG protein. In one embodiment, a set of stained colorectal tumor samples includes: (a) a first sample derived from a colorectal tumor, which is histochemically stained for human EGFR protein; and (b) a second sample derived from the same colorectal tumor as the first sample, which is histochemically stained for human AREG protein and human EREG protein.In one embodiment, the first, second, and / or third samples are tissue sections derived from the same fixed tissue sample. In one embodiment, the sections are made from a formalin-fixed paraffin-embedded (FFPE) tissue sample. In one embodiment, the first, second, and / or third samples are serial sections derived from the same FFPE tissue sample. In one embodiment, a set of stained serial sections is provided, the set of stained serial sections comprising (a) the first, second, and / or third serial sections. In another embodiment, the set of stained serial sections may further comprise (b) additional serial sections stained with a morphological stain (e.g., hematoxylin and eosin (H&E)).

[0041] In one embodiment, staining of colorectal tumor specimens is performed by a multiplex method. Multiplex histochemical staining is a staining method that applies multiple biomarker-specific reagents to a single section, staining it with dyes that are distinguishable from each other. In multiplex staining, the biomarker-specific reagents and detection reagents are applied in a manner that allows different biomarkers to be differentially labeled. The multiplex method allows the user to observe the spatial relationships between different biomarkers without having to rely on aligning separately histochemically stained slides with each other.

[0042] In one embodiment, a multiplex histochemical staining method is provided, which yields a stained colorectal tumor sample derived from a colorectal tumor, the stained colorectal sample being histochemically stained for human EGFR protein and one or more of human EREG protein and human AREG protein, and the histochemical stain for human EREG protein is distinguishable from the histochemical stain for one or more of human EREG protein and human AREG protein. In one embodiment, the stained colorectal sample is histochemically stained for human EGFR protein and human EREG protein. In one embodiment, the stained colorectal sample is histochemically stained for human EGFR protein and human EREG protein, and the histochemical stain for EGFR is distinguishable from the histochemical stain for EREG. In one embodiment, the stained colorectal sample is histochemically stained for human EGFR protein and human AREG protein, and the histochemical stain for EGFR is distinguishable from the histochemical stain for AREG. In one embodiment, a stained colorectal sample is histochemically stained for human EGFR protein, human EREG protein, and human AREG protein, and the histochemical stain for EGFR is distinguishable from the histochemical stain for EREG, and the histochemical stain for AREG is distinguishable from the histochemical stain for EGFR and the histochemical stain for EREG. In another embodiment, a stained colorectal sample is histochemically stained for human EGFR protein, human EREG protein, and human AREG protein, and the histochemical stain for EGFR is distinguishable from the histochemical stain for EREG, but the histochemical stain for AREG is not distinguishable from the histochemical stain for EREG.

[0043] A. Samples and sample preparation This method is applied to tissue samples of colorectal tissue obtained from subjects suspected of having colorectal tumors, such as tumor biopsy samples and resected specimens.

[0044] In one embodiment, the tissue sample is a fixed tissue sample. Fixation of the tissue sample preserves cells and tissue components in a state as close to living as possible, allowing them to undergo preparation procedures without significant changes. The processes of autolysis and bacterial degradation that begin upon cell death are stopped, and the cells and tissue components in the sample are stabilized, making them able to withstand subsequent tissue processing steps. Fixatives can be classified into crosslinking binders (aldehydes such as formaldehyde, paraformaldehyde, and glutaraldehyde, as well as non-aldehyde crosslinking binders), oxidizing agents (metal ions and complexes such as osmium tetroxide and chromic acid), protein denaturants (e.g., acetic acid, methanol, and ethanol), fixatives of unknown mechanisms (e.g., mercuric chloride, acetone, and picric acid), combination reagents (e.g., Carnoy fixative, methacarn, Bouin's solution, B5 fixative, Rossman's solution, and Gendre's solution), microwave, and other fixatives (e.g., excluded volume fixation and vapor fixation). Additives such as buffers, surfactants, tannic acid, phenol, metal salts (e.g., zinc chloride, zinc sulfate, and lithium salts), and lanthanum may be included in the fixatives. The most commonly used fixative in sample preparation is formaldehyde, usually in the form of formalin solution (formaldehyde in aqueous (typically buffered) solution). In one embodiment, the sample used in the present method is fixed by a method comprising fixation in a formalin-based fixative. In one example, the fixative is 10% neutral buffered formalin. Notwithstanding these examples, the tissue can be fixed by a process using any fixative medium that is compatible with the biomarker-specific reagents and specific detection reagents used.

[0045] In some cases, fixed tissue samples are embedded in an embedding medium. The embedding medium is an inert material in which tissue and / or cells are embedded so that they can be preserved for future analysis. Embedding also allows the tissue sample to be sliced ​​into thin sections. Examples of embedding mediums include paraffin, celloidin, OCT® compounds, agar, plastic, or acrylic. In one embodiment, the sample is fixed in formalin and embedded in paraffin to form a formalin-fixed paraffin-embedded (FFPE) block. In a typical embedding process (e.g., those used for FFPE blocks), after the sample is fixed, it is subjected to a series of alcohol immersions using progressively increasing alcohol concentrations, typically ranging from about 70% to about 100%, to dehydrate the sample. The alcohol is generally an alkanol, particularly methanol and / or ethanol. In certain working embodiments, 70%, 95%, and 100% ethanol were used in these sequential dehydration steps. After the final alcohol treatment step, the sample is then immersed in another organic solvent, commonly called a clarifying solution. The clarifying solution (1) removes any remaining alcohol and (2) increases the hydrophobicity of the sample for the subsequent wax treatment step. The clarifying solvent is usually an aromatic organic solvent such as xylene. By applying embedding material to the clarified sample, a block is formed, from which tissue sections can be cut (for example, by using a microtome).

[0046] Regardless of these examples, the present disclosure does not require any specific processing steps, as long as the tissue sample obtained is compatible with the histochemical staining of the sample for the biomarker of interest, and with the reagents used for this staining and subsequent microscopic evaluation or digital imaging.

[0047] B. Sample Selection In one embodiment, the tumor from which the sample originates is staged before staining for EGFR protein and EREG protein and / or AREG protein. Stage 0 colorectal cancer is cancer that has not grown beyond the inner lining of the colon. Stage 1 colorectal cancer is cancer that has not spread outside the colon wall itself or into nearby lymph nodes. Stage 2 colorectal cancer is cancer that has passed through the colon wall and possibly into nearby tissues, but has not spread to lymph nodes. Stage 3 colorectal cancer is cancer that has spread to nearby lymph nodes but has not spread to other parts of the body. Stage IV colorectal cancer is cancer that has spread from the colon to organs and tissues distal to the colon. In one embodiment, the sample is selected for staining if it is stage III or stage IV colorectal cancer. In another embodiment, the sample is selected for staining if it is stage IV colorectal cancer.

[0048] C. Histochemical staining, general Labeling of a target biomarker can be achieved by contacting a tissue section with a biomarker-specific reagent under conditions that facilitate specific binding between the target biomarker and the biomarker-specific reagent. The sample is then contacted with a set of detection reagents that interact with the biomarker-specific reagent to facilitate the attachment of a detectable portion near the target biomarker in the sample, thereby generating a detectable signal localized to the target biomarker. Optionally, the biomarker-stained section may be further stained with a contrast agent (such as hematoxylin stain) to visualize the polymer structure. Furthermore, serial sections of the biomarker-stained section or the biomarker-stained section itself may be stained with a morphological stain, which may be useful for identifying regions of interest for subsequent digital analysis.

[0049] The labeling methods described herein may be carried out using an automated staining machine (or other slide processing machine), manually, or may be characterized by a combination of automated and manual processes.

[0050] C1. Biomarker-specific reagents The histochemical staining methods disclosed herein involve contacting tissue sections of colorectal tumors with one or more underbiomarker-specific reagents for human EGFR protein, human EREG protein, and / or human AREG protein under conditions supporting specific binding between the biomarker-specific reagent and the biomarker expressed by the sample. EREG and AREG, like all EGFR ligands, are first expressed as propeptides, which are cleaved on the cell surface to release active signaling domains. The canonical amino acid sequences of full-length human EGFR, as well as human EREG and AREG (and their propeptides), are listed in Table 1. As will be understood by those skilled in the art, the exact amino acid sequences may vary slightly from subject to subject. TIFF0007855525000001.tif65170

[0051] In one embodiment, the biomarker-specific reagent for human EGFR protein is a biomarker-specific reagent that can specifically bind to a polypeptide containing SEQ ID NO: 1. In one embodiment, the biomarker-specific reagent for human EREG protein is a biomarker-specific reagent that can specifically bind to a polypeptide containing SEQ ID NO: 2. In one embodiment, the biomarker-specific reagent for human AREG protein is a biomarker-specific reagent that can specifically bind to a polypeptide containing SEQ ID NO: 3.

[0052] In one embodiment, the EGFR biomarker-specific reagent is an antibody. In another embodiment, the antibody is a monoclonal antibody. Non-limiting examples of EGFR-specific monoclonal antibodies are shown in Table 2. TIFF0007855525000002.tif196170

[0053] In one embodiment, the EGFR biomarker-specific reagent is a monoclonal antibody against the intracellular domain of EGFR. In another embodiment, the EGFR biomarker-specific reagent is a monoclonal antibody against the extracellular domain of EGFR. In yet another embodiment, the EGFR biomarker-specific reagent is a monoclonal antibody that recognizes both full-length EGFR and the EGFRvIII mutant.

[0054] In one embodiment, the EREG biomarker-specific reagent is an antibody. Non-specific examples of EREG-specific antibodies are shown in Table 3. TIFF0007855525000003.tif112170

[0055] In one embodiment, the EREG biomarker-specific reagent is a monoclonal antibody selected from Table 3.

[0056] In one embodiment, the AREG biomarker-specific reagent is an antibody. Non-specific examples of AREG-specific antibodies are shown in Table 4. TIFF0007855525000004.tif71170

[0057] In one embodiment, the AREG biomarker-specific reagent is selected from Table 4.

[0058] C2. Antigen retrieval Immobilization chemically alters the substituents of a sample. This can change the ability of a biomarker-specific reagent to specifically bind to that biomarker. In some cases, the effects of immobilization can be overcome by treating the sample before contact with the biomarker-specific reagent; this process is commonly called antigen retrieval. Antigen retrieval can be achieved by physical methods, chemical methods, or a combination of both. Examples of antigen retrieval methods are detailed in Shi et al. (J Histochemistry & Cytochemistry, 2011, 59:13-32), D'Amico et al. (J Immunological Methods, 2009, 341:1-18), as well as McNicoll and Richmond (Histopathology, 1998, 32:97-103), and in U.S. Patents 9,506,928 and 6,544,798. In one example, antigen retrieval can be achieved by treating the sample with a protease (e.g., trypsin, DNase, proteinase K, pepsin, pronase, ficin, etc.) (known as protease-induced epitope retrieval (PIER)). In another example, the fixed sample is heated while in contact with a buffer solution (known as heat-induced epitope retrieval (HIER)). The HIER technique can be optimized by varying the temperature (e.g., up to approximately 100°C), time (typically up to 30 minutes), and / or pH (e.g., in the range of approximately pH 6 to pH 10). Examples of HIER solutions include citrate buffer solution (e.g., pH approximately 6), ethylenediaminetetraacetic acid (EDTA) solution (e.g., pH approximately 8), tris(hydroxymethyl)aminomethane (Tris)-EDTA buffer (e.g., pH approximately 9), Tris buffer (e.g., pH approximately 10), glycine-HCl buffer, periodic acid, urea, lead thiocyanate solution, etc.

[0059] In one embodiment, a simplex method is provided in which antigen retrieval conditions are selected and optimized for each set of biomarker-specific reagents applied to each individual tissue section. In another embodiment, a multiplex method is provided in which the set of biomarker-specific reagents includes one or more of the EGFR biomarker-specific reagent, the EREG biomarker-specific reagent, and the AREG biomarker-specific reagent, and antigen retrieval conditions compatible with each biomarker-specific reagent in the set are selected for the tissue section to be stained.

[0060] Regardless of these examples, the present disclosure does not require a specific antigen retrieval step, as long as the tissue sample obtained is compatible with the reagents used for histochemical staining of the sample for the biomarker of interest, and for subsequent microscopic evaluation or digital imaging of the stained sample.

[0061] C3. Detection Scheme In the histochemical method described herein, the biomarker-specific reagent facilitates the detection of the biomarker by mediating the attachment of a detectable portion near the biomarker to which the biomarker-specific reagent binds in the sample.

[0062] In one embodiment, the detectable portion is directly conjugated to a biomarker-specific reagent, and therefore adheres to the sample when the biomarker-specific reagent binds to its target. Such a detection scheme is called a "direct detection method."

[0063] In other embodiments, the attachment of detectable portions is brought into contact with one or more detection reagents, the biomarker-specific reagents interacting with each other such that the detectable portions attach to the sample near where the biomarker-specific reagent binds, but not far away from the binding site. Such a detection scheme is called an "indirect detection method."

[0064] In one embodiment, an indirect detection method is used, and the detectable portion adheres to the biomarker-specific reagent via an enzymatic reaction localized to the biomarker-specific reagent. Suitable enzymes for such reactions are well known and include, but are not limited to, oxidoreductases, hydrolases, phosphatases, and peroxidases. Specific enzymes clearly included are horseradish peroxidase (HRP), alkaline phosphatase (AP), acid phosphatase, glucose oxidase, β-galactosidase, β-glucuronidase, and β-lactamase. The enzyme may be directly conjugated to the biomarker-specific reagent or indirectly associated with the biomarker-specific reagent via a labeled conjugate. As used herein, “labeled conjugate” includes: (a) a specific binder; and (b) an enzyme conjugated to the specific binder that is reactive with a chromogenic substrate, a signaling conjugate, or an enzyme-reactive dye under suitable reaction conditions resulting in in situ production of the dye and / or adhesion of the dye to a tissue sample.

[0065] In non-limiting examples, the specific binding agent for the labeled conjugate may be a secondary detection reagent (e.g., a species-specific secondary antibody conjugated to a primary antibody, an anti-hapten antibody conjugated to a hapten conjugate primary antibody, or a biotin-binding protein conjugated to a biotinylated primary antibody), a tertiary detection reagent (e.g., a species-specific tertiary antibody conjugated to a secondary antibody, an anti-hapten antibody conjugated to a hapten conjugate secondary antibody, or a biotin-binding protein conjugated to a biotinylated secondary antibody), or other such configurations.

[0066] Haptens are molecules, typically small molecules, that can specifically combine with or bind to antibodies but are substantially immune to immunogenicity except when combined with a carrier molecule. Many haptens are known and frequently used for analytical procedures, including dinitrophenyl (DNP), biotin, digoxigenin (DIG), fluorescein, rhodamine, or those disclosed in their entirety in U.S. Patent No. 7,695,929, which is incorporated herein by reference. Ventana Medical Systems, Inc., the assignee of this application, has specially developed other haptens, including haptens selected from oxazoles, pyrazoles, thiazoles, nitroaryls, benzofurans, triterpenes, ureas, thioureas, rotenoids, coumarins, cyclolignans, and combinations thereof. Examples of specific haptens include benzofurazans, nitrophenyls, 4-(2-hydroxyphenyl)-1H-benzo[b][1,4]diazepine-2(3H)-ones, and haptens containing 3-hydroxy-2-quinoxaline carbamides. Multiple different haptens may be coupled to form polymer carriers. Furthermore, compounds such as haptens may be coupled to other molecules using linkers such as NHS-PEG linkers.

[0067] The enzyme, thus localized to the biomarker-specific reagent bound to the sample, can then be used in several schemes for attaching the detectable portion.

[0068] In some embodiments, the enzyme reacts with a chromogenic compound / substrate. Specific non-limiting examples of chromogenic compounds / substrates include 4-nitrophenyl phosphate (pNPP), Fast Red, bromochloroindolyl phosphate (BCIP), nitrobluetetrazolium (NBT), BCIP / NBT, Fast Red, AP Orange, AP Blue, tetramethylbenzidine (TMB), 2,2'-azino-di-[3-ethylbenzothiazoline sulfonate] (ABTS), o-dianisidine, 4-chloronaphthol (4-CN), nitrophenyl-β-D-galactopyranoside (ONPG), Examples include o-phenylenediamine (OPD), 5-bromo-4-chloro-3-indolyl-β-galactopyranoside (X-Gal), methylumbelliferyl-β-D-galactopyranoside (MU-Gal), p-nitrophenyl-α-D-galactopyranoside (PNP), 5-bromo-4-chloro-3-indolyl-β-D-glucuronide (X-Gluc), 3-amino-9-ethylcarbazole (AEC), fuchsin, iodonitrotetrazolium (INT), tetrazolium blue, or tetrazolium violet.

[0069] In some embodiments, enzymes can be used in metallographic detection schemes. Metallographic detection methods include using an enzyme, such as alkaline phosphatase (AP), in combination with a water-soluble metal ion and a redox-inactive substrate for the enzyme. In some embodiments, the substrate is converted by the enzyme into a redox activator, which reduces the metal ion and forms a detectable precipitate (see, for example, U.S. Patent Application No. 11 / 015,646, PCT Publication No. 2005 / 003777, filed December 20, 2004, and U.S. Patent Application Publication No. 2004 / 0265922; each of which is incorporated herein by reference in whole). Metallographic detection methods may also include using an oxidoreductase enzyme (e.g., horseradish peroxidase) in combination with a water-soluble metal ion, an oxidizing agent, and a reducing agent to form a detectable precipitate (see, for example, U.S. Patent No. 6,670,113, which is incorporated herein by reference in whole).

[0070] In some embodiments, the enzymatic reaction occurs between the enzyme and the dye itself, converting the dye from an unbound species to a species that adheres to the sample. For example, the reaction of DAB with a peroxidase (e.g., horseradish peroxidase) oxidizes the DAB, causing it to precipitate.

[0071] In yet another embodiment, the detectable portion attaches via a signaling conjugate containing a potentially reactive moiety configured to react with an enzyme to form a reactive species that can bind to the sample or other detectable component. These reactive species can react with the sample proximal to their formation, i.e., near the enzyme, but are rapidly converted to non-reactive species distal to the site where the enzyme attaches, so that the signaling conjugate does not attach thereto. Examples of potentially reactive moieties include quinone methide (QM) analogs, such as those described in WO2015124703A1, and tyramide conjugates, such as those described in WO2012003476A2, each of which is incorporated herein by reference in whole. In some examples, the potentially reactive moiety is directly conjugated to a dye such as N,N'-biscarboxypentyl-5,5'-disulfonato-indodicarbocyanine (Cy5), 4-(dimethylamino)azobenzene-4'-sulfonamide (DABSYL), tetramethylrhodamine (DISCOVERY Purple, Ventana, Tucson, AZ), or rhodamine 110 (rhodamine). In other examples, the potentially reactive moiety is conjugated to one member of a specific binding pair, and the dye is linked to the other member of the specific binding pair. In other examples, the potentially reactive moiety is linked to one member of a specific binding pair, and the enzyme is linked to the other member of the specific binding pair, and the enzyme is reactive to a chromogenic substrate so as to result in (a) the formation of a dye, or (b) the attachment of a dye (e.g., DAB).Examples of specific binding pairs include: (1) a biotin-binding entity (e.g., avidin, streptavidin, deglycosylated avidin (e.g., NEUTRAVIDIN), or a biotin-binding protein (e.g., CAPTAVIDIN) having nitrated tyrosine at the biotin-binding site, linked to a biotin-binding site) (for example, if the dye is a DAB, a peroxidase linked to a biotin-binding protein); and (2) an anti-hapten antibody (e.g., if the dye is a DAB, a peroxidase linked to an anti-hapten antibody), linked to a hapten (e.g., if the dye is a DAB), linked to a hapten (e.g., a peroxidase linked to an anti-hapten antibody).

[0072] Table 5 specifically includes non-limiting examples of combinations of biomarker-specific reagents and detection reagents. TIFF0007855525000005.tif250170TIFF0007855525000006.tif210170TIFF0007855525000007.tif210170TIFF0007855525000008.tif202170 TIFF0007855525000009.tif241170TIFF0007855525000010.tif232170TIFF0007855525000011.tif180170TIFF0007855525000012.tif250170

[0073] Non-exclusive examples of commercially available detection reagents or kits containing detection reagents include: the VENTANA ultraView detection system (secondary antibody conjugated to enzymes containing HRP and AP), the VENTANA iVIEW detection system (biotinized anti-species secondary antibody and streptavidin-conjugated enzyme), the VENTANA OptiView detection system (OptiView) (anti-species secondary antibody conjugated to a hapten and anti-hapten tertiary antibody conjugated to an enzyme polymer), the VENTANA amplification kit (a non-conjugated secondary antibody that can be used with any of the aforementioned VENTANA detection systems to amplify the number of enzymes attached to the primary antibody binding site), the VENTANA OptiView amplification system (anti-species secondary antibody conjugated to a hapten, anti-hapten tertiary antibody conjugated to an enzyme polymer, and tyramide conjugated to the same hapten); and VENTANA DISCOVERY (e.g., DISCOVERY Yellow Kit, DISCOVERY Purple Kit, DISCOVERY Silver Kit, DISCOVERY Red Kit, DISCOVERY Rhodamine Kit, etc.), DISCOVERY OmniMap, DISCOVERY UltraMap anti-hapten antibodies, secondary antibodies, chromogens, fluorophores, and dye kits; PowerVision and PowerVision+IHC detection systems (where secondary antibodies are directly polymerized with HRP or AP to form dense polymers with a high ratio of enzyme to antibody); and DAKO EnVision®+System (enzyme-labeled polymers conjugated to secondary antibodies).

[0074] C4. Automation Systems In one embodiment, the histochemical staining methods described herein are performed using an automated IHC staining device. Specific examples of automated IHC staining devices include: intelliPATH (Biocare Medical), WAVE (Celerus Diagnostics), DAKO OMNIS and DAKO AUTOSTAINER LINK 48 (Agilent Technologies), BENCHMARK XT (Ventana Medical Systems, Inc.), BENCHMARK Special Stains (Ventana Medical Systems, Inc.), BENCHMARK ULTRA (Ventana Medical Systems, Inc.), BENCHMARK GX (Ventana Medical Systems, Inc.), DISCOVERY XT (Ventana Medical Systems, Inc.), DISCOVERY ULTRA (Ventana Medical Systems, Inc.), Leica BOND, and Lab Vision Autostainer (Thermo Scientific). Automated IHC staining devices are also described in Prichard, Overview of Automated Immunohistochemistry, Arch Pathol Lab Med., Vol. 138, pp. 1578–1582 (2014), which is incorporated herein by reference in whole. Furthermore, Ventana Medical Systems, Inc. is the assignee of several U.S. patents disclosing systems and methods for performing automated analysis, including U.S. Patents 5,650,327, 5,654,200, 6,296,809, 6,352,861, 6,827,901, and 6,943,029, as well as U.S. Published Patent Applications 20030211630 and 20040052685, each incorporated herein by reference in whole. The methods of the present invention may be adapted to be carried out with any suitable automated IHC staining device.

[0075] Automated IHC staining devices typically implement a staining protocol via a stainer unit that dispenses reagents onto a slide containing the sample to be stained. Commercial staining units typically operate on one of the following principles: (1) Open individual slide staining, where the slide is positioned horizontally and the reagent is distributed as a paddle onto the surface of the slide containing the tissue sample (e.g., implemented by DAKO AUTOSTAINER Link 48 (Agilent Technologies) and intelliPATH (Biocare Medical) stainers); (2) Liquid overlay technique, where the reagent is covered by or distributed through a layer of inert fluid deposited on the sample (e.g., implemented in VENTANA BenchMark and DISCOVERY stainers); (3) Capillary gap staining, where the slide surface is positioned parallel and close to another surface (which may be another slide or cover plate) to create a narrow gap through which capillary force draws up the liquid reagent and keeps it in contact with the sample (e.g., the staining principle used by DAKO TECHMATE, Leica BOND, and DAKO OMNIS stainers). Even after several repetitions of capillary gap staining, the fluid within the gap does not mix (e.g., in DAKO TECHMATE and Leica BOND). In some variations of capillary gap staining, the reagents are mixed in the gap; for example, in parallel motion gap technique, a gap is created between the slide and the curved surface, and the movement of the surfaces relative to each other brings about mixing (see US7,820,381); in dynamic gap staining, the sample is applied to the slide using capillary forces similar to those used in capillary gap staining, and then the reagents are agitated during incubation by moving the parallel surfaces relative to each other (e.g., the staining principle implemented in DAKO OMNIS slide stainers (Agilent)). More recently, the use of inkjet technology to deposit reagents onto the slide has been proposed. See WO2016-170008A1.This list of staining principles is not intended to be exhaustive, and the methods and systems described herein are intended to include any staining techniques (both known and those to be developed in the future) that may be used to apply appropriate reagents to a sample.

[0076] The present invention is not limited to the use of automated systems. In some embodiments, the histochemical labeling methods described herein are applied manually. Alternatively, certain steps may be performed manually and others performed in an automated system.

[0077] C5. Counterstaining and Morphological Staining If desired, biomarker-stained slides may be counterstained to aid in the identification of morphologically relevant areas and / or to identify regions of interest (ROIs). Examples of counterstains include chromogenic nuclear counterstains such as hematoxylin (stains blue to purple), methylene blue (stains blue), toluidine blue (stains nuclei dark blue and polysaccharides pink to red), nucleafast red (also known as Kernechtrot dye, stains red), and methyl green (stains green); non-nucleochromogenic stains such as eosin (stains pink); 4',6-diamino-2-phenylindole (DAPI, stains blue), propidium iodide (stains red), Hoechst stain (stains blue), nuclea green DCS1 (stains green), and nuclea yellow (Hoechst Examples include fluorescent nuclear stains such as S769121 (stains yellow under neutral pH and blue under acidic pH), DRAQ5 (stains red), and DRAQ7 (stains red); and fluorescent non-nuclear stains such as fluorophore-labeled phalloidin (stains filamentous actin, the color depending on the conjugated fluorophore).

[0078] In certain embodiments, serial sections of biomarker-stained sections (or the biomarker-stained sections themselves) may be morphologically stained. Basic morphological staining techniques often rely on staining the nuclear structure with a first dye and the cytoplasmic structure with a second stain. Many morphological stains are known, including but not limited to hematoxylin and eosin (H&E) stains and Lee stains (methylene blue and basic fuchsin). Examples of commercially available H&E (Heat and Effect) stainers include the VENTANA SYMPHONY (individual slide stainer) and VENTANA HE 600 (individual slide stainer) H&E stainers by Roche; the Dako CoverStainer (batch stainer) by Agilent Technologies; and the Leica ST4020 Small Linear Stainer (batch stainer), Leica ST5020 Multistainer (batch stainer), and Leica ST5010 Autostainer XL series (batch stainers) H&E stainers by Leica Biosystems Nussloch GmbH.

[0079] D. Multiplex staining method As described above, in one embodiment, the colorectal sample is stained by the multiplex method. The multiplex method involves differential staining of different biomarkers in a single tissue section.

[0080] One method for achieving differential staining of different biomarkers is to select a combination of biomarker-specific reagents and detection reagents that does not result in off-target cross-reactivity between different antibodies or detection reagents (referred to as "combination staining"). In such examples, all biomarker-specific reagents are conjugated to the sample before any of the detection reagents are applied. In these examples, the biomarker-specific reagents and detection reagents must be selected such that, regardless of whether both biomarker-specific reagents are present, the first set of detection reagents reacts only with the first biomarker-specific reagent, and the second set of detection reagents reacts only with the second biomarker-specific reagent. Therefore, for example, if the biomarker-specific reagent is an antibody, the EGFR antibody may be selected from a first species (e.g., mouse anti-human EGFR monoclonal antibody, rat anti-human EGFR monoclonal antibody, or rabbit anti-human EGFR monoclonal antibody), the EREG antibody may be selected from a second species (e.g., mouse anti-human EREG monoclonal antibody, rat anti-human EREG monoclonal antibody, or rabbit anti-human EREG monoclonal antibody, provided that the second species is different from the first species of antibody), and the AREG antibody may be selected from a third species (e.g., mouse anti-human AREG monoclonal antibody, rat anti-human AREG monoclonal antibody, or rabbit anti-human AREG monoclonal antibody, provided that the third species is different from the first and second species). In such embodiments, secondary antibodies having different species specificities may be provided to enable differential staining of different targets. In another embodiment, tagged biomarker-specific reagents may be used (e.g., those with hapten tags, those with epitope tags, etc.). In such cases, different tags on different biomarker-specific reagents facilitate the binding of different detection reagent sets to the sample. Therefore, for example, if the biomarker-specific reagents are antibodies, they may be coupled to different hapten tags or epitope tags, and the secondary antibody may be selected to specifically bind to the hapten tag or epitope tag.Furthermore, each detection reagent set should be adapted to attach different detectable entities to the section, for example, by attaching different enzymes near each specific binder. Such a configuration has the potential advantage of allowing each set of biomarker-specific reagents and associated detection reagents to be present on the sample simultaneously, and / or performing staining using a cocktail of biomarker-specific reagents and / or detection reagents, thereby reducing the number of staining steps. However, such a configuration is not always feasible because reagents may cross-react with different enzymes, and various antibodies may cross-react with each other, leading to abnormal staining.

[0081] Another method for achieving differential labeling of different biomarkers is to sequentially stain the sample for each biomarker. In such embodiments, a first biomarker-specific reagent is reacted with the section, followed by the adhesion of a first detectable portion with a secondary detection reagent and other detection reagents to the first biomarker-specific reagent. The section is then processed to remove the biomarker-specific reagent and associated detection reagents from the section while leaving the adhered stain intact. This process is repeated for subsequent biomarker-specific reagents. Examples of methods for removing biomarker-specific reagents and associated detection reagents include heating the sample in the presence of a buffer that elutes the antibody from the sample (known as the "heat-kill method"), such as those disclosed by Stack et al., Multiplexed immunohistochemistry, imaging, and quantitation: A review, with an assessment of Tyramide signal amplification, multispectral imaging and multiplex analysis, Methods, Vol. 70, No. 1, pp. 46-58 (November 2014), and PCT / EP2016 / 057955, which are incorporated by reference.

[0082] As those skilled in the art will understand, combined staining and sequential staining methods may be combined. For example, if only a subset of biomarker-specific reagents are compatible with combined staining, the sequential staining method may be modified so that the biomarker-specific reagents compatible with combined staining are applied to the sample using the combined staining method, and the remaining antibodies are applied using the sequential staining method.

[0083] In one embodiment, a multiplex method is provided which includes contacting a single tissue section of an FFPE colorectal tumor specimen with the following: • A human EGFR protein biomarker-specific reagent, and a detection reagent sufficient to attach the first chromogen to the vicinity of the EGFR protein biomarker-specific reagent bound to the tissue section; and • A human AREG protein biomarker-specific reagent, and a detection reagent sufficient to attach a second chromogen to the vicinity of the human AREG protein biomarker-specific reagent bound to a tissue section. In one embodiment, a multiplex method is provided which includes contacting a single tissue section of an FFPE colorectal tumor specimen with the following: • A human EGFR protein biomarker-specific reagent, and a detection reagent sufficient to attach the first chromogen to the vicinity of the EGFR protein biomarker-specific reagent bound to the tissue section; and • A human EREG protein biomarker-specific reagent, and a detection reagent sufficient to attach a second chromogen to the vicinity of the EREG protein biomarker-specific reagent bound to a tissue section. In one embodiment, a multiplex method is provided which includes contacting a single tissue section of an FFPE colorectal tumor specimen with the following: • A human EGFR protein biomarker-specific reagent, and a detection reagent sufficient to attach the first chromogen to the vicinity of the EGFR protein biomarker-specific reagent bound to the tissue section; and A human EREG protein biomarker-specific reagent, a human AREG protein biomarker-specific reagent, and a detection reagent sufficient to attach a second chromogen to the human EREG protein biomarker-specific reagent and the human AREG protein biomarker-specific reagent bound to a tissue section. In one embodiment, a multiplex method is provided which includes contacting a single tissue section of an FFPE colorectal tumor specimen with the following: • A human EGFR protein biomarker-specific reagent, and a detection reagent sufficient to attach the first chromogen to the vicinity of the EGFR protein biomarker-specific reagent bound to the tissue section; and • A human EREG protein biomarker-specific reagent, and a detection reagent sufficient to attach a second chromogen to the vicinity of the human EREG protein biomarker-specific reagent bound to a tissue section; and • A human AREG protein biomarker-specific reagent, and a detection reagent sufficient to attach a third chromogen to the vicinity of the human AREG protein biomarker-specific reagent bound to a tissue section. In these exemplary embodiments, biomarkers may be labeled in a specific order as desired. For example, EGFR may be labeled first before one or more EGFR ligands. Or, one or both EGFR ligands may be labeled before EGFR. Or, one EGFR ligand may be labeled before EGFR, and one EGFR ligand may be labeled after EGFR. The ease of detection of detectable parts (e.g., chromogens) may influence the order in which they are used. For example, the most easily detectable part (e.g., chromogen) may be selected for the biomarker with the lowest abundance. Similarly, the least detectable part (e.g., chromogen) may be selected for the biomarker with the highest abundance.

[0084] The detectable region (e.g., chromogen) used to detect EGFR may be different from the detectable region (e.g., chromogen) used to detect AREG and / or EREG. In some embodiments, the detectable region (e.g., chromogen) used to detect EGFR is the same as the detectable region (e.g., chromogen) used to detect AREG. In some embodiments, the detectable region (e.g., chromogen) used to detect EGFR is the same as the detectable region (e.g., chromogen) used to detect EREG. In some cases, the degree of ligand expression (regardless of identity) may be predictive. In some embodiments, the detectable region (e.g., chromogen) used to detect AREG is the same as the detectable region (e.g., chromogen) used to detect EREG.

[0085] III. Image Processing and Analysis In one embodiment, digital images of stained tissue sections obtained according to the method described above may be acquired. After staining the tissue sections, the samples may be subjected to image acquisition, as well as image processing and analysis. The digital images may be useful, for example, for long-term archiving of test results and / or digital analysis of staining patterns. In another embodiment, the digital images may be used in a digital analysis of a cohort of patient-derived tumors with known outcomes to develop a scoring algorithm for evaluating the expression of EGFR and EGFR ligands. In yet another embodiment, the digital images may be fed into a diagnostic analysis system trained to help evaluate stained samples to predict the response to EGFR-targeted therapy.

[0086] A. Image acquisition Tissue sections are transferred to an imaging apparatus or image acquisition system to obtain digital images of the tissue sections. The image acquisition system may include a scanning platform such as a slide scanner, which can scan stained slides at 20x, 40x, or other magnifications to produce high-resolution digital images of the entire slide. At a basic level, a typical slide scanner includes at least: (1) a microscope with an objective lens, (2) a light source (depending on the dye, such as halogen, light-emitting diode, white light, and / or multi-wavelength light sources), (3) robotics for moving the slide glass (or for moving the optics relative to the slide), (4) one or more digital cameras for capturing images, and (5) a computer and associated software for controlling the robotics and for operating, managing, and viewing the digital slides. Digital data at several different XY positions on the slide (and possibly in multiple Z planes) is captured by the camera's charge-coupled device (CCD), and the images are combined into one to form a composite image of the entire scanned surface. Common methods to achieve this include: (1) tile-based scanning, where the slide or optics is moved very slightly to capture image frames of squares that slightly overlap adjacent squares. The captured squares are then automatically matched with each other to create a composite image; and (2) line-based scanning, where the slide is moved in a single axis direction during acquisition to capture several composite image "strips." These image strips can then be matched with each other to form a larger composite image.

[0087] A detailed overview of various scanners (both fluorescence and brightfield) can be found in Farahani et al., Whole slide imaging in pathology: advantages, limitations, and emerging perspectives, Pathology and Laboratory Medicine Int'l, Vol. 7, pp. 23-33 (June 2015), the entire content of which is referenced. Examples of commercially available slide scanners include: 3DHistech PANNORAMIC SCAN II; DigiPath PATHSCOPE; Hamamatsu NANOZOOMER RS, HT, and XR; Huron TISSUESCOPE 4000, 4000XT, and HS; Leica SCANSCOPE AT, AT2, CS, FL, and SCN400; Mikroscan D2; Olympus VS120-SL; Omnyx VL4, and VL120; PerkinElmer LAMINA; Philips ULTRA-FAST SCANNER; Sakura Finetek VISIONTEK; Unic PRECICE 500, and PRECICE 600x; VENTANA ISCAN COREO and ISCAN HT; and Zeiss AXIO SCAN.Z1. Other exemplary systems and features can be found, for example, in WO2011-049608, or in U.S. Patent Application No. 61 / 533,114, filed September 9, 2011, entitled "IMAGING SYSTEMS, CASSETTES, AND METHODS OF USING THE SAME," the contents of which are incorporated by reference in their entirety.

[0088] Images generated by the scanning platform can be transferred to an image analysis system, a server or database accessible by the image analysis system, or a non-temporary digital storage medium. In some embodiments, images can be automatically transferred over one or more local area networks and / or wide area networks. In some embodiments, the image analysis system may be integrated with or included in the scanning platform and / or other modules of the image acquisition system, in which case images can be transferred to the image analysis system. In some embodiments, the image acquisition system may not be communicatively connected to the image analysis system, in which case images may be stored in any type of non-volatile storage medium (e.g., a flash drive) and downloaded from the medium to the image analysis system, or to a server or database communicatively connected to it.

[0089] B. Image Analysis In one embodiment, the digital image is analyzed by an image analysis system. In such an embodiment, the image acquired as described above is processed by an image analysis system which includes at least a processor and a memory connected to the processor, the memory being for storing computer executable instructions that, when executed by the processor, cause the processor to perform an operation.

[0090] An image analysis system may feature one or more computing devices, such as a desktop computer, laptop computer, tablet, smartphone, server, purpose-specific computing device, or any other type of electronic device capable of performing the techniques and operations described herein. In some embodiments, the image analysis system may be implemented as a single device. In other embodiments, the image analysis system may be implemented as a combination of two or more devices. For example, the image analysis system may include one or more server computers and one or more client computers that are connected to each other in a communicative manner via one or more local area networks and / or wide area networks such as the Internet.

[0091] The image analysis system may include memory, a processor, and a display. The memory may include any combination of any type of volatile or non-volatile memory, such as random access memory (RAM), read-only memory such as electrically erasable programmable read-only memory (EEPROM), flash memory, hard drives, solid-state drives, optical discs, etc. The processor may include one or more processors of any type, such as a central processing unit (CPU), graphics processing unit (GPU), special-purpose signal processor or image processor, field-programmable gate array (FPGA), tensor processing unit (TPU), etc.

[0092] Displays using any suitable technology such as LCD, LED, OLED, TFT, or plasma can be implemented. In some implementations, the display may be a touch-sensitive display (touchscreen).

[0093] Images generated by the scanning platform may be transferred to an image analysis system, or to a server or database accessible by the image analysis system. In some embodiments, images may be transferred automatically over one or more local area networks and / or wide area networks. In some embodiments, the image analysis system may be integrated with or included in the scanning platform and / or other modules of the image acquisition system, in which case images may be transferred to the image analysis system. In some embodiments, the image acquisition system may not be communicatively connected to the image analysis system, in which case images may be stored on any type of non-volatile storage medium (e.g., flash drive, hard drive, etc.) and downloaded from the medium to the image analysis system, or to a server or database communicatively connected to it.

[0094] Those skilled in the art will understand that the biological image analysis devices described herein may be included in a system comprising further components, such as analyzers, scanners, etc. For example, a biological image analyzer may be communicatively connected to a computer-readable storage medium containing a digital copy of an image of a biological sample. Alternatively, a biological image analysis device may be communicatively connected to an imaging apparatus.

[0095] Those skilled in the art will understand that further modules or databases may be incorporated into the workflow. For example, an image processing module may be run to apply a specific filter to the acquired image or to identify a specific histological and / or morphological structure in the tissue sample. Furthermore, a region of interest (ROI) selection module may be used to select a specific portion of the image for analysis. Similarly, an unmixing module may be run to provide image channel images corresponding to a specific stain or biomarker.

[0096] The image analysis system may also include object identifiers, ROI generators, user interface modules, and / or scoring engines. Those skilled in the art will understand that each module may be implemented as several submodules, and that any two or more modules may be combined into a single module. Furthermore, in some embodiments, the system may include additional engines and modules (e.g., input devices, networking and communication modules). Exemplary commercially available software packages useful for implementing the modules disclosed herein include the VENTANA VIRTUOSO software suite (Ventana Medical Systems, Inc.); the TISSUE STUDIO, DEVELOPER XD, and IMAGE MINER software suites (Definiens); the BIOTOPIX, ONCOTOPIX, and STEREOTOPIX software suites (Visiopharm); and the HALO platform (Indica Labs, Inc.).

[0097] For biomarkers scored based on their association with a specific type of object (e.g., a membrane), the features extracted by the object identifier may include features or feature vectors sufficient to categorize objects in a sample as biomarker-positive objects or biomarker-negative markers of interest, and / or to categorize objects by the level or intensity of biomarker staining. If biomarkers can be weighted differently depending on the type of object expressing them, the features extracted by the object identifier may include features relevant to determining the type of object associated with the biomarker-positive pixel. Thus, objects can be categorized at least based on biomarker expression (e.g., biomarker-positive or biomarker-negative cells), and, where relevant, based on the object's subtype (e.g., tumor cells). If the degree of biomarker expression is scored regardless of its association with an object, the features extracted by the object identifier may include, for example, the location and / or intensity of the biomarker-positive pixel. The exact features extracted from the image depend on the type of classification function applied and are well known to those skilled in the art.

[0098] The image analysis system can also provide images to an ROI generator. The ROI generator can be used to identify a single or multiple ROIs in an image, from which a score is calculated. In some cases, the object identifier may not apply to the entire image, and the single or multiple ROIs generated by the ROI generator may be used to define a subset of the image in which the object identifier operates.

[0099] Object identifiers and ROI generators can be implemented in any order. For example, object identifiers may be applied to the entire image first. Then, the location and characteristics of the identified objects may be stored and the ROI generator may be called once it is implemented. Alternatively, the ROI generator may be implemented first. In this case, object identifiers may be implemented only for ROIs, or still for the entire image. It may also be possible to implement object identifiers and ROI generators simultaneously.

[0100] In one embodiment, the memory of the image analysis system instructs the processor to perform a set of functions including: (a) unmixing the digital images of the stained slides described herein to obtain deconvoluted images of each pigment used to stain the slides (and optionally counterstains used to stain the slides); and (b) identifying one or more objects of interest in the deconvoluted images and extracting one or more object metrics from the objects of interest. In one embodiment, the set of objects and associated object metrics may be used, for example, to develop a predictive scoring algorithm for identifying patients responsive to EGFR-targeted therapy. In another embodiment, the image analysis system may further comprise a scoring engine which applies a predictive scoring function to a feature vector including a set of object metrics for human EGFR protein in a colorectal tumor of interest and a set of object metrics for either or both human AREG protein and human EREG protein in a colorectal tumor of interest, the output of which is a score indicating whether the colorectal tumor is likely to respond to EGFR-targeted therapy. In another embodiment, the memory of the image analysis system instructs the processor to execute a set of functions that include performing a scoring guide on an image, the scoring guide comprising several classifiable subsets, the classifiable subsets being based on the application of a clustering function to several extracted features of several objects of interest.

[0101] B1. Unmixing Unmixing is the procedure of decomposing the measured spectrum of a mixed pixel into a set of component spectra present within that pixel and a corresponding set of fractions indicating the proportion of each component spectrum. Specifically, the unmixing process can extract stain-specific channels and determine the local concentration of individual stains using reference spectra that are well known for standard types of tissue and stain combinations. Unmixing may use reference spectra read from a control image or estimated from an observed image. Unmixing the component signals of each input pixel allows for the readout and analysis of stain-specific channels, such as hematoxylin and eosin channels in H&E images, or diaminobenzidine (DAB) and counterstain (e.g., hematoxylin) channels in IHC images. Terms such as "unmixing" and "color deconvolution" (or "deconvolution") (e.g., "deconvolving," "unmixed") are used synonymously in the art. To decompose each pixel of an RGB image into a set of constituent stains and the fractional contribution from each of them, several techniques have been proposed, including (but not limited to) the processes described by Ruifrok et al. (Anal.Quant.Cytol.Histol., 2001, 23:291~299), Chen and Srinivas (Comput Med Imaging Graph, 2015, 46(1):30~39), Kesheva (Lincoln Laboratory Journal, 2003, 14:55~78), Greer (IEEE Trans Image Proc., 2012, 221:219~228), and Yang et al. (IEEE Trans.Image Proc., 2011, 20:1112-1125).

[0102] In one embodiment, the digital images acquired as described above are deconvolved into separate images deconvolved for each chromogen. Thus, for example, a multiplex stained slide can be prepared in which the slide is stained with a first chromogen for EGFR and at least a second chromogen for one or more of EREG and AREG, and the digital image of the stained slide can be deconvolved based on the channels for each chromogen.

[0103] B2. Object Identification In one embodiment, an object is identified in the deconvoluted image. The "object" is a structure or staining pattern in a tumor sample used to evaluate and quantify biomarker staining. Examples include: biomarker-positive cells (e.g., EGFR-positive cells, AREG-positive cells, EREG-positive cells, and / or EGFR ligand-positive cells); biomarker-positive membranes (e.g., EGFR-positive membranes, AREG-positive membranes, EREG-positive membranes, and / or EGFR ligand-positive membranes); biomarker-positive spotted membrane staining patterns (e.g., EGFR-positive spotted staining, AREG-positive spotted staining, EREG-positive spotted staining, and / or EGFR ligand-positive spotted staining); biomarker-positive cytoplasm (e.g., EGFR-positive cytoplasm, AREG-positive cytoplasm, EREG-positive cytoplasm, and / or EGFR ligand-positive cytoplasm); biomarker-positive cell clusters (e.g., regions exceeding a predefined area having a density of biomarker-positive cells exceeding a predefined threshold, e.g., EGFR-positive cell clusters). Examples include: stars, AREG-positive cell clusters, EREG-positive cell clusters, and / or EGFR ligand-positive cell clusters; biomarker-positive tumor cells (e.g., EGFR-positive tumor cells, AREG-positive tumor cells, EREG-positive tumor cells, and / or EGFR ligand-positive tumor cells); biomarker-positive membranes associated with tumor cells (e.g., EGFR-positive membranes associated with tumor cells, AREG-positive membranes associated with tumor cells, EREG-positive membranes associated with tumor cells, and / or EGFR ligand-positive membranes associated with tumor cells); biomarker-positive cytoplasm associated with tumor cells (e.g., EGFR-positive cytoplasm associated with tumor cells, AREG-positive cytoplasm associated with tumor cells, EREG-positive cytoplasm associated with tumor cells, and / or EGFR ligand-positive cytoplasm associated with tumor cells); and so on.

[0104] In one embodiment, the image analysis system runs an object identifier function on one or more of the deconvoluted images to identify and mark relevant objects and other features in the images that can later be used for scoring. The object identifier can extract (or generate for each image) multiple image features that characterize various objects in the image, and pixels that represent the expression of a biomarker. The values ​​of multiple image features can be combined to form a high-dimensional vector, which will hereafter be referred to as the "feature vector" that characterizes the expression of the biomarker.

[0105] For biomarkers scored based on their association with a specific type of object (e.g., a membrane), the features extracted by the object identifier may include features or feature vectors sufficient to categorize objects in a sample as biomarker-positive or biomarker-negative objects of interest, and / or to categorize them by the level or intensity of biomarker staining. If the biomarker can be weighted differently depending on the type of object expressing it, the features extracted by the object identifier may include features relevant to determining the type of object associated with the biomarker-positive pixel. Thus, objects can be categorized at least based on biomarker expression (e.g., biomarker-positive or biomarker-negative cells), and, where relevant, based on the object's subtype (e.g., tumor cells). If the degree of biomarker expression is scored regardless of its association with an object, the features extracted by the object identifier may include, for example, the location and / or intensity of the biomarker-positive pixel. The exact features extracted from the image depend on the type of classification function applied and are well known to those skilled in the art.

[0106] In some embodiments, it may be desirable to limit the image analysis to a specific region of interest (ROI) that defines a biologically important area where a biomarker is detected and / or quantified. Common examples of morphological regions in tumor-containing tissue sections that may be considered ROIs include: the whole tumor (WT) region, the invasive margin (IM) region, the tumor core (TC) region, and the peri-tumoral (PT) region. In some embodiments, the ROI is identified in the image of the entire slide to detect all tissue regions within the ROI while limiting the amount of background non-tissue area analyzed. In some embodiments, the ROI is identified in a digital image (e.g., an H&E stained image) of a first serial section of the test specimen stained with a morphological stain, and the ROI is automatically aligned with a digital image of at least a second serial section of the test specimen stained with a different stain. In some embodiments, the ROI is identified in a digital image of a first serial section of the test specimen stained with H&E, and the ROI is automatically aligned with digital images of at least a second serial section, a third serial section of the test specimen, and a fourth serial section of the test specimen.

[0107] An ROI may be limited to a morphological region, extended to include regions outside the morphological region (i.e., by extending the periphery of the ROI outside the morphological region by a predetermined distance), or restricted to subregions of the morphological region (e.g., by shrinking the ROI inward by a predetermined distance around the morphological region, or by identifying regions within the ROI that have certain characteristics (e.g., baseline density of a particular cell type)). If the morphological region is an edge region, the ROI may be defined, for example, as all locations within a predetermined distance from any point on the edge, all locations on one side of the edge within a predetermined distance from any point on the edge, the smallest geometric region encompassing the entire edge region (e.g., a circle, ellipse, square, rectangle, etc.), or all locations within a circle with a predetermined radius centered on the center point of the edge region.

[0108] In relation to the biomarkers of this disclosure, the ROI may include biomarker-positive cell clusters or regions within a predetermined distance from biomarker-positive cell clusters (such as EGFR-positive tumor regions). In some embodiments, the same ROI may be used for all sections and biomarkers. For example, a morphologically defined ROI may be identified in H&E-stained sections of the sample and used for all biomarker-stained sections. In other embodiments, different ROIs may be used for different biomarkers (e.g., EGFR ligand analysis may be limited to EGFR-rich regions only, while EGFR may be identified throughout the entire tumor region).

[0109] In some embodiments, an ROI identification module can be used to select a portion of a biological sample from which an image or image data should be acquired, for example, a region of interest where fibroblasts are heavily concentrated. In some embodiments, the ROI is identified by a user of the system of this disclosure, or another system communicatively connected to the system of this disclosure. Alternatively, in other embodiments, the region selection module reads the location or identification information of the region or interest from storage / memory. In some embodiments, the ROI identification module automatically generates the ROI, for example, via a method described in PCT / EP2015 / 062015, the entirety of which is incorporated herein by reference. In some embodiments, the ROI is automatically determined by the system based on some predefined criteria or characteristics present in or relating to the image (for example, for a biological sample stained with three or more stains, it identifies an area in the image containing only two of the stains). The region selection module then outputs the ROI. In a particular embodiment, the ROI identification module generates a graphic user interface including a digital image, and a trained professional (such as a pathologist) manually draws one or more morphological regions in the digital image as ROIs. In other embodiments, a computer-implemented system may assist the user in annotating ROIs ("semi-automated ROI annotation"). For example, the user can draw one or more regions on a digital image, which the system then automatically converts into a complete ROI. For example, if the desired ROI is a WT region, the user can draw the WT region (e.g., by outlining or tracing), and the system applies a pattern recognition function using computer vision and machine learning to identify regions with similar morphological characteristics to the WT region. Many other configurations can also be used. When ROI generation is semi-automated, the user may be given the option to modify the ROI annotated by the computer system, for example, by zooming in on the ROI or annotating regions of the ROI or objects within the ROI to be excluded from analysis.In some embodiments, a pathologist annotates the tumor, and a software system is used to identify object metrics. In some embodiments, an image (of the tumor) is acquired, the image is scanned, the pathologist annotates the tumor / image, and then output is generated. In other embodiments, the computer system may automatically suggest ROIs without direct input from the user (referred to as "automated ROI annotation"). For example, a pre-trained tissue segmentation function or other pattern recognition function may be applied to an unannotated image to identify desired morphological regions for use as ROIs. The user may be given the option to modify the ROIs annotated by the computer system, for example, by zooming in on the ROI or annotating regions of the ROI or objects within the ROI to be excluded from the analysis.

[0110] In one embodiment, the ROI is directly annotated in the digital image of the biomarker-stained sample, in which case the ROI is carried over to the deconvoluted image. In another embodiment, the ROI is annotated in the digital image of serial sections of the biomarker-stained sample, and the annotated ROI is aligned with the digital image of the biomarker-stained sample. In such embodiments, the image analysis system may perform an alignment function that moves the annotation to adjacent slides while taking into account the position, orientation, and local deformation of the tissue sections. The alignment function may further include functions that allow the user to edit the annotation, for example, by enabling the movement of the annotation, rotation of the annotation, local modification of their contours, depiction of staining artifacts, etc. An exemplary alignment function is disclosed, for example, in US2016 / 0321495A1, which is incorporated herein by reference. In one embodiment, a set of images is provided generated from a simplex staining methodology, wherein serial sections of each simplex-stained sample are prepared, the serial sections are stained with a morphological stain (such as H&E), and the ROI is annotated onto a digital image of the morphologically stained sample and aligned with the biomarker-stained serial sections (or their deconvoluted images). In another embodiment, a set of images is provided generated from a multiplex staining methodology, wherein serial sections of a multiplex-stained sample are prepared, the serial sections are stained with a morphological stain (such as H&E), and the ROI is annotated onto a digital image of the morphologically stained sample and aligned with the biomarker-stained serial sections (or their deconvoluted images).

[0111] In one embodiment, object metrics are calculated by applying ROI metrics to raw object counts. Examples of ROI metrics that may be used to calculate object metrics include: the area of ​​the ROI; the total number of cells within the ROI; the total number of specific cell types within the ROI (e.g., tumor cells, immune cells, stromal cells, cells positive for a first biomarker, etc.); the length of the edges defining the ROI (e.g., the perimeter of the ROI, or the length of the midline bisecting the ROI); the number of cells defining the edges of the ROI, etc. Examples of object metrics related to selected objects are listed in Table 6. TIFF0007855525000013.tif250170TIFF0007855525000014.tif47170

[0112] The object metric may be based directly on the raw count within the ROI (hereinafter referred to as the "total metric"), or it may be based on the mean or median of the object metrics of multiple control regions within the ROI (hereinafter referred to as the "global metric"). These two methods are illustrated in Figure 1. In either case, an image of a slide is provided with the annotated ROI (represented as a region within a dashed line) and the identified object of interest. In the total metric method, the feature metric is calculated by quantifying the relevant metrics of all marked features within the ROI ("ROI object metrics") and dividing the ROI object metrics (e.g., the total number of marked objects or the total area of ​​marked biomarker expression) by the ROI metrics (e.g., the area of ​​the ROI, the total number of cells within the ROI) (Step A1). In the global metric method, multiple control regions (indicated by white circles) are superimposed on the ROI (Step B1). The control area metric ("CR metric") is calculated by quantifying a relevant metric of the control area ("CR object metric") (e.g., the total number of marked objects in the control area or the total area of ​​marked biomarker expression in the control area) and dividing it by the control area ROI metric ("CR ROI metric") (e.g., the area of ​​the control area, the total number of cells in the control area) (Step B2). A separate CR metric is calculated for each control area. The global metric is obtained by calculating the mean or median of all CR metrics (Step B3).

[0113] When a control region is used, any method of overlapping the control region for metric processing may be used. In certain embodiments, the ROI may be divided into multiple grid spaces, each of which constitutes a control region (these may be of equal size, random size, or any combination of different sizes). Alternatively, multiple control regions of known size (which may be the same or different) may be arranged adjacent to or overlapping each other so as to substantially cover the entire ROI. Other methods and configurations may be used, as long as the output is an object metric of the ROI that can be compared between different samples. Specific examples of combinations of ROIs, objects, and object metrics useful for evaluating images of stained samples disclosed herein include, but are not necessarily limited to, those listed in Table 7 below. In each example, “object metric” in Table 7 may refer to a total metric, a control region metric, or a global metric. TIFF0007855525000015.tif232170TIFF0007855525000016.tif233170TIFF0007855525000017.tif218170TIFF00078555250 00018.tif250170TIFF0007855525000019.tif245170TIFF0007855525000020.tif234170TIFF0007855525000021.tif223170 TIFF0007855525000022.tif232170TIFF0007855525000023.tif235170TIFF0007855525000024.tif213170TIFF00078555250 00025.tif232170TIFF0007855525000026.tif218170TIFF0007855525000027.tif250170TIFF0007855525000028.tif124170

[0114] If desired, the calculated object metrics may be converted to normalized feature vectors. In a typical example, the calculated object metrics are plotted for samples in a cohort to evaluate the distribution and identify whether there is right- or left-skewness. A biologically significant cutoff (maximum cutoff for right-skewed distributions and / or minimum cutoff for left-skewed distributions) is identified, and each sample with a value above the cutoff (above the cutoff for right-skewed distributions, or below the cutoff for left-skewed distributions) is assigned an object metric equal to the cutoff value. The cutoff value (hereinafter referred to as the "normalization factor") is then applied to each object metric. For right-skewed distributions, the normalized object metric is obtained by dividing the object metric by the normalization factor, in which case the object metric is expressed on a maximum scale (i.e., the value of the normalized metric does not exceed a default maximum value, such as 1, 10, or 100). Similarly, for a left-skewed distribution, a normalized object metric is obtained by dividing the object metric by a normalization factor, in which case the object metric is represented on a minimum scale (i.e., the value of the normalized metric does not fall below a predetermined minimum value, such as 1, 10, or 100). If desired, the normalized object metric may be multiplied or divided by a predetermined constant value to obtain a desired scale (for example, for a right-skewed distribution, multiply by 100 to obtain a percentage of the normalization factor instead of a fraction of the normalization factor). For a test sample, the normalized object metric may be calculated by applying the normalization factor identified for modeling, as well as the maximum and / or minimum cutoffs, to the object metric calculated for the test sample.

[0115] In another embodiment, objects are clustered into one of several groups based on various extracted features, such as cell size, shape, staining intensity, texture, and staining response. In an exemplary embodiment, an unsupervised clustering function, such as the function described in US62 / 441,068 filed December 30, 2016, is applied to the images.

[0116] C. Scoring Function In embodiments where prediction of response to EGFR therapy is desired, a scoring engine may be implemented. The scoring engine applies a scoring function to a feature vector containing object metrics for each of the biomarkers being evaluated and calculates a score. The scoring engine may then generate a report containing the scores.

[0117] To identify a scoring function, object metrics from a cohort of patients with known outcomes are modeled for their ability to predict relative tumor prognosis, risk of progression, and / or likelihood of responding to a particular treatment process.

[0118] In one embodiment, the scoring function is derived by modeling various combinations of object metrics for their correlation with various outcome events. Object metrics for a sample can be modeled for outcomes using one or more of various models, including “time to event” models (such as the Cox proportional hazards model for overall survival, progression-free survival, or recurrence-free survival) and binary event models (such as logistic regression models). In one embodiment, a “time to event” model is used. These models test each variable for its ability to predict the relative risk of a defined event occurring at any given time. In such cases, the “event” is typically overall survival, recurrence-free survival, and / or progression-free survival. In one example, the “time to event” model is the Cox proportional hazards model for overall survival, recurrence-free survival, or progression-free survival. The Cox proportional hazards model can be written as Equation 1: Score = exp(b1 x 1 + b2 x 2 + ... b p X p ) Equation 1 In each case, in the formula, X1, X2, ..., Xp are values ​​of the object metric (which may be subject to maximum and / or minimum cutoff, and / or normalization), and b1, b2...b pis a constant extrapolated from the model for each feature metric. For each patient sample in the study cohort, data is obtained regarding the feature metrics for the outcome being tracked (time to death, time to recurrence, or time to progression) and each biomarker being analyzed. Candidate Cox proportional models are generated by inputting the feature metric data and survival data for each individual in the cohort into a suite of computer statistical analysis software (among others, The R Project for Statistical Computing (available at https: / / www.r-project.org / ), SAS, MATLAB, etc.). The predictive power of each candidate model is tested using a concordance index such as the C-index. The model with the highest concordance score using the selected concordance index is selected as the continuous scoring function.

[0119] Furthermore, one or more stratified cutoffs may be selected to divide patients into “risk bins” according to relative risk (e.g., “high risk” and “low risk,” quartiles, deciles, etc.). In one example, the stratified cutoffs are selected using a receiver operating characteristic (ROC) curve. The ROC curve allows the user to balance the sensitivity of the model (i.e., prioritizing capturing as many “positive” or “high risk” candidates as possible) with the specificity of the model (i.e., minimizing false positives for “high risk candidates”). In one embodiment, a cutoff is selected between the high-risk bin and the low-risk bin for overall survival, relapse-free survival, or progression-free survival, and the selected cutoff balances sensitivity and specificity.

[0120] After a scoring function is modeled and an optional hierarchical cutoff is selected, the scoring function can be applied to images of the test specimen to calculate a response score for the specimen. Typically, the test specimen is similar to the specimen type used to model a continuous scoring function, except that the outcome is unknown. The test specimen is stained for biomarkers relevant to the scoring function, relevant object metrics are calculated, and if normalization factors and / or maximum and / or minimum cutoffs are used, these are applied to feature metrics to obtain normalized feature metrics. The response score is calculated by applying the scoring function to the feature metrics or normalized feature metrics. The response score can then be incorporated into the clinician's diagnostic and / or treatment decisions.

[0121] IV.Clinical application In clinical practice, scores obtained from histochemical staining, as described above, can be used to determine the course of treatment for a patient. This disclosure also describes a method of treating a patient with anti-EGFR therapy, wherein the patient is treated with anti-EGFR therapy when the patient has a tumor that is scored or categorized (as described above) as “expected to have a positive response to anti-EGFR therapy” or “likely to respond to anti-EGFR therapy”.

[0122] In one embodiment, anti-EGFR therapy is an EGFR antibody-based therapy. These therapies typically rely on an antibody or antibody fragment that binds to the extracellular domain of EGFR and disrupts the association between EGFR and its ligands (including EREG and AREG). In one embodiment, the EGFR antibody-based therapy comprises cetuximab and / or panitumumab. In one embodiment, the EGFR antibody-based therapy is administered when (a) the expression patterns of EGFR and one or more of EREG and AREG indicate that the patient is likely to respond to EGFR antibody-based therapy; and (b) the subject or sample is determined to be RAS wild-type. The Ras protein is a small GTPase active as a downstream component of the EGFR signaling network. The human Ras protein is encoded by one of three RAS genes: HRAS (encoding the h-Ras protein), KRAS (encoding the k-Ras protein), and NRAS (encoding the n-Ras protein). The HRAS, KRAS, and NRAS genes are collectively referred to as “RAS” in this specification. The H-Ras, k-Ras, and n-Ras proteins are collectively referred to as “Ras proteins” in this specification. The canonical sequence of the human h-Ras protein is presented as SEQ ID NO: 4 (Uniprot acceptance number P01112-1). The canonical sequence of the human k-Ras protein is presented as SEQ ID NO: 5 (Uniprot acceptance number P01116-1). The canonical sequence of the human n-Ras protein is presented as SEQ ID NO: 6 (Uniprot acceptance number P01111-1). Oncogene mutations in the RAS typically result in constitutively active Ras proteins. Therefore, patients with activating mutations in at least one Ras protein are likely to be resistant to anti-EGFR therapy.Activated Ras mutations in colorectal cancer have been outlined, in particular, by Prior et al., Cancer Res., Vol. 72, No. 10, pp. 2457–67 (May 2012) (referenced), and Waring et al., Clin. Colorectal Cancer, Vol. 15, No. 2, pp. 95–103 (June 2016) (referenced). As used herein, “wild-type RAS” means that a sample or subject has shown negative test results in a RAS mutation screening assay for at least NRAS and KRAS mutations (whether currently known or to be discovered in the future) that confer resistance to EGFR monoclonal antibody therapy. In one embodiment, the RAS mutation screening assay includes determining the presence or absence of activating mutations in at least codons 12 and 13 of NRAS and codons 12 and 13 of KRAS, and if the sample or subject does not contain any activating mutations in codons 12 and 13 of NRAS and codons 12 and 13 of KRAS, the sample is considered "RAS wild-type". In another embodiment, the RAS mutation screening assay includes determining the presence or absence of activating mutations in at least codons 12, 13, 59, 61, 117, and 146 of NRAS and codons 12, 13, 59, 61, 117, and 146 of KRAS. If it is determined that the sample or subject does not contain any of the activating mutations in codons 12, 13, 59, 61, 117, and 146 of NRAS and codons 12, 13, 59, 61, 117, and 146 of KRAS and has wild-type RAS status, the sample is considered "RAS wild-type". Screening for Ras mutation status may be performed on various different types of samples derived from a subject, including tissue samples derived from tumor and blood samples of the same subject from which the tissue sample was obtained.Many different methods are known for screening Ras mutation status, including sequencing, pyrosequencing, real-time PCR, allele-specific real-time PCR, restriction fragment length polymorphism (RFLP) analysis with sequencing, amplification refractory mutation systems (ARMS), or methods based on cold-PCR (coamplification at lower denaturation temperature PCR) with sequencing. Other specific exemplary methods for screening for Ras mutations include, but are not limited to, blood-based screening methods that rely on circulating tumor DNA (ctDNA) (see, for example, Schmiegel et al., Mol. Oncol., Vol. 11, No. 2, pp. 208-2019 (February 2017) (screening for mutations by applying an emulsion digital PCR-based assay for exons 2, 3, and 4 of KRAS and NRAS to a circulating cell-free DNA assay)), as well as tissue-based methods such as screening for mutations in exons 2, 3, and 4 of KRAS and NRAS in tumor tissue samples using Sanger sequencing, massively parallel sequencing (including pyrosequencing, circulating reversible termination, semiconducting sequencing, or sequencing methodologies based on phospho-binding fluorescent nucleotide technology), or PCR-based assays (including quantitative PCR and digital PCR). The present invention is not limited to any specific method with respect to screening for Ras mutation status. In some embodiments, the sample or subject is determined to be RAS wild-type before staining for EGFR and EGFR ligands is performed. In other embodiments, the sample is stained for EGFR and EGFR ligands regardless of the RAS mutation status.

[0123] In one embodiment, EGFR antibody-based therapy is incorporated into a treatment regimen for RAS wild-type subjects with stage III colorectal tumors. While surgical removal of the tumor or partial colectomy (including removal of nearby lymph nodes) followed by adjuvant chemotherapy and / or radiotherapy is common at this stage, chemotherapy without surgery (sometimes in combination with radiotherapy) may be used for certain patients. Common chemotherapy regimens include fluoropyrimidine-based chemotherapy sometimes combined with leucovorin and / or alkylating agents (such as oxaliplatin). Non-limiting combination therapies used at this stage include FOLFOX (5-FU, leucovorin, and oxaliplatin) or CapeOx (capecitabine and oxaliplatin). In one specific non-limiting embodiment, a method for treating stage III colorectal cancer may include: (a) For subjects with an expression pattern of EGFR and one or more of EREG and AREG indicating a high likelihood of responding to EGFR antibody-based therapy, and (b) RAS wild-type status: Administer EGFR antibody-based therapy, optionally in combination with fluoropyrimidine-based chemotherapy or fluoropyrimidine-based combination chemotherapy (such as FOLFOX or CapeOx); or (a) If the expression patterns of EGFR and one or more of EREG and AREG indicate that the patient is unlikely to respond to EGFR antibody-based therapy, and / or (b) if an activated RAS mutation is present, administer a course of therapy that does not include EGFR antibody-based therapy.

[0124] In another embodiment, EGFR antibody-based therapy is incorporated into a treatment regimen for RAS wild-type subjects with stage IV colorectal tumors. A treatment regimen for stage IV colorectal tumors typically includes surgical removal or partial colectomy (including removal of nearby lymph nodes) and metastasis (if possible), as well as adjuvant or neoadjuvant chemotherapy and / or radiotherapy. Surgical removal or partial colectomy (including removal of nearby lymph nodes) and metastasis (if possible), as well as chemotherapy and / or radiotherapy, are typically performed at this stage. Common chemotherapy includes fluoropyrimidine-based chemotherapy, sometimes combined with leucovorin and / or other chemotherapy and / or targeted therapies. Non-limited combination therapies used at this stage include: • FOLFOX: Leucovorin, 5-FU, and oxaliplatin (ELOXATIN); • FOLFIRI: Leucovorin, 5-FU, and irinotecan (CAMPTOSAR); • CapeOX: Capecitabine (Xeloda) and oxaliplatin; FOLFOXIRI: leucovorin, 5-FU, oxaliplatin, and irinotecan; • One of the above combinations, plus either a VEGF-targeting drug (such as bevacizumab [Avastin], ziv-aflibercept [ZALTRAP], or ramucirumab [CYRAMZA]) or an EGFR-targeting drug (such as cetuximab [Erbitux] or panitumumab [VECTIBIX]); • 5-FU and leucovorin, with or without the presence of the target drug; • Capecitabine, regardless of the presence or absence of the target drug; • Irinotecan, regardless of the presence or absence of the target drug; • Cetuximab monotherapy; • Panitumumab monotherapy; • Regorafenib (STIVARGA) alone; and • Trifluridine and tipiracil (LONSURF). In one particular, non-limiting embodiment, a method for treating stage IV colorectal cancer may include: (a) Expression patterns of EGFR and one or more of EREG and AREG that indicate a high likelihood of responding to EGFR antibody-based therapy in the patient; and (b) in the case of subjects with RAS wild-type status, EGFR antibody-based therapy may be administered in combination with one or more additional therapies selected from the group consisting of FOLFOX, FOLFIRI, CapeOX, FOLFOXIRI, 5-FU and leucovorin, capecitabine, irinotecan, and VEGF-targeting drugs (such as bevacizumab, ziv-aflibercept, and ramucirumab); or • If (a) the expression patterns of EGFR and one or more of EREG and AREG indicate that the patient is unlikely to respond to EGFR antibody-based therapy, and / or (b) the patient has an activated RAS mutation, administer a course of therapy that does not include EGFR antibody-based therapy (e.g., a VEGF-targeting drug, FOLFOX (optionally combined with a VEGF-targeting drug), FOLFIRI (optionally combined with a VEGF-targeting drug), CapeOX (optionally combined with a VEGF-targeting drug), FOLFOXIRI (optionally combined with a VEGF-targeting drug), 5-FU and leucovorin (optionally combined with a VEGF-targeting drug), capecitabine (optionally combined with a VEGF-targeting drug), irinotecan (optionally combined with a VEGF-targeting drug), regorafenib, or trifluridine and tipiracil (optionally combined with a VEGF-targeting drug)). [Examples]

[0125] V. Examples Example 1: Colorectal cancer sample and sample processing In a study of 57 cases of colorectal cancer, 11 4 μm sections were obtained from each sample and stained in the following order (see Table 8). TIFF0007855525000029.tif80170

[0126] Slide 2 shows the results of multiplex IHC performed using a BenchMark ULTRA instrument. The antibodies used included EGFR (5B7) rabbit antibody clone, EREG (L8) rabbit antibody clone, and AREG (L10) rabbit antibody clone. EGFR was stained with DISCOVERY yellow, EREG with DISCOVERY teal, and AREG with DISCOVERY purple. Since each of the three primary antibodies was a rabbit antibody, sequential multiplex staining was used, and cell conditioning buffer 2 (CC2) and heat were applied to the tissue sections after each staining to denature the antibodies and prevent cross-reactivity. An example protocol for multiplex staining described herein can be summarized as follows: apply deparaffinizing buffer, apply antigen retrieval buffer, apply anti-EREG antibody and detection reagent, apply heat kill step, apply anti-EGFR antibody and detection reagent, apply heat kill step, apply anti-AREG antibody and detection reagent. Table 9 below describes the protocol used for the multiplex staining method with BenchMark ULTRA (Ventana Medical Systems, Inc.) in this example. The present invention is not limited to this protocol. TIFF0007855525000030.tif250170TIFF0007855525000031.tif251170TIFF0007855525000032.tif251170 TIFF0007855525000033.tif251170TIFF0007855525000034.tif251170TIFF0007855525000035.tif183170

[0127] Slides 3, 5, and 7 show the simplex IHC method performed on a BenchMark XT instrument. Slide 3 features an AREG(L10) rabbit antibody clone and the OptiView DAB detection kit. Slide 5 features an EREG(L8) rabbit antibody and the OptiView DAB detection kit. Slide 7 features an EGFR(5B7) rabbit antibody and the OptiView DAB detection kit.

[0128] For image acquisition and analysis, stained slides were scanned using a VENTANA iSCAN HT slide scanner at 20x magnification and the HT focusing technique. Readouts combined the total number and density of IHC-positive and negative cells with descriptive statistics of cell-by-cell expression patterns, spatial patterns of positive cells, and cell collocations between different markers determined after automated alignment of consecutive or nearby tissue sections.

[0129] Example 2: Correlation between simplex assay and qPCR Slide 11 from each case in Example 1 was sent for qPCR analysis. For statistical analysis, IHC status was correlated with qPCR. Correlation was measured using Spearman's ρ. Subsequently, LOESS and single-piece linear regression were plotted on the data. The highest tertile of either AREG or EREG qPCR was plotted, and the point where it intersected the regression line was determined as the relevant cutoff point for the IHC parameter.

[0130] The qPCR results of the sample from Example 1 are comparable to publicly available data. Figure 2A shows that the distribution of qPCR values ​​is similar to that of published values, and Figure 2B shows that EREG mRNA expression is closely related to AREG mRNA expression.

[0131] Figures 3A and 3B show that the positivity rate correlates well with qPCR for both EREG and AREG. Figure 3A is a scatter plot of the percentage of tumor cells positive for human EREG protein stained for IHC, using qPCR data from the same sample. This scatter plot shows a Spearman ρ: 0.9012 with a LOESS curve having a P value < 0.001 and a span of 0.8 and an angle of 2. As can be seen from the LOESS curve, the ΔCT of the upper tertile of qPCR expression for EREG is ≥ 0.4833, which intersects with the 67.5825% EREG protein-positive tumor cell rate.

[0132] The distribution of EREG data resembles that seen in the comparison of two assays with different dynamic ranges (Figure 3A). qPCR exhibits a wider dynamic range, with signals generated below the IHC detection limit and after IHC becoming saturated. Similar results were obtained for amphiregulin, but it did not appear to reach a saturation point.

[0133] In addition to the positivity rate, an unsupervised clustering function described in US62 / 441,068, filed December 30, 2016 (the entire function is incorporated herein by reference), was applied to the images. This assay generated four distinct classifications of marker-positive cells (hereinafter referred to as parameter 1, parameter 2, parameter 3, and parameter 4). Several of these parameters were found to be useful and correlate with qPCR. The results are shown in Figures 4A–4H. Parameter 1 correlated very well with EREG, with Spearman's ρ being 0.8855 and % positivity being the best. The cut-off points for parameter 1 were 6.6744% for EREG and 2.5275% for AREG (Figures 4A, 4B). Parameter 2 correlated less with qPCR data than the other readouts, with Spearman's ρ remaining at 0.5753 for EREG and 0.6593 for AREG, although the values ​​still reached significance. This weak correlation leaves some inconsistencies when comparing IHC and qPCR for either marker. Parameters 3 and 4 both show good correlations with mRNA expression (Figures 4E, 4F, 4G, and 4H), and P4 shows the best correlation with AREG IHC (Figure 4H).

[0134] In addition to unbiased parameters, the algorithm also scored assays for specific intracellular localizations, including membrane, cytoplasm, and spotted granules. Automated image analysis determined the overall staining intensity and the individual staining intensities for membrane, cytoplasm, or spotted patterns on a cell-by-cell basis. In EREG, both membrane and cytoplasmic staining intensities correlated well with mRNA expression (Figures 5A, 5C). Furthermore, AREG correlated with membrane staining intensity (Figures 5B, 5D). Note that while the spotted / granule staining patterns were readily apparent and very clear in both assays, their correlation with qPCR was very poor.

[0135] Next, it was shown that IHC of EREG and AREG by digital image analysis has similar clinical utility to qPCR analysis of EGFR ligand. Each computer-generated parameter was correlated with qPCR values ​​to establish IHC cutpoints. Image analysis results were obtained for all relevant tissues on the slide. High-resolution results in a single field of view (FOV) (see Figures 6A, 6B, and 6C) demonstrate that the automated analysis identifies all tumor cells and classifies them as marker-negative (shown in green and blue) or marker-positive (shown in yellow, orange, red, and magenta). The number of tumor cells across the entire slide is reported separately for marker-negative and marker-positive cells. The cutoff values ​​for all 11 parameters and their Spearman's ρ values ​​are listed in Table 10 below. TIFF0007855525000036.tif83170

[0136] Based on Spearman's ρ value, several EREG parameters show a strong correlation between 0.89 and 0.90. Furthermore, the top variables of AREG IHC show correlations of 0.70 and 0.71.

[0137] Example 3: Correlation between multiplex assay and simplex assay Figure 7 shows that the colorectal case from Example 1 was efficiently stained using the multiplex IHC assay. In the multiplex assay, EGFR was stained with DISCOVERY Yellow, EREG with DISCOVERY Teal, and AREG with tetramethylrhodamine (DISCOVERY Purple). The multiplex assay results (Slide 2) were compared with single DAB staining of their equivalents (e.g., Slide 3 for AREG, Slide 5 for EREG, and Slide 7 for EGFR). The first row of Figure 8 shows that the multiplex staining matches the signals of the corresponding DAB simplex assay. The second row of Figure 8 shows that using digital image analysis, the multiplex assay can be analyzed into the stains of its constituent components. The third row shows that the analyzed channels can be recombined and restained to create a pseudo-DAB image. Figure 8 shows that the multiplex assay can provide the same predictive capability as the simplex assay.

Claims

1. (a) Contacting the tissue section with a human EGFR protein biomarker-specific binder and a detection reagent sufficient to directly or indirectly attach the first chromogen to the human EGFR protein biomarker-specific binder bound to the tissue section; (b) Contact the tissue section with an AREG protein biomarker-specific binder and a detection reagent sufficient to directly or indirectly attach a second chromogen to the AREG protein biomarker-specific binder bound to the tissue section; (c) Contacting the tissue section with an EREG protein biomarker-specific binder and a detection reagent sufficient to directly or indirectly attach a third chromogen to the EREG protein biomarker-specific binder bound to the tissue section; (d) Obtaining digital images of the tissue section containing the attached first, second, and third chromogens; (e) Identifying the region of interest (ROI) of the acquired digital image; (f) Identifying multiple object features within an identified ROI, wherein the object features are selected from at least biomarker-positive membrane staining, biomarker-positive cytoplasmic staining, and biomarker-positive cell staining; (g) (i) to obtain an ROI object metric by quantifying a plurality of identified object features, and (ii) to calculate a test object metric by dividing the obtained ROI object metric by an ROI metric, wherein the ROI metric is selected from the area of ​​the identified ROI, the total number of cells in the identified ROI, the cell ratio in the identified ROI, or the ligand-receptor ratio in the identified ROI; and (h) Using a scoring engine to calculate a score for acquired digital images based on the calculated test object metrics, the scoring engine is derived from one or more models that correlate (i) training object metrics calculated from training samples from multiple patients with (ii) the respective clinical outcomes of multiple patients from which the training samples originate; Includes, A method wherein a first pigment, a second pigment, and a third pigment have deconvolutionable colors.

2. The method according to claim 1, wherein the biomarker-specific conjugate is an antibody or an antigen-binding fragment thereof.

3. The method according to claim 1 or 2, wherein the tissue section is a formalin-fixed paraffin-embedded (FFPE) tissue section.

4. The method according to any one of claims 1 to 3, wherein the tissue section is derived from a colorectal tumor sample.

5. The method according to any one of claims 1 to 3, wherein the tissue section is derived from a polyp.

6. The method according to any one of claims 1 to 3, wherein the tissue section is RAS wild-type.

7. The method according to any one of claims 1 to 6, wherein the tissue section does not contain a mutation that enables ligand-independent EGFR signaling.

8. The method according to any one of claims 1 to 7, wherein the tissue section does not contain a RAS protein having a mutation that confers resistance to EGFR monoclonal antibody therapy.

9. The method according to any one of claims 1 to 8, further comprising visualizing the chromogen using bright-field microscopy.

10. The method according to claim 1, further comprising evaluating, based on a calculated score, whether the patient from whom the tissue section was obtained is likely to benefit from anti-EGFR therapy.

11. The method according to claim 1, wherein the identified ROI is an EGFR-positive cell cluster.

12. The method according to claim 1, further comprising normalizing the test object metric before calculating the score.

Images