WO2022051245A2 - Methods and systems for predicting response to anti-tnf therapies - Google Patents

Methods and systems for predicting response to anti-tnf therapies Download PDF

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Publication number
WO2022051245A2
WO2022051245A2 PCT/US2021/048346 US2021048346W WO2022051245A2 WO 2022051245 A2 WO2022051245 A2 WO 2022051245A2 US 2021048346 W US2021048346 W US 2021048346W WO 2022051245 A2 WO2022051245 A2 WO 2022051245A2
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Prior art keywords
gene expression
tnf therapy
subjects
genes
responsive
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English (en)
French (fr)
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WO2022051245A3 (en
Inventor
Susan GHIASSIAN
Marc Santolini
Nancy Schoenbrunner
Keith J. Johnson
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Scipher Medicine Corp
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Scipher Medicine Corp
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Priority to IL300978A priority Critical patent/IL300978A/en
Priority to CA3191195A priority patent/CA3191195A1/en
Priority to AU2021336781A priority patent/AU2021336781A1/en
Priority to MX2023002446A priority patent/MX2023002446A/es
Priority to GB2303624.7A priority patent/GB2616129A/en
Priority to JP2023513939A priority patent/JP2023538963A/ja
Priority to KR1020237011014A priority patent/KR20240018404A/ko
Priority to CN202180074291.7A priority patent/CN117615780A/zh
Application filed by Scipher Medicine Corp filed Critical Scipher Medicine Corp
Priority to EP21864967.1A priority patent/EP4208256A4/en
Publication of WO2022051245A2 publication Critical patent/WO2022051245A2/en
Publication of WO2022051245A3 publication Critical patent/WO2022051245A3/en
Priority to US18/176,288 priority patent/US20230282367A1/en
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K39/00—Medicinal preparations containing antigens or antibodies
    • A61K39/395—Antibodies; Immunoglobulins; Immune serum, e.g. antilymphocytic serum
    • A61K39/39533—Antibodies; Immunoglobulins; Immune serum, e.g. antilymphocytic serum against materials from animals
    • A61K39/3955—Antibodies; Immunoglobulins; Immune serum, e.g. antilymphocytic serum against materials from animals against proteinaceous materials, e.g. enzymes, hormones, lymphokines
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P29/00—Non-central analgesic, antipyretic or antiinflammatory agents, e.g. antirheumatic agents; Non-steroidal antiinflammatory drugs [NSAID]
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism

Definitions

  • Tumor necrosis factor is a cell signaling protein related to regulation of immune cells and apoptosis, and is implicated in a variety of immune and autoimmune-mediated disorders.
  • TNF is known to promote inflammatory response, which causes many problems associated with autoimmune disorders, such as rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, Crohn’s disease, ulcerative colitis, inflammatory bowel disease, chronic psoriasis, hidradenitis suppurativa, asthma, juvenile idiopathic arthritis, vitiligo, Graves’ ophthalmopathy (also known as thyroid eye disease, or Graves’ orbitopathy), and multiple sclerosis.
  • autoimmune disorders such as rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, Crohn’s disease, ulcerative colitis, inflammatory bowel disease, chronic psoriasis, hidradenitis suppurativa,
  • TNF-mediated disorders are currently treated by inhibition of TNF, and in particular by administration of an anti-TNF agent (i.e., by anti-TNF therapy).
  • anti-TNF agents approved in the United States include monoclonal antibodies that target TNF, such as adalimumab (Humira®), certolizumab pegol (Cimiza®), golimumab (Simponi® and Simponi Aria®), and infliximab (Remicade®), decoy circulating receptor fusion proteins such as etanercept (Enbrel®), and biosimilars, such as adalimumab ABP 501 (AMGEVITATM), and etanercept biosimilars GP2015 (Erelzi).
  • a significant known problem with anti-TNF therapies is that response rates are inconsistent. Indeed, recent international conferences designed to bring together leading scientists and clinicians in the fields of immunology and rheumatology to identify unmet needs in these fields almost universally identify uncertainty in response rates as an ongoing challenge. For example, the 19 th annual International Targeted Therapies meeting, which held break-out sessions relating to challenges in treatment of a variety of diseases, including rheumatoid arthritis, psoriatic arthritis, axial spondyloarthritis, systemic lupus erythematous, and connective tissue diseases (e.g.
  • Sjogren's syndrome, Systemic sclerosis, vasculitis including Bechet’s and IgG4 related disease identified certain issues common to all of these diseases, specifically, “the need for better understanding the heterogeneity within each disease . . . so that predictive tools for therapeutic responses can be developed.
  • Winthrop, et al. “The unmet need in rheumatology: Reports from the targeted therapies meeting 2017,” Clin. Immunol, pii: S 1521-6616(17)30543-0, Aug. 12, 2017.
  • extensive literature relating to treatment of Crohn’s Disease with anti-TNF therapy consistently bemoans erratic response rates and inability to predict which patients will benefit. See, e.g., M.T.
  • Provided technologies permit care providers to distinguish subjects likely to benefit from anti-TNF therapy from those who are not, reduce risks to patients, increase timing and quality of care for non-responder patient populations, increase efficiency of drug development, and avoid costs associated with administering ineffective therapy to non-responder patients or with treating side effects such patients experience upon receiving anti-TNF therapy.
  • Provided technologies embody and/or arise from, among other things, certain insights that include, for example, identification of the source of a problem with certain conventional approaches to defining responder vs. non-responder populations and/or that represent particularly useful strategies for defining classifiers that distinguish between such populations.
  • certain insights include, for example, identification of the source of a problem with certain conventional approaches to defining responder vs. non-responder populations and/or that represent particularly useful strategies for defining classifiers that distinguish between such populations.
  • the present disclosure identifies that one source of a problem with many conventional strategies for defining responder vs. non-responder populations through consideration of gene expression differences in the populations is that they typically prioritize or otherwise focus on highest fold changes; the present disclosure teaches that such an approach misses subtle but meaningful differences relevant to disease biology.
  • the present disclosure offers an insight that mapping of genes with altered expression levels onto a human interactome map (in particular onto a human interactome map that represents experimentally supported physical interactions between cellular components which, in some embodiments, explicitly excludes any theoretical, calculated, or other interaction that has been proposed but not experimentally validated), can provide a useful and effective classifier for defining responders vs. non-responders to anti-TNF therapy.
  • genes included in such a classifier represent a connected module on the human interactome.
  • the present disclosure provides a method of treating subjects suffering from a disease, disorder, or condition (e.g., inflammatory bowel disease, ulcerative colitis or Crohn’s disease) with anti-TNF therapy, the method comprising a step of: administering the anti-TNF therapy to subjects who have been determined to display a gene expression response signature established to distinguish between responsive and non-responsive prior subjects who have received the anti-TNF therapy (e.g., where “prior subjects” refer to subjects who have previously received the anti-TNF therapy, and have been classified as responsive or non- responsive to said anti-TNF therapy).
  • a disease, disorder, or condition e.g., inflammatory bowel disease, ulcerative colitis or Crohn’s disease
  • the present disclosure provides method of treating a subject suffering from a disease, disorder, or condition (e.g., inflammatory bowel disease, ulcerative colitis or Crohn’s disease) with an anti-TNF therapy, the method comprising a step of: administering the anti-TNF therapy to subjects who have been determined not to display a gene expression response signature established to distinguish between responsive and non-responsive prior subjects who have received the anti-TNF therapy.
  • a disease, disorder, or condition e.g., inflammatory bowel disease, ulcerative colitis or Crohn’s disease
  • the present disclosure provides method of treating a subject suffering from a disease, disorder, or condition with an anti-TNF therapy, the method comprising a step of: administering the anti-TNF therapy to subjects who have been determined to be responsive via a classifier determined to distinguish between responsive and non-responsive subjects who have received the anti-TNF therapy (“prior subjects”), wherein the classifier distinguishes between responsive and non-responsive subjects on the basis of a set of variables, the set of variables comprising expression of one or more genes selected from:
  • the present disclosure provides a kit for evaluating a likelihood that a subject suffering from an autoimmune disorder will not respond to an anti-TNF therapy, the kit comprising a set of reagents for detecting an expression level of one or more genes selected from the group consisting of:
  • Administration typically refers to the administration of a composition to a subject or system, for example to achieve delivery of an agent that is, or is included in or otherwise delivered by, the composition.
  • agent refers to an entity (e.g., for example, a lipid, metal, nucleic acid, polypeptide, polysaccharide, small molecule, etc., or complex, combination, mixture or system [e.g., cell, tissue, organism] thereof), or phenomenon (e.g., heat, electric current or field, magnetic force or field, etc.).
  • entity e.g., for example, a lipid, metal, nucleic acid, polypeptide, polysaccharide, small molecule, etc., or complex, combination, mixture or system [e.g., cell, tissue, organism] thereof
  • phenomenon e.g., heat, electric current or field, magnetic force or field, etc.
  • amino acid refers to any compound and/or substance that can be incorporated into a polypeptide chain, e.g., through formation of one or more peptide bonds.
  • an amino acid has the general structure H2N-C(H)(R)- COOH.
  • an amino acid is a naturally-occurring amino acid.
  • an amino acid is a non-natural amino acid; in some embodiments, an amino acid is a D-amino acid; in some embodiments, an amino acid is an L-amino acid.
  • standard amino acid refers to any of the twenty L-amino acids commonly found in naturally occurring peptides. “Nonstandard amino acid” refers to any amino acid, other than the standard amino acids, regardless of whether it is or can be found in a natural source.
  • an amino acid, including a carboxy- and/or amino-terminal amino acid in a polypeptide can contain a structural modification as compared to the general structure above.
  • an amino acid may be modified by methylation, amidation, acetylation, pegylation, glycosylation, phosphorylation, and/or substitution (e.g., of the amino group, the carboxylic acid group, one or more protons, and/or the hydroxyl group) as compared to the general structure.
  • such modification may, for example, alter the stability or the circulating halflife of a polypeptide containing the modified amino acid as compared to one containing an otherwise identical unmodified amino acid.
  • such modification does not significantly alter a relevant activity of a polypeptide containing the modified amino acid, as compared to one containing an otherwise identical unmodified amino acid.
  • amino acid may be used to refer to a free amino acid; in some embodiments it may be used to refer to an amino acid residue of a polypeptide, e.g., an amino acid residue within a polypeptide.
  • an analog refers to a substance that shares one or more particular structural features, elements, components, or moieties with a reference substance. Typically, an “analog” shows significant structural similarity with the reference substance, for example sharing a core or consensus structure, but also differs in certain discrete ways.
  • an analog is a substance that can be generated from the reference substance, e.g., by chemical manipulation of the reference substance. In some embodiments, an analog is a substance that can be generated through performance of a synthetic process substantially similar to (e.g., sharing a plurality of steps with) one that generates the reference substance. In some embodiments, an analog is or can be generated through performance of a synthetic process different from that used to generate the reference substance.
  • Antagonist may refer to an agent, or condition whose presence, level, degree, type, or form is associated with a decreased level or activity of a target.
  • An antagonist may include an agent of any chemical class including, for example, small molecules, polypeptides, nucleic acids, carbohydrates, lipids, metals, and/or any other entity that shows the relevant inhibitory activity.
  • an antagonist may be a “direct antagonist” in that it binds directly to its target; in some embodiments, an antagonist may be an “indirect antagonist” in that it exerts its influence by means other than binding directly to its target; e.g., by interacting with a regulator of the target, so that the level or activity of the target is altered).
  • an “antagonist” may be referred to as an “inhibitor”.
  • Antibody refers to a polypeptide that includes canonical immunoglobulin sequence elements sufficient to confer specific binding to a particular target antigen.
  • intact antibodies as produced in nature are approximately 150 kD tetrameric agents comprised of two identical heavy chain polypeptides (about 50 kD each) and two identical light chain polypeptides (about 25 kD each) that associate with each other into what is commonly referred to as a “Y-shaped” structure.
  • Each heavy chain is comprised of at least four domains (each about 110 amino acids long)- an amino-terminal variable (VH) domain (located at the tips of the Y structure), followed by three constant domains: CHI, CH2, and the carboxy-terminal CH3 (located at the base of the Y’ s stem).
  • VH amino-terminal variable
  • CHI amino-terminal variable
  • CH2 amino-terminal variable
  • CH3 located at the base of the Y’ s stem
  • a short region known as the “switch” connects the heavy chain variable and constant regions.
  • the “hinge” connects CH2 and CH3 domains to the rest of the antibody. Two disulfide bonds in this hinge region connect the two heavy chain polypeptides to one another in an intact antibody.
  • Each light chain is comprised of two domains - an amino-terminal variable (VL) domain, followed by a carboxy-terminal constant (CL) domain, separated from one another by another “switch”.
  • Intact antibody tetramers are comprised of two heavy chain-light chain dimers in which the heavy and light chains are linked to one another by a single disulfide bond; two other disulfide bonds connect the heavy chain hinge regions to one another, so that the dimers are connected to one another and the tetramer is formed.
  • Naturally-produced antibodies are also glycosylated, typically on the CH2 domain.
  • Each domain in a natural antibody has a structure characterized by an “immunoglobulin fold” formed from two beta sheets (e.g., 3-, 4-, or 5-stranded sheets) packed against each other in a compressed antiparallel beta barrel.
  • Each variable domain contains three hypervariable loops known as “complement determining regions” (CDR1, CDR2, and CDR3) and four somewhat invariant “framework” regions (FR1, FR2, FR3, and FR4).
  • CDR1, CDR2, and CDR3 three hypervariable loops known as “complement determining regions” (CDR1, CDR2, and CDR3) and four somewhat invariant “framework” regions (FR1, FR2, FR3, and FR4).
  • the Fc region of naturally-occurring antibodies binds to elements of the complement system, and also to receptors on effector cells, including for example effector cells that mediate cytotoxicity.
  • affinity and/or other binding attributes of Fc regions for Fc receptors can be modulated through glycosylation or other modification.
  • antibodies produced and/or utilized in accordance with the present invention include glycosylated Fc domains, including Fc domains with modified or engineered such glycosylation.
  • any polypeptide or complex of polypeptides that includes sufficient immunoglobulin domain sequences as found in natural antibodies can be referred to and/or used as an “antibody”, whether such polypeptide is naturally produced (e.g., generated by an organism reacting to an antigen), or produced by recombinant engineering, chemical synthesis, or other artificial system or methodology.
  • an antibody is polyclonal; in some embodiments, an antibody is monoclonal.
  • an antibody has constant region sequences that are characteristic of mouse, rabbit, primate, or human antibodies.
  • antibody sequence elements are humanized, primatized, chimeric, etc, as is known in the art.
  • an antibody utilized in accordance with the present invention is in a format selected from, but not limited to, intact IgA, IgG, IgE or IgM antibodies; bi- or multi- specific antibodies (e.g., Zybodies®, etc); antibody fragments such as Fab fragments, Fab’ fragments, F(ab’)2 fragments, Fd’ fragments, Fd fragments, and isolated CDRs or sets thereof; single chain Fvs; polypeptide-Fc fusions; single domain antibodies (e.g., shark single domain antibodies such as IgNAR or fragments thereof); cameloid antibodies; masked antibodies (e.g., Probodies®); Small Modular ImmunoPharmaceuticals (“SMIPsTM ); single chain or Tandem diabodies (Tand
  • an antibody may lack a covalent modification (e.g., attachment of a glycan) that it would have if produced naturally.
  • an antibody may contain a covalent modification (e.g., attachment of a glycan, a payload [e.g., a detectable moiety, a therapeutic moiety, a catalytic moiety, etc], or other pendant group [e.g., poly-ethylene glycol, etc.]).
  • Two events or entities are “associated” with one another, as that term is used herein, if the presence, level, degree, type and/or form of one is correlated with that of the other.
  • a particular entity e.g., polypeptide, genetic signature, metabolite, microbe, etc
  • two or more entities are physically “associated” with one another if they interact, directly or indirectly, so that they are and/or remain in physical proximity with one another.
  • two or more entities that are physically associated with one another are covalently linked to one another; in some embodiments, two or more entities that are physically associated with one another are not covalently linked to one another but are non-covalently associated, for example by means of hydrogen bonds, van der Waals interaction, hydrophobic interactions, magnetism, and combinations thereof.
  • a biological sample typically refers to a sample obtained or derived from a biological source (e.g., a tissue or organism or cell culture) of interest, as described herein.
  • a source of interest comprises an organism, such as an animal or human.
  • a biological sample is or comprises biological tissue or fluid.
  • a biological sample may be or comprise bone marrow; blood; blood cells; ascites; tissue or fine needle biopsy samples; cell-containing body fluids; free floating nucleic acids; sputum; saliva; urine; cerebrospinal fluid, peritoneal fluid; pleural fluid; feces; lymph; gynecological fluids; skin swabs; vaginal swabs; oral swabs; nasal swabs; washings or lavages such as a ductal lavages or broncheoalveolar lavages; aspirates; scrapings; bone marrow specimens; tissue biopsy specimens; surgical specimens; feces, other body fluids, secretions, and/or excretions; and/or cells therefrom, etc.
  • a biological sample is or comprises cells obtained from an individual.
  • obtained cells are or include cells from an individual from whom the sample is obtained.
  • a sample is a “primary sample” obtained directly from a source of interest by any appropriate means.
  • a primary biological sample is obtained by methods selected from the group consisting of biopsy (e.g., fine needle aspiration or tissue biopsy), surgery, collection of body fluid e.g., blood, lymph, feces etc.), etc.
  • sample refers to a preparation that is obtained by processing (e.g., by removing one or more components of and/or by adding one or more agents to) a primary sample. For example, filtering using a semi-permeable membrane.
  • processing e.g., by removing one or more components of and/or by adding one or more agents to
  • a primary sample For example, filtering using a semi-permeable membrane.
  • Such a “processed sample” may comprise, for example nucleic acids or proteins extracted from a sample or obtained by subjecting a primary sample to techniques such as amplification or reverse transcription of mRNA, isolation and/or purification of certain components, etc.
  • biological network refers to any network that applies to biological systems, having sub-units (e.g., “nodes”) that are linked into a whole, such as species units linked into a whole web.
  • a biological network is a protein-protein interaction network (PPI), representing interactions among proteins present in a cell, where proteins are nodes and their interactions are edges.
  • PPI protein-protein interaction network
  • connections between nodes in a PPI are experimentally verified.
  • connections between nodes are a combination of experimentally verified a mathematically calculated.
  • a biological network is a human interactome (a network of experimentally derived interactions that occur in human cells, which includes protein-protein interaction information as well as gene expression and co-expression, cellular co-localization of proteins, genetic information, metabolic and signaling pathways, etc.).
  • a biological network is a gene regulatory network, a gene co-expression network, a metabolic network, or a signaling network.
  • Combination Therapy refers to a clinical intervention in which a subject is simultaneously exposed to two or more therapeutic regimens (e.g. two or more therapeutic agents).
  • the two or more therapeutic regimens may be administered simultaneously.
  • the two or more therapeutic regimens may be administered sequentially (e.g., a first regimen administered prior to administration of any doses of a second regimen).
  • the two or more therapeutic regimens are administered in overlapping dosing regimens.
  • administration of combination therapy may involve administration of one or more therapeutic agents or modalities to a subject receiving the other agent(s) or modality.
  • combination therapy does not necessarily require that individual agents be administered together in a single composition (or even necessarily at the same time).
  • two or more therapeutic agents or modalities of a combination therapy are administered to a subject separately, e.g., in separate compositions, via separate administration routes (e.g., one agent orally and another agent intravenously), and/or at different time points.
  • two or more therapeutic agents may be administered together in a combination composition, or even in a combination compound (e.g., as part of a single chemical complex or covalent entity), via the same administration route, and/or at the same time.
  • Comparable refers to two or more agents, entities, situations, sets of conditions, etc., that may not be identical to one another but that are sufficiently similar to permit comparison there between so that one skilled in the art will appreciate that conclusions may reasonably be drawn based on differences or similarities observed.
  • comparable sets of conditions, circumstances, individuals, or populations are characterized by a plurality of substantially identical features and one or a small number of varied features.
  • the phrase “corresponding to” refers to a relationship between two entities, events, or phenomena that share sufficient features to be reasonably comparable such that “corresponding” attributes are apparent.
  • the term may be used in reference to a compound or composition, to designate the position and/or identity of a structural element in the compound or composition through comparison with an appropriate reference compound or composition.
  • a monomeric residue in a polymer e.g., an amino acid residue in a polypeptide or a nucleic acid residue in a polynucleotide
  • a residue in an appropriate reference polymer may be identified as “corresponding to” a residue in an appropriate reference polymer.
  • residues in a polypeptide are often designated using a canonical numbering system based on a reference related polypeptide, so that an amino acid “corresponding to” a residue at position 190, for example, need not actually be the 190 th amino acid in a particular amino acid chain but rather corresponds to the residue found at 190 in the reference polypeptide; those of ordinary skill in the art readily appreciate how to identify "corresponding" amino acids.
  • sequence alignment strategies including software programs such as, for example, BLAST, CS-BLAST, CUSASW++, DIAMOND, FASTA, GGSEARCH/GLSEARCH, Genoogle, HMMER, HHpred/HHsearch, IDF, Infernal, KLAST, USEARCH, parasail, PSLBLAST, PSI-Search, ScalaBLAST, Sequilab, SAM, SSEARCH, SWAPHI, SWAPHLLS, SWIMM, or SWIPE that can be utilized, for example, to identify “corresponding” residues in polypeptides and/or nucleic acids in accordance with the present disclosure.
  • software programs such as, for example, BLAST, CS-BLAST, CUSASW++, DIAMOND, FASTA, GGSEARCH/GLSEARCH, Genoogle, HMMER, HHpred/HHsearch, IDF, Infernal, KLAST, USEARCH, parasail, PSLBLAST, PSI-Search, ScalaBLAST,
  • Dosing regimen refers to a set of unit doses (typically more than one) that are administered individually to a subject, typically separated by periods of time.
  • a given therapeutic agent has a recommended dosing regimen, which may involve one or more doses.
  • a dosing regimen comprises a plurality of doses each of which is separated in time from other doses.
  • individual doses are separated from one another by a time period of the same length; in some embodiments, a dosing regimen comprises a plurality of doses and at least two different time periods separating individual doses.
  • all doses within a dosing regimen are of the same unit dose amount. In some embodiments, different doses within a dosing regimen are of different amounts. In some embodiments, a dosing regimen comprises a first dose in a first dose amount, followed by one or more additional doses in a second dose amount different from the first dose amount. In some embodiments, a dosing regimen comprises a first dose in a first dose amount, followed by one or more additional doses in a second dose amount same as the first dose amount. In some embodiments, a dosing regimen is correlated with a desired or beneficial outcome when administered across a relevant population (i.e., is a therapeutic dosing regimen).
  • Improved, increased or reduced As used herein, the terms “improved,” “increased,” or “reduced,”, or grammatically comparable comparative terms thereof, indicate values that are relative to a comparable reference measurement. For example, in some embodiments, an assessed value achieved with an agent of interest may be “improved” relative to that obtained with a comparable reference agent.
  • an assessed value achieved in a subject or system of interest may be “improved” relative to that obtained in the same subject or system under different conditions (e.g., prior to or after an event such as administration of an agent of interest), or in a different, comparable subject (e.g., in a comparable subject or system that differs from the subject or system of interest in presence of one or more indicators of a particular disease, disorder or condition of interest, or in prior exposure to a condition or agent, etc.).
  • Patient or subject refers to any organism to which a provided composition is or may be administered, e.g., for experimental, diagnostic, prophylactic, cosmetic, and/or therapeutic purposes. Typical patients or subjects include animals (e.g., mammals such as mice, rats, rabbits, non-human primates, and/or humans). In some embodiments, a patient is a human. In some embodiments, a patient or a subject is suffering from or susceptible to one or more disorders or conditions. In some embodiments, a patient or subject displays one or more symptoms of a disorder or condition. In some embodiments, a patient or subject has been diagnosed with one or more disorders or conditions.
  • animals e.g., mammals such as mice, rats, rabbits, non-human primates, and/or humans.
  • a patient is a human.
  • a patient or a subject is suffering from or susceptible to one or more disorders or conditions.
  • a patient or subject displays one or more symptoms of a disorder or condition.
  • a patient or subject has been diagnosed with one or more disorders
  • a patient or a subject is receiving or has received certain therapy to diagnose and/or to treat a disease, disorder, or condition.
  • pharmaceutical composition refers to an active agent, formulated together with one or more pharmaceutically acceptable carriers.
  • the active agent is present in unit dose amounts appropriate for administration in a therapeutic regimen to a relevant subject (e.g., in amounts that have been demonstrated to show a statistically significant probability of achieving a predetermined therapeutic effect when administered), or in a different, comparable subject (e.g., in a comparable subject or system that differs from the subject or system of interest in presence of one or more indicators of a particular disease, disorder or condition of interest, or in prior exposure to a condition or agent, etc.).
  • comparative terms refer to statistically relevant differences (e.g., that are of a prevalence and/or magnitude sufficient to achieve statistical relevance). Those skilled in the art will be aware, or will readily be able to determine, in a given context, a degree and/or prevalence of difference that is required or sufficient to achieve such statistical significance.
  • compositions, and/or dosage forms which are, within the scope of sound medical judgment, suitable for use in contact with the tissues of human beings and animals without excessive toxicity, irritation, allergic response, or other problem or complication, commensurate with a reasonable benefit/risk ratio.
  • responder refers to a subject that displays an improvement in clinical signs and symptoms after receiving anti-TNF therapy for a period of time.
  • the medical community may establish an appropriate period of time for any particular disease or condition, or for any particular patient or patient type.
  • the period of time may be at least 8 weeks.
  • the period of time may be at least 12 weeks.
  • the period of time may be 14 weeks.
  • N on-Responder- refers to a subject that displays a insufficient improvement in clinical signs and symptoms after receiving anti-TNF therapy for a period of time.
  • the medical community may establish an appropriate period of time for any particular disease or condition, or for any particular patient or patient type. To give but a few examples, in some embodiments, the period of time may be at least 8 weeks. In some embodiments, the period of time may be at least 12 weeks. In some embodiments, the period of time may be 14 weeks.
  • reference describes a standard or control relative to which a comparison is performed.
  • an agent, animal, individual, population, sample, sequence or value of interest is compared with a reference or control agent, animal, individual, population, sample, sequence or value.
  • a reference or control is tested and/or determined substantially simultaneously with the testing or determination of interest.
  • a reference or control is a historical reference or control, optionally embodied in a tangible medium.
  • a reference or control is determined or characterized under comparable conditions or circumstances to those under assessment.
  • Therapeutic agent in general refers to any agent that elicits a desired pharmacological effect when administered to an organism.
  • an agent is considered to be a therapeutic agent if it demonstrates a statistically significant effect across an appropriate population.
  • the appropriate population may be a population of model organisms.
  • an appropriate population may be defined by various criteria, such as a certain age group, gender, genetic background, preexisting clinical conditions, etc.
  • a therapeutic agent is a substance that can be used to alleviate, ameliorate, relieve, inhibit, prevent, delay onset of, reduce severity of, and/or reduce incidence of one or more symptoms or features of a disease, disorder, and/or condition.
  • a “therapeutic agent” is an agent that has been or is required to be approved by a government agency before it can be marketed for administration to humans.
  • a “therapeutic agent” is an agent for which a medical prescription is required for administration to humans.
  • therapeutically effective amount refers to an amount of a substance (e.g., a therapeutic agent, composition, and/or formulation) that elicits a desired biological response when administered as part of a therapeutic regimen.
  • a therapeutically effective amount of a substance is an amount that is sufficient, when administered to a subject suffering from or susceptible to a disease, disorder, and/or condition, to treat, diagnose, prevent, and/or delay the onset of the disease, disorder, and/or condition.
  • the effective amount of a substance may vary depending on such factors as the desired biological endpoint, the substance to be delivered, the target cell or tissue, etc.
  • the effective amount of compound in a formulation to treat a disease, disorder, and/or condition is the amount that alleviates, ameliorates, relieves, inhibits, prevents, delays onset of, reduces severity of and/or reduces incidence of one or more symptoms or features of the disease, disorder and/or condition.
  • a therapeutically effective amount is administered in a single dose; in some embodiments, multiple unit doses are required to deliver a therapeutically effective amount.
  • Treat As used herein, the terms “treat,” “treatment,” or “treating” refer to any method used to partially or completely alleviate, ameliorate, relieve, inhibit, prevent, delay onset of, reduce severity of, and/or reduce incidence of one or more symptoms or features of a disease, disorder, and/or condition. Treatment may be administered to a subject who does not exhibit signs of a disease, disorder, and/or condition. In some embodiments, treatment may be administered to a subject who exhibits only early signs of the disease, disorder, and/or condition, for example, for the purpose of decreasing the risk of developing pathology associated with the disease, disorder, and/or condition.
  • FIGs. 1A and IB are plots illustrating ulcerative colitis (UC) response signature genes modules detected using the human interactome (HI) from the UC cohort.
  • the response signature genes found in gene expression data form a significant cluster when mapped to the HI (FIG. 1A) and is much larger than expected by chance (FIG. IB) which reflects an underlying biology of response.
  • FIGs. 2A and 2B are plots illustrating in-cohort performance of response predictions of a near perfect classifier using leave-one-out cross-validation.
  • FIG. 2A is a receiver operating characteristic (ROC) curve and
  • FIG. 2B illustrates the Negative Predictive Value (NPV) vs. True Negative Rate (TNR) curve.
  • the classifier is able to detect 70% of the non-responders with 100% accuracy, and 100% of the non-responders with 90% accuracy.
  • FIGs. 3A and 3B are plots illustrating cross-cohort performance of response prediction classifier when testing on an independent cohort.
  • FIG. 3A is an ROC curve and
  • FIG. 3B illustrates the NPV vs. TNR curve.
  • the classifier is able to detect 50% of the non-responders with 100% accuracy.
  • FIGs. 4A, 4B, 4C, and 4D are plots illustrating in-cohort rheumatoid arthritis (RA) classifier validation using leave-one-out cross validation when training on Feature Set 1 (FIGs. 4A and 4B) and top nine signature genes (FIGs. 4C and 4D).
  • RA in-cohort rheumatoid arthritis
  • FIGs. 5A and 5B are plots illustrating ROC curves of cross cohort classifier test results (in FIG. 5A) and negative predictive performance (in FIG. 5B) for the RA classifier.
  • FIG. 6 is an exemplary workflow for developing a classifier.
  • FIG. 7A-7C provide identification of response discriminatory genes in cohort B.
  • FIG. 7A provides Pearson correlation distribution of gene expression values with response outcomes in observed versus randomized gene expression data. The signal-to-noise ratio of actual and randomized Pearson correlations were derived by dividing the randomized valued by the observed value.
  • FIG. 7B provides top 200 genes with highest signal-noise-ratio were mapped on the network resulting in observation of a significantly large connected component (LCC) shown in shaded region.
  • FIG. 7C provides a heatmap representing the baseline gene expression values of LCC genes used for classifier training across patients. Red corresponds to higher relative expression values and yellow corresponds to lower relative expression values.
  • LCC connected component
  • FIGs. 8A-8D provide cross-cohort performance of response prediction classifiers.
  • FIG. 8 A provides ROC curves of classifier validation in two independent cohorts. Classifier A is the classifier trained on cohort A and validated on cohort B and vice versa.
  • FIG. 8B provides a depiction of accuracy in predicting non-responders (e.g., inadequate) responders to infliximab in an independent cohort.
  • FIG. 8C provides classifier A prediction scores for cohort B patients.
  • FIG. 8D provides Classifier B prediction scores for cohort A patients.
  • FIGs. 9A-9B provide distinct gene lists mapped onto the same network region of the Human Interactome indicated a common underlying biology of response.
  • FIG. 9A illustrates largest connected component formed by the proteins encoded by the response signature genes from the two cohorts. Proteins encoded by cohort A genes are in orange and those encoded by cohort B genes are in blue.
  • FIG. 9B illustrates distribution of LCC size from random expectation.
  • FIG. 10 is a map of the UC response module detected using the Human Interactome from cohort A.
  • the response signature genes found in each cohort form a significant cluster (LCC) that is much bigger than expected by chance and reflects an underlying biology of response to infliximab in UC patients.
  • Proteins are indicated by circles.
  • Physical interactions are indicated by lines. Proteins encoded by the top 200 genes identified in each cluster that lack at least one physical interaction with a protein encoded by another top 200 gene are not shown.
  • FIG. 11 is an example network environment and computing devices for use in various embodiments.
  • FIG. 12 shows an example of a computing device 500 and a mobile computing device 550 that can be used to implement the techniques described in this disclosure.
  • the response rate for patients undergoing anti-TNF therapy is inconsistent. Technologies that reliably identify responsive or non-responsive subjects would be beneficial, as they would avoid wasteful and even potentially damaging administration of therapy to subjects who will not respond, and furthermore would allow timely determination of more appropriate treatment for such subjects.
  • the present disclosure provides such technologies, addressing needs of patients, their families, drug developers, and medical professionals each of whom suffers under the current system.
  • Cancer is typically associated with particular strong driver genes, which dramatically simplifies the analysis required to identify responder vs non-responder patient populations, and significantly improves success rates.
  • diseases associated with more complex genetic (and/or epigenetic) contributions have thus far presented an insurmountable challenge for available technologies.
  • the present disclosure appreciates that machine learning may be useful for finding correlation between datasets of patients, but fails to achieve sufficient predictive accuracy across cohorts. Furthermore, the present disclosure identifies that prioritizing or otherwise focusing on highest fold changes misses subtle but meaningful differences relevant to disease biology. Still further, the present disclosure offers an insight that mapping of genes with altered expression levels onto a human interactome (e.g., that represents experimentally supported physical interactions between cellular components and, in some embodiments, explicitly excludes any theoretical, calculated, or other interaction that has been proposed but not experimentally validated) can provide a useful and effective classifier for defining responders vs. non-responders to anti-TNF therapy. In some embodiments, genes included in such a classifier represent a connected module in the human interactome. Examples of methods of treatment and classifier development related to the present disclosure is found in WO 2019/178546, which is incorporated by reference herein in its entirety.
  • TNF-mediated disorders are currently treated by inhibition of TNF, and in particular by administration of an anti-TNF agent (i.e., by anti-TNF therapy).
  • anti-TNF agents approved for use in the United States include monoclonal antibodies such as adalimumab (Humira®), certolizumab pegol (Cimiza®), infliximab (Remicade®), and decoy circulating receptor fusion proteins such as etanercept (Enbrel®). These agents are currently approved for use in treatment of indications, according to dosing regimens, as set forth below in Table 1:
  • the anti-TNF therapy is or comprises administration of infliximab (Remicade®), adalimumab (Humira®), certolizumab pegol (Cimiza®), etanercept (Enbel®), or biosimilars thereof.
  • the anti- TNF therapy is or comprises administration of infliximab (Remicade®) or adalimumab (Humira®).
  • the anti-TNF therapy is or comprises administration of infliximab (Remicade®).
  • the anti-TNF therapy is or comprises administration of adalimumab (Humira®).
  • the anti-TNF therapy is or comprises administration of a biosimilar anti-TNF agent.
  • the anti-TNF agent is selected from infliximab biosimilars such as CT-P13, BOW015, SB2, Inflectra, Renflexis, and Ixifi, adalimumab biosimilars such as ABP 501 (AMGEVITATM), Adfrar, and HulioTM and etanercept biosimilars such as HD203, SB4 (Benepali®), GP2015, Erelzi, and Intacept.
  • the present disclosure defines patient populations to whom anti- TNF therapy should (or should not) be administered.
  • technologies provided by the present disclosure generate information useful to doctors, pharmaceutical companies, payers, and/or regulatory agencies who wish to ensure that anti-TNF therapy is administered to responder populations and/or is not administered to non-responder populations.
  • provided disclosures are useful in any context in which administration of anti- TNF therapy is contemplated or implemented.
  • provided technologies are useful in the diagnosis and/or treatment of subjects suffering from a disease, disorder, or condition associated with aberrant (e.g., elevated) TNF expression and/or activity.
  • provided technologies are useful in monitoring subjects who are receiving or have received anti- TNF therapy.
  • provided technologies identify whether a subject will or will not respond to a given anti-TNF therapy.
  • the provided technologies identify whether a subject will develop resistance to a given anti-TNF therapy.
  • a subject is suffering from a disease, disorder, or condition selected from rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, Crohn’s disease (adult or pediatric), ulcerative colitis, inflammatory bowel disease, chronic psoriasis, plaque psoriasis, hidradenitis suppurativa, asthma, uveitis, juvenile idiopathic arthritis, vitiligo, Graves’ ophthalmopathy (also known as thyroid eye disease, or Graves’ orbitopathy), and multiple sclerosis.
  • a disease, disorder, or condition selected from rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, Crohn’s disease (adult or pediatric), ulcerative colitis, inflammatory bowel disease, chronic psoriasis, plaque psoriasis, hidradenitis suppurativa, asthma, uveitis, juvenile idiopathic arthritis
  • the disease, disorder, or condition is rheumatoid arthritis. In some embodiments, the disease, disorder, or condition is psoriatic arthritis. In some embodiments, the disease, disorder, or condition is ankylosing spondylitis. In some embodiments, the disease, disorder, or condition is Crohn’s disease. In some embodiments, the disease, disorder, or condition is adult Crohn’s disease. In some embodiments, the disease, disorder, or condition is pediatric Crohn’s disease. In some embodiments, the disease, disorder, or condition is inflammatory bowel disease. In some embodiments, the disease, disorder, or condition is ulcerative colitis. In some embodiments, the disease, disorder, or condition is chronic psoriasis.
  • the disease, disorder, or condition is plaque psoriasis. In some embodiments, the disease, disorder, or condition is hidradenitis suppurativa. In some embodiments, the disease, disorder, or condition is asthma. In some embodiments, the disease, disorder, or condition is uveitis. In some embodiments, the disease, disorder, or condition is juvenile idiopathic arthritis. In some embodiments, the disease, disorder, or condition is vitiligo. In some embodiments, the disease, disorder, or condition is Graves’ ophthalmopathy (also known as thyroid eye disease, or Graves’ orbitopathy). In some embodiments, the disease, disorder, or condition is multiple sclerosis
  • a gene classifier comprises a gene expression response signature (e.g., a set of one or more genes) that distinguishes between responsive and non-responsive prior subjects (i.e., where “prior subjects” refers to subjects who have previously received an anti-TNF therapy, and have been classified as responders or non-responders).
  • a gene expression response signature e.g., a set of one or more genes
  • a particular gene expression response signature classifies responder or non-responder populations for a particular anti-TNF therapy (e.g., a particular anti-TNF agent and/or regimen). In some embodiments, a particular gene expression response signature classifies responder or non-responder populations suffering from a particular disease, disorder, or condition, for a particular anti-TNF therapy (e.g., a particular anti-TNF agent and/or regimen).
  • responder and/or non-responder populations for different anti-TNF therapies may overlap or be co-extensive; in some such embodiments, the present disclosure may provide gene expression response signatures that serve as gene classifiers for responder and/or non-responder populations across anti-TNF therapies.
  • a gene expression response signature is identified by retrospective analysis of gene expression levels in biological samples from subjects who have received anti-TNF therapy (i.e., “prior subjects”) and have been determined to respond (i.e., are responders) or not to respond (i.e., are non-responders). In some embodiments, all such subjects have received the same anti-TNF therapy (optionally for the same or different periods of time); alternatively or additionally, in some embodiments, all such subjects have been diagnosed with the same disease, disorder or condition.
  • subjects whose biological samples are analyzed in the retrospective analysis had received different anti-TNF therapy (e.g., with a different anti-TNF agent and/or according to a different regimen); alternatively or additionally, in some embodiments, subjects whose biological samples are analyzed in the retrospective analysis have been diagnosed with different diseases, disorders, or conditions.
  • a gene expression response signature as described herein is determined by comparison of gene expression levels in the responder vs. non-responder populations whose biological samples are analyzed in a retrospective analysis as described herein.
  • a gene expression response signature comprises genes whose individual expression levels show statistically significant differences between the responder and non- responder populations.
  • a gene expression response signature comprises genes whose linear combination of expression levels show statistically significant differences between the responder and non-responder populations.
  • a gene expression response signature comprises genes whose non-linear combination of expression levels show statistically significant differences between the responder and non-responder populations.
  • a gene expression response signature is incorporated into a classifier for distinguishing between responder and non-responder subjects.
  • a classifier is developed by assessing each of: the one or more genes whose expression levels significantly correlate (e.g., in a linear and/or non-linear manner) to clinical responsiveness or nonresponsiveness (i.e., a gene expression response signature); and optionally one or more of the presence of the one or more single nucleotide polymorphs (SNPs) and at least one clinical characteristic.
  • SNPs single nucleotide polymorphs
  • the present disclosure embodies an insight that the source of a problem with certain prior efforts to identify or provide gene expression response signatures through comparison of gene expression levels in responder vs non-responder populations have emphasized and/or focused on (often solely on) genes that show the largest difference (e.g., greater than 2-fold change) in expression levels between the populations.
  • the present disclosure appreciates that even genes those expression level differences are relatively small (e.g., less than 2-fold change in expression) provide useful information if the difference is significant, and are valuably included in a gene expression response signature in embodiments described herein.
  • the present disclosure embodies an insight that analysis of interaction patterns of genes whose expression levels show statistically significant differences (optionally including small differences) between responder and non-responder populations as described herein provides new and valuable information that materially improves the quality and predictive power of a gene expression response signature.
  • the present disclosure provides technologies that allow practitioners to reliably and consistently predict response to anti-TNF therapy in a cohort of subjects (e.g., treatment naive subjects, i.e., subjects who have not received anti-TNF therapy).
  • the rate of response for some anti-TNF therapies is less than 35% within a given cohort of subjects.
  • the provided technologies allow for prediction of greater than 65% accuracy within a cohort of subjects a response rate (i.e., whether certain subjects will or will not respond to a given therapy).
  • the methods and systems described herein predict 65% or greater the subjects that are responders (i.e., will respond to anti-TNF therapy) within a given cohort.
  • the methods and systems described herein predict 70% or greater the subjects that are responders within a given cohort. In some embodiments, the methods and systems described herein predict 80% or greater the subjects that are responders within a given cohort. In some embodiments, the methods and systems described herein predict 90% or greater the subjects that are responders within a given cohort. In some embodiments, the methods and systems described herein predict 100% the subjects that are responders within a given cohort. In some embodiments, the methods and systems described herein predict 65% or greater the subjects that are non-responders (i.e., will not respond to anti-TNF therapy) within a given cohort. In some embodiments, the methods and systems described herein predict 70% or greater the subjects that are non-responders within a given cohort.
  • the methods and systems described herein predict 80% or greater the subjects that are non-responders within a given cohort. In some embodiments, the methods and systems described herein predict 90% or greater the subjects that are non-responders within a given cohort. In some embodiments, the methods and systems described herein predict 100% of the subjects that are non-responders within a given cohort.
  • a gene expression response signature is developed by assessing one or more genes selected from Table A or Table B
  • a gene expression response signature is developed by assessing one or more genes selected from Table C and Table D:
  • a gene expression response signature is developed by assessing one or more genes selected from Table E:
  • a gene expression response signature is developed by assessing SUMO2 and/or PKM.
  • a provided gene expression response signature is a gene or set of genes that can be used to determine whether a subject will or will not respond to a particular therapy (e.g., anti-TNF therapy).
  • a gene expression response signature itself can be a classifier, or can otherwise be part of a classifier that distinguishes between responsive and non-responsive subjects.
  • a gene expression response signature can be identified using mRNA and/or protein expression datasets, for example as may be or have been prepared from validated biological data (e.g., biological data derived from publicly available databases such as Gene Expression Omnibus (“GEO”))-
  • GEO Gene Expression Omnibus
  • a gene expression response signature may be derived by comparing gene expression levels of known responsive and known non-responsive prior subjects to a specific therapy (e.g., anti-TNF therapy).
  • certain genes i.e., signature genes are selected from this cohort of gene expression data to be used in developing the gene expression response signature.
  • signature genes are identified by methods analogous to those reported by Santolini, “A personalized, multiomics approach identifies genes involved in cardiac hypertrophy and heart failure,” Systems Biology and Applications, (2016)4:12; doi:10.1038/s41540-018-0046-3, which is incorporated herein by reference.
  • signature genes are identified by comparing gene expression levels of known responsive and non-responsive prior subjects and identifying significant changes between the two groups, wherein the significant changes can be large differences in expression (e.g., greater than 2-fold change), small differences in expression (e.g., less than 2-fold change), or both.
  • genes are ranked by significance of difference in expression. In some embodiments, significance is measured by Pearson correlation between gene expression and response outcome.
  • signature genes are selected from the ranking by significance of difference in expression. In some embodiments, the number of signature genes selected is less than the total number of genes analyzed. In some embodiments, 200 signature genes or less are selected. In some embodiments 100 genes or less are selected. [0074] In some embodiments, signature genes are selected in conjunction with their location on a human interactome (HI), a map of protein-protein interactions. Use of the HI in this way encompasses a recognition that mRNA activity is dynamic and determines the actual over and under expression of proteins critical to understanding certain diseases. In some embodiments, genes associated with response to certain therapies (i.e., anti-TNF therapy) may cluster (i.e., form a cluster of genes) in discrete modules on the HI map.
  • therapies i.e., anti-TNF therapy
  • a gene expression response signature is derived from signature genes selected from the cluster of genes on the HI map. Accordingly, in some embodiments, a gene expression response signature is derived from a cluster of genes associated with response to anti-TNF therapy on a human interactome map.
  • genes associated with response to certain therapies exhibit certain topological properties when mapped onto a human interactome map.
  • a plurality of genes associated with response to anti-TNF therapy and characterized by their position (i.e., topological properties, e.g., their proximity to one another) on a human interactome map exhibit certain topological properties when mapped onto a human interactome map.
  • genes associated with response to certain therapies may exist within close proximity to one another on the HI map. Said proximal genes, do not necessarily need to share fundamental underlying disease biology. That is, in some embodiments, proximal genes do not share significant protein interaction. Accordingly, in some embodiments, the gene expression response signature is derived from genes that are proximal on a human interactome map. In some embodiments, the gene expression response signature is derived from certain other topological features on a human interactome map.
  • genes associated with response to certain therapies may be determined by Diffusion State Distance (DSD) (see Cao, et al., PLOS One, 8(10): e76339 (Oct. 23, 2013)) when used in combination with the HI map.
  • DSD Diffusion State Distance
  • signature genes are selected by (1) ranking genes based on the significance of difference of expression of genes as compared to known responders and known non-responders; (2) selecting genes from the ranked genes and mapping the selected genes onto a human interactome map; and (3) selecting signature genes from the genes mapped onto the human interactome map.
  • signature genes e.g., selected from the Santolini method, or using various network topological properties including, but not limited to, clustering, proximity and diffusion-based methods
  • a probabilistic neural network to thereby provide (i.e., “train”) the gene expression response signature.
  • the probabilistic neural network implements the algorithm proposed by D. F.
  • the probabilistic neural network is written in the R-statistical language, and knowing a set of observations described by a vector of quantitative variables, classifies observations into a given number of groups (e.g., responders and non-responders). The algorithm is trained with the data set of signature genes taken from known responders and non-responders and guesses new observations that are provided.
  • the probabilistic neural network is one derived from https://CRAN.R--project.org/package-pnn.
  • a gene expression response signature can be trained in the probabilistic neural network using a cohort of known responders and non- responders using leave-one-out cross and/or k-fold cross validation.
  • such a process leaves one sample out (i.e., leave-one-out) of the analysis and trains the classifier only based on the remaining samples.
  • the updated classifier is then used to predict a probability of response for the sample that’s left out.
  • such a process can be repeated iteratively, for example, until all samples have been left out once.
  • such a process randomly partitions a cohort of known responders and non- responders into k equal sizes groups.
  • the outcome is a probability score for each sample in the training set. Such probability scores can correlate with actual response outcome.
  • a Recursive Operating Curves (ROC) can be used to estimate the performance of the classifier.
  • AUC Area Under Curve
  • NPV Negative Predictive Value
  • a classifier can be tested in a completely independent (i.e., blinded) cohort to, for example, confirm the suitability (i.e., using leave-one-out and/or k-fold cross validation).
  • provided methods further comprise one or more steps of validating a gene expression response signature, for example, by assigning probability of response to a group of known responders and non-responders; and checking the gene expression response signature against a blinded group of responders and non-responders.
  • the output of these processes is a trained gene expression response signature useful for establishing whether a subject will or will not respond to a particular therapy (e.g., anti-TNF therapy).
  • a gene expression response signature is validated using a cohort of subjects having previously been treated with anti-TNF therapy, but is independent from the cohort of subjects used to prepare the classifier. In some embodiments, a gene expression response signature is considered “validated” when 90% or greater of non-responding subjects are predicted with 50% or greater accuracy within the validating cohort.
  • the gene expression response signature predicts responsiveness of subjects with at least 50% accuracy across a population of subjects. In some embodiments, the gene expression response signature predicts responsiveness of subjects with at least 60% accuracy predicting responsiveness across a population of subjects. In some embodiments, the gene expression response signature predicts responsiveness of subjects with at least 80% accuracy across a population of subjects. In some embodiments, the gene expression response signature predicts responsiveness of subjects with at least 90% accuracy across a population of subjects. In some embodiments, the gene expression response signature predicts responsiveness of subjects with at least 95% accuracy across a population of subjects. In some embodiments, the gene expression response signature predicts responsiveness of subjects with at least 97% accuracy across a population of subjects. In some embodiments, the gene expression response signature predicts responsiveness of subjects with at least 98% accuracy across a population of subjects. In some embodiments, the gene expression response signature predicts responsiveness of subjects with at least 99% accuracy across a population of subjects.
  • the gene expression response signature is established to distinguish between responsive and non-responsive prior subjects who have received a type of therapy, e.g., anti-TNF therapy.
  • This gene expression response signature derived from these prior responders and non-responders, is used to classify subjects (outside of the previously-identify cohorts) as responders or non-responders, i.e., can predict whether a subject will or will not respond to a given therapy.
  • a classifier is validated by analyzing gene expression levels in biological samples from a first cohort of subjects who have previously received the anti-TNF therapy (“prior subjects”) and have been determined to respond (“responders”) or not to respond (“non-responders”) to the anti-TNF therapy to identify genes that show statistically significant differences in expression level between the responders and the non-responders (“signature genes”).
  • signature genes are mapped onto a biological network (e.g., a human interactome).
  • a subset of signature genes are selected on the basis of their connectivity in the biological network to provide a candidate gene list.
  • a method of validating a classifier comprising training a classifier (e.g., an non-validated classifier) on expression levels of the genes of the candidate gene list from the first cohort of subjects (e.g., prior subjects, that is, subjects who have previously been classified as responsive or non- responsive to anti-TNF therapy) to identify a subset of the prior subjects having a pattern of expression of the candidate gene list indicative that the subset of prior subjects are unlikely to respond to the anti-TNF therapy, to thereby obtain a trained classifier.
  • a classifier e.g., an non-validated classifier
  • a trained classifier is validated via analysis of a second cohort comprising an independent and blinded group of responders and non-responders, and selecting a cutoff score such that the validated classifier distinguishes about 50% of prior subjects that are non-responsive (i.e., have a TNR of about 0.5) to the anti-TNF therapy.
  • a validated classifier distinguishes about 65% of prior subjects that are non-responsive (i.e., have a TNR of about 0.65) to the anti-TNF therapy.
  • a validated classifier distinguishes about 70% of prior subjects that are non-responsive (i.e., have a TNR of about 0.7) to the anti-TNF therapy.
  • a validated classifier distinguishes about 80% of prior subjects that are non-responsive (i.e., have a TNR of about 0.8) to the anti-TNF therapy. In some embodiments, a validated classifier distinguishes about 90% of prior subjects that are non- responsive (i.e., have a TNR of about 0.9) to the anti-TNF therapy. In some embodiments, a validated classifier distinguishes about 95% of prior subjects that are non-responsive (i.e., have a TNR of about 0.95) to the anti-TNF therapy. In some embodiments, a validated classifier distinguishes about 100% of prior subjects that are non-responsive (i.e., have a TNR of about 1.0) to the anti-TNF therapy.
  • a validated classifier distinguishes at least 50% of prior subjects that are non-responsive to the anti-TNF therapy with at least 60% NPV (i.e., has an NPV of about 0.6). In some embodiments, a validated classifier distinguishes at least 50% of prior subjects that are non-responsive to the anti-TNF therapy with at least 70% NPV (i.e., has an NPV of about 0.7). In some embodiments, a validated classifier distinguishes at least 50% of prior subjects that are non-responsive to the anti-TNF therapy with at least 80% NPV (i.e., has an NPV of about 0.8).
  • a validated classifier distinguishes at least 50% of prior subjects that are non- responsive to the anti-TNF therapy with at least 90% NPV (i.e., has an NPV of about 0.9). In some embodiments, a validated classifier distinguishes at least 50% of prior subjects that are non- responsive to the anti-TNF therapy with at least 95% NPV (i.e., has an NPV of about 0.95). In some embodiments, a validated classifier distinguishes at least 50% of prior subjects that are non- responsive to the anti-TNF therapy with at least 100% NPV (i.e., has an NPV of about 1.0).
  • Detecting gene classifiers in subjects is a routine matter for those of skill in the art.
  • a variety of methods can be used to determine whether a subject or group of subjects express the established gene classifier.
  • a practitioner can obtain a blood or tissue sample from the subject prior to administering of therapy, and extract and analyze mRNA profiles from said blood or tissue sample.
  • RNA sequencing assays such as assays based on microarray, bead array, and NANOSTRING (direct detection of color-coded hybridized probes) technologies
  • RNA sequencing assays such as real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) or reverse transcription loop mediated isothermal amplification (RT-LAMP)
  • mass spectrometry-based protein detection assays such as targeted mass spectrometry (MRM or SRM) or immunoaffinity liquid chromatography - tandem mass spectrometry (IA LC-MS/MC)
  • immunoassay-based protein detection assays such as enzyme-linked immunosorbent assays (ELISA), immunohistochemistry, or flow cytometry).
  • the present disclosure provides methods of determining whether a subject is classified as a responder or non-responder, comprising measuring gene expression by at least one of a microarray, RNA sequencing, real-time quantitative reverse transcription PCR (qRT-PCR), bead array, and ELISA.
  • the present disclosure provides methods of determining whether a subject is classified as a responder or non-responder comprising measuring gene expression of a subject by RNA sequencing (i.e., RNAseq).
  • the provided technologies provide methods comprising determining, prior to administering anti-TNF therapy, that a subject displays a gene expression response signature associated with response to anti-TNF therapy; and administering the anti-TNF therapy to the subject determined to display the gene expression response signature. In some embodiments, the provided technologies provide methods comprising determining, prior to administering anti-TNF therapy, that a subject does not display the gene expression response signature; and administering a therapy alternative to anti-TNF therapy to the subject determine not to display the gene expression signature.
  • the therapy alternative to anti-TNF therapy is selected from rituximab (Rituxan®), sarilumab (Kevzara®), tofacitinib citrate (Xeljanz®), lefunomide (Arava®), vedolizumab (Entyvio®), tocilizumab (Actemra®), anakinra (Kineret®), and abatacept (Orencia®).
  • gene expression is measured by subtracting background data, correcting for batch effects, and dividing by mean expression of housekeeping genes.
  • background subtraction refers to subtracting the average fluorescent signal arising from probe features on a chip not complimentary to any mRNA sequence, i.e. signals that arise from non-specific binding, from the fluorescence signal intensity of each probe feature.
  • the background subtraction can be performed with different software packages, such as Affymetrix Gene Expression Console. Housekeeping genes are involved in basic cell maintenance and, therefore, are expected to maintain constant expression levels in all cells and conditions.
  • the expression level of genes of interest i.e., those in the response signature, can be normalized by dividing the expression level by the average expression level across a group of selected housekeeping genes. This housekeeping gene normalization procedure calibrates the gene expression level for experimental variability. Further, normalization methods such as robust multi-array average (“RMA”) correct for variability across different batches of microarrays, are available in R packages recommended by either Illumina and/or Affymetrix platforms. The normalized data is log transformed, and probes with low detection rates across samples are removed. Furthermore, probes with no available genes symbol or Entrez ID are removed from the analysis.
  • RMA robust multi-array average
  • the present disclosure provides a kit comprising means for detecting a gene expression response signature established to distinguish between responsive and non- responsive prior subjects who have received anti-TNF therapy.
  • the kit facilitates comparison levels of gene expression of a subject to the gene expression response signature (i.e., the gene classifier) established to distinguish between responsive and non- responsive prior subjects who have received anti-TNF therapy.
  • a kit comprises a set of reagents for detecting an expression level of one or more genes in a gene expression response signature described herein.
  • the present disclosure provides a kit comprising means for detecting a gene expression response signature established to distinguish between responsive and non- responsive prior subjects suffering from a disease, disorder, or condition and who have received anti-TNF therapy, wherein the gene expression response signature comprises an expression level of PKM and SUMO2.
  • the present disclosure provides a kit for evaluating a likelihood that a patient having an autoimmune disorder will not respond to an anti-TNF therapy, the kit comprising a set of reagents for detecting an expression level of one or more genes selected from the group consisting of
  • a kit comprises a set of reagents for detecting and/or measuring expression level of one or more genes described herein.
  • a kit comprises components for hybridization-based RNA detection assays (such as assays based on microarray, bead array, and NANOSTRING (direct detection of color-coded hybridized probes) technologies), RNA sequencing assays, amplification-based RNA detection assays (such as real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) or reverse transcription loop mediated isothermal amplification (RT-LAMP)), mass spectrometry-based protein detection assays (such as targeted mass spectrometry (MRM or SRM) or immunoaffinity liquid chromatography - tandem mass spectrometry (IA LC-MS/MC)) and immunoassay-based protein detection assays (such as enzyme-linked immunosorbent assays (ELISA), immunohistochemistry, or flow cytometry).
  • RNA detection assays such as assays based on micro
  • the gene expression response signature comprises an expression level of (1) PKM and SUMO2; and (2) one or more genes selected from
  • the gene expression response signature comprises an expression level of (1) PKM and SUMO2; and (2) one or more genes selected from
  • the gene expression response signature comprises an expression level of (1) PKM and SUMO2; and (2) one or more genes selected from
  • the present disclosure provides technologies for predicting responsiveness to anti-TNF therapies.
  • provided technologies exhibit consistency and/or accuracy across cohorts superior to previous methodologies.
  • the present disclosure provides technologies for patient stratification, defining and/or distinguishing between responder and non-responder populations.
  • the present disclosure provides methods for treating subjects with anti-TNF therapy, which methods, in some embodiments, comprise a step of: administering the anti-TNF therapy to subjects who have been determined not to display a gene expression response signature established to distinguish between responsive and non-responsive prior subjects who have received the anti- TNF therapy.
  • the gene expression response signature includes a plurality of genes established to distinguish between responsive and non-responsive prior subjects for a given anti-TNF therapy.
  • the plurality of genes are determined to cluster with one another in a human interactome map.
  • the plurality of genes are proximal in a human interactome map.
  • the plurality of genes comprise genes that are shown to be statistically significantly different between responsive and non- responsive prior subjects.
  • the present disclosure provides technologies for monitoring therapy for a given subject or cohort of subjects.
  • gene expression level can change over time, it may, in some instances, be necessary or desirable to evaluate a subject at one or more points in time, for example, at specified and or periodic intervals.
  • the present disclosure provides a method of treating a subject suffering from a disease, disorder, or condition (e.g., inflammatory bowel disease, ulcerative colitis or Crohn’s disease) with an anti-TNF therapy, the method comprising a step of: administering the anti-TNF therapy to subjects who have been determined not to display a gene expression response signature established to distinguish between responsive and non-responsive prior subjects who have received the anti-TNF therapy, wherein the gene expression response signature comprises an expression level of PKM and SUMO2.
  • a disease, disorder, or condition e.g., inflammatory bowel disease, ulcerative colitis or Crohn’s disease
  • the present disclosure provides A method of treating a subject suffering from a disease, disorder, or condition with an anti-TNF therapy, the method comprising a step of: administering the anti-TNF therapy to subjects who have been determined to be responsive via a classifier determined to distinguish between responsive and non-responsive subjects who have received the anti-TNF therapy (“prior subject”), and the classifier measures expression of one or more genes (e.g., two or more, three or more, four or more, five or more, six or more, or substantially all) selected from:
  • the classifier measures expression of one or more genes (e.g., two or more, three or more, four or more, five or more, six or more, or substantially all) selected from:
  • the classifier measures expression of one or more genes (e.g., two or more, three or more, four or more, five or more, six or more, or substantially all) selected from:
  • the classifier measures expression of SUMO2 and PKM.
  • the classifier measures expression levels of two or more genes selected from:
  • a gene expression response signature comprises an expression level of (1) PKM and SUMO2, and (2) one or more genes selected from
  • a gene expression response signature comprises an expression level of (1) PKM and SUMO2, and (2) one or more genes selected from
  • the gene expression response signature comprises an expression level of (1) PKM and SUMO2; and (2) one or more genes selected from
  • repeated monitoring under time permits or achieves detection of one or more changes in a subject’s gene expression profile or characteristics that may impact ongoing treatment regimens.
  • a change is detected in response to which particular therapy administered to the subject is continued, is altered, or is suspended.
  • therapy may be altered, for example, by increasing or decreasing frequency and/or amount of administration of one or more agents or treatments with which the subject is already being treated.
  • therapy may be altered by addition of therapy with one or more new agents or treatments.
  • therapy may be altered by suspension or cessation of one or more particular agents or treatments.
  • a given anti-TNF therapy can then be administered.
  • a given interval e.g., every six months, every year, etc.
  • the subject can be tested again to ensure that they still qualify as “responsive” to a given anti-TNF therapy.
  • the subject’s therapy can be altered to suit the change in gene expression.
  • the present disclosure provides methods of administering therapy to a subject previously determined not to display a gene expression response signature associated with anti-TNF therapy, wherein the subject does not displays a gene expression response signature associated with response to anti-TNF therapy.
  • the present disclosure provides methods of treating subjects with anti-TNF therapy, the method comprising a step of: administering the anti-TNF therapy to subjects who have been determined not to display a gene expression response signature established to distinguish between responsive and non-responsive prior subjects who have received the anti- TNF therapy.
  • the present disclosure provides methods further comprising determining, prior to the administering, that a subject does not display the gene expression response signature; and administering the anti-TNF therapy to the subject determined not to display the gene expression response signature.
  • the present disclosure provides methods further comprising determining, prior to the administering, that a subject does display the gene expression response signature; and administering a therapy alternative to anti-TNF therapy to the subject determined to display the gene expression response signature.
  • the gene expression response signature was established to distinguish between responsive and non-responsive prior subjects who have received the anti-TNF therapy by a method comprising steps of: mapping genes whose expression levels significantly correlate to clinical responsiveness or non-responsiveness to a human interactome map; and selecting a plurality of genes determined to cluster with one another in a human interactome map, thereby establishing the gene expression response signature.
  • the gene expression response signature was established to distinguish between responsive and non-responsive prior subjects who have received the anti-TNF therapy by a method comprising steps of: mapping genes whose expression levels significantly correlate to clinical responsiveness or non-responsiveness to a human interactome map; and selecting a plurality of genes determined to be proximal with one another in a human interactome map, thereby establishing the gene expression response signature.
  • the present disclosure provides methods further comprising steps of: validating the gene expression response signature by assigning probability of response to a group of known responders and non-responders; and checking the gene expression response signature against a blinded group of responders and non-responders.
  • the subjects to whom the anti-TNF therapy is administered are suffering from the same disease, disorder or condition as the prior responsive and non-responsive prior subjects.
  • the gene expression response signature includes expression levels of a plurality of genes derived from a cluster of genes associated with response to anti-TNF therapy on a human interactome map.
  • the gene expression response signature includes expression levels of a plurality of genes proximal to genes associated with response to anti-TNF therapy on a human interactome map.
  • the gene expression response signature includes expression levels of a plurality of genes determined to cluster with one another in a human interactome map. [00124] In some embodiments, the gene expression response signature includes expression levels of a plurality of genes that are proximal in a human interactome map.
  • genes of the subject are measured by at least one of a microarray, RNA sequencing, real-time quantitative reverse transcription PCR (qRT-PCR), bead array, ELISA, and protein expression.
  • a microarray RNA sequencing, real-time quantitative reverse transcription PCR (qRT-PCR), bead array, ELISA, and protein expression.
  • qRT-PCR real-time quantitative reverse transcription PCR
  • a disease, disorder, or condition described herein is an autoimmune disease.
  • the subject suffers from a disease, disorder, or condition selected from rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, Crohn’s disease (adult or pediatric), ulcerative colitis, inflammatory bowel disease, chronic psoriasis, plaque psoriasis, hidradenitis suppurativa, asthma, uveitis, juvenile idiopathic arthritis, vitiligo, Graves’ ophthalmopathy (also known as thyroid eye disease, or Graves’ orbitopathy), and multiple sclerosis.
  • a disease, disorder, or condition selected from rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, Crohn’s disease (adult or pediatric), ulcerative colitis, inflammatory bowel disease, chronic psoriasis, plaque psoriasis, hidradenitis suppurativa, asthma, uveitis, juvenile idiopathic arthritis,
  • the subject suffers from an autoimmune disease selected from rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, Crohn’s disease (adult or pediatric), ulcerative colitis, inflammatory bowel disease, chronic psoriasis, plaque psoriasis, hidradenitis suppurativa, asthma, uveitis, juvenile idiopathic arthritis, vitiligo, Graves’ ophthalmopathy (also known as thyroid eye disease, or Graves’ orbitopathy), and multiple sclerosis.
  • an autoimmune disease selected from rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, Crohn’s disease (adult or pediatric), ulcerative colitis, inflammatory bowel disease, chronic psoriasis, plaque psoriasis, hidradenitis suppurativa, asthma, uveitis, juvenile idiopathic arthritis, vitiligo, Grave
  • the anti-TNF therapy is or comprises administration of infliximab, adalimumab, etanercept, cirtolizumab pegol, golilumab, or biosimilars thereof. In some embodiments, the anti-TNF therapy is or comprises administration of infliximab or adalimumab.
  • the present disclosure provides, in a method of administering anti-TNF therapy, the improvement that comprises administering the therapy selectively to subjects who have been determined to display a gene expression response signature established to distinguish between responsive and non-responsive prior subjects who have received the anti-TNF therapy.
  • the subjects to whom the anti-TNF therapy is administered are suffering from the same disease, disorder or condition as the prior responsive and non-responsive prior subjects.
  • the gene expression response signature includes expression levels of a plurality of genes derived from a cluster of genes associated with response to anti-TNF therapy on a human interactome map.
  • the anti-TNF therapy is or comprises administration of infliximab, adalimumab, etanercept, cirtolizumab pegol, golilumab, or biosimilars thereof.
  • the disease, disorder, or condition is rheumatoid arthritis.
  • the disease, disorder, or condition is ulcerative colitis.
  • the present disclosure provides use of an anti-TNF therapy in the treatment of a subject determined to display a gene expression response signature established to distinguish between responsive and non-responsive prior subjects who have received the anti- TNF therapy.
  • determining that the subject displays the gene expression response signature.
  • determining that the subject does not display the gene expression response signature.
  • the gene expression response signature was established to distinguish between responsive and non-responsive prior subjects who have received the anti-TNF therapy by a method comprising steps of: mapping genes whose expression levels significantly correlate to clinical responsiveness or non-responsiveness to a human interactome map; and selecting a plurality of genes determined to cluster with one another in a human interactome map, thereby establishing the gene expression response signature.
  • the gene expression response signature was established to distinguish between responsive and non-responsive prior subjects who have received the anti-TNF therapy by a method comprising steps of: mapping genes whose expression levels significantly correlate to clinical responsiveness or non-responsiveness to a human interactome map; and selecting a plurality of genes determined to be proximal with one another in a human interactome map, thereby establishing the gene expression response signature.
  • the gene expression response signature was established to distinguish between responsive and non-responsive prior subjects who have received the anti-TNF therapy by the method further comprising steps of validating the gene expression response signature by assigning probability of response to a group of known responders and non-responders; and checking the gene expression response signature against a blinded group of responders and non-responders.
  • the present disclosure provides a method of of validating response to an anti-TNF therapy in a subject, the method comprising: receiving, by a processor of a computing device, a gene expression response signature determined to distinguish between responsive and non-responsive subjects to the anti-TNF therapy; analyzing, by the processor, gene expression of the subject relative to the gene expression response signature to determine whether the subject expresses the gene expression response signature, wherein the gene expression response signature comprising one or more genes selected from:
  • the present disclosure provides a system for determining or validating responsiveness to anti-TNF therapy for a subject suffering from a disease, the system comprising: a processor of a computing device; and a memory having instructions stored thereon, wherein the instructions, when executed by the processor cause the processor to perform the steps of methods described herein.
  • the present disclosure provides a system for determining or validating responsiveness to anti-TNF therapy for a subject suffering from a disease, the system comprising: a processor of a computing device; and a memory having instructions stored thereon, wherein the instructions, when executed by the processor cause the processor to perform the following steps: receiving, by the processor, a gene expression response signature determined to distinguish between responsive and non-responsive subjects to the anti-TNF therapy; analyzing, by the processor, gene expression of the subject relative to the gene expression response signature to determine whether the subject expresses the gene expression response signature, wherein the gene expression response signature comprising one or more genes selected from
  • the cloud computing environment 400 may include one or more resource providers 402a, 402b, 402c (collectively, 402). Each resource provider 402 may include computing resources.
  • computing resources may include any hardware and/or software used to process data.
  • computing resources may include hardware and/or software capable of executing algorithms, computer programs, and/or computer applications.
  • exemplary computing resources may include application servers and/or databases with storage and retrieval capabilities.
  • Each resource provider 402 may be connected to any other resource provider 402 in the cloud computing environment 400.
  • the resource providers 402 may be connected over a computer network 408.
  • Each resource provider 402 may be connected to one or more computing device 404a, 404b, 404c (collectively, 404), over the computer network 408.
  • the cloud computing environment 400 may include a resource manager 406.
  • the resource manager 406 may be connected to the resource providers 402 and the computing devices 404 over the computer network 408.
  • the resource manager 406 may facilitate the provision of computing resources by one or more resource providers 402 to one or more computing devices 404.
  • the resource manager 406 may receive a request for a computing resource from a particular computing device 404.
  • the resource manager 406 may identify one or more resource providers 402 capable of providing the computing resource requested by the computing device 404.
  • the resource manager 406 may select a resource provider 402 to provide the computing resource.
  • the resource manager 406 may facilitate a connection between the resource provider 402 and a particular computing device 404.
  • FIG. 12 shows an example of a computing device 500 and a mobile computing device 550 that can be used to implement the techniques described in this disclosure.
  • the computing device 500 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers.
  • the mobile computing device 550 is intended to represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart-phones, and other similar computing devices.
  • the components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to be limiting.
  • the computing device 500 includes a processor 502, a memory 504, a storage device 506, a high-speed interface 508 connecting to the memory 504 and multiple high-speed expansion ports 510, and a low-speed interface 512 connecting to a low-speed expansion port 514 and the storage device 506.
  • Each of the processor 502, the memory 504, the storage device 506, the high-speed interface 508, the high-speed expansion ports 510, and the low-speed interface 512 are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate.
  • the processor 502 can process instructions for execution within the computing device 500, including instructions stored in the memory 504 or on the storage device 506 to display graphical information for a GUI on an external input/output device, such as a display 516 coupled to the high-speed interface 508.
  • an external input/output device such as a display 516 coupled to the high-speed interface 508.
  • multiple processors and/or multiple buses may be used, as appropriate, along with multiple memories and types of memory.
  • multiple computing devices may be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system).
  • a processor any number of processors (one or more) of any number of computing devices (one or more).
  • a function is described as being performed by “a processor”, this encompasses embodiments wherein the function is performed by any number of processors (one or more) of any number of computing devices (one or more) (e.g., in a distributed computing system).
  • the memory 504 stores information within the computing device 500.
  • the memory 504 is a volatile memory unit or units.
  • the memory 504 is a non-volatile memory unit or units.
  • the memory 504 may also be another form of computer-readable medium, such as a magnetic or optical disk.
  • the storage device 506 is capable of providing mass storage for the computing device 500.
  • the storage device 506 may be or contain a computer- readable medium, such as a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations. Instructions can be stored in an information carrier.
  • the instructions when executed by one or more processing devices (for example, processor 502), perform one or more methods, such as those described above.
  • the instructions can also be stored by one or more storage devices such as computer- or machine- readable mediums (for example, the memory 504, the storage device 506, or memory on the processor 502).
  • the high-speed interface 508 manages bandwidth-intensive operations for the computing device 500, while the low-speed interface 512 manages lower bandwidth-intensive operations. Such allocation of functions is an example only.
  • the highspeed interface 508 is coupled to the memory 504, the display 516 (e.g., through a graphics processor or accelerator), and to the high-speed expansion ports 510, which may accept various expansion cards (not shown).
  • the low-speed interface 512 is coupled to the storage device 506 and the low-speed expansion port 514.
  • the low-speed expansion port 514 which may include various communication ports (e.g., USB, Bluetooth®, Ethernet, wireless Ethernet) may be coupled to one or more input/output devices, such as a keyboard, a pointing device, a scanner, or a networking device such as a switch or router, e.g., through a network adapter.
  • input/output devices such as a keyboard, a pointing device, a scanner, or a networking device such as a switch or router, e.g., through a network adapter.
  • the computing device 500 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a standard server 520, or multiple times in a group of such servers. In addition, it may be implemented in a personal computer such as a laptop computer 522. It may also be implemented as part of a rack server system 524. Alternatively, components from the computing device 500 may be combined with other components in a mobile device (not shown), such as a mobile computing device 550. Each of such devices may contain one or more of the computing device 500 and the mobile computing device 550, and an entire system may be made up of multiple computing devices communicating with each other.
  • the mobile computing device 550 includes a processor 552, a memory 564, an input/output device such as a display 554, a communication interface 566, and a transceiver 568, among other components.
  • the mobile computing device 550 may also be provided with a storage device, such as a micro-drive or other device, to provide additional storage.
  • a storage device such as a micro-drive or other device, to provide additional storage.
  • Each of the processor 552, the memory 564, the display 554, the communication interface 566, and the transceiver 568, are interconnected using various buses, and several of the components may be mounted on a common motherboard or in other manners as appropriate.
  • the processor 552 can execute instructions within the mobile computing device 550, including instructions stored in the memory 564.
  • the processor 552 may be implemented as a chipset of chips that include separate and multiple analog and digital processors.
  • the processor 552 may provide, for example, for coordination of the other components of the mobile computing device 550, such as control of user interfaces, applications run by the mobile computing device 550, and wireless communication by the mobile computing device 550.
  • the processor 552 may communicate with a user through a control interface 558 and a display interface 556 coupled to the display 554.
  • the display 554 may be, for example, a TFT (Thin-Film-Transistor Eiquid Crystal Display) display or an OEED (Organic Eight Emitting Diode) display, or other appropriate display technology.
  • the display interface 556 may comprise appropriate circuitry for driving the display 554 to present graphical and other information to a user.
  • the control interface 558 may receive commands from a user and convert them for submission to the processor 552.
  • an external interface 562 may provide communication with the processor 552, so as to enable near area communication of the mobile computing device 550 with other devices.
  • the external interface 562 may provide, for example, for wired communication in some implementations, or for wireless communication in other implementations, and multiple interfaces may also be used.
  • the memory 564 stores information within the mobile computing device 550.
  • the memory 564 can be implemented as one or more of a computer-readable medium or media, a volatile memory unit or units, or a non-volatile memory unit or units.
  • An expansion memory 574 may also be provided and connected to the mobile computing device 550 through an expansion interface 572, which may include, for example, a SIMM (Single In Line Memory Module) card interface.
  • SIMM Single In Line Memory Module
  • the expansion memory 574 may provide extra storage space for the mobile computing device 550, or may also store applications or other information for the mobile computing device 550.
  • the expansion memory 574 may include instructions to carry out or supplement the processes described above, and may include secure information also.
  • the expansion memory 574 may be provide as a security module for the mobile computing device 550, and may be programmed with instructions that permit secure use of the mobile computing device 550.
  • secure applications may be provided via the SIMM cards, along with additional information, such as placing identifying information on the SIMM card in a non-hackable manner.
  • the memory may include, for example, flash memory and/or NVRAM memory (non-volatile random access memory), as discussed below.
  • instructions are stored in an information carrier, that the instructions, when executed by one or more processing devices (for example, processor 552), perform one or more methods, such as those described above.
  • the instructions can also be stored by one or more storage devices, such as one or more computer- or machine-readable mediums (for example, the memory 564, the expansion memory 574, or memory on the processor 552).
  • the instructions can be received in a propagated signal, for example, over the transceiver 568 or the external interface 562.
  • the mobile computing device 550 may communicate wirelessly through the communication interface 566, which may include digital signal processing circuitry where necessary.
  • the communication interface 566 may provide for communications under various modes or protocols, such as GSM voice calls (Global System for Mobile communications), SMS (Short Message Service), EMS (Enhanced Messaging Service), or MMS messaging (Multimedia Messaging Service), CDMA (code division multiple access), TDMA (time division multiple access), PDC (Personal Digital Cellular), WCDMA (Wideband Code Division Multiple Access), CDMA2000, or GPRS (General Packet Radio Service), among others.
  • GSM voice calls Global System for Mobile communications
  • SMS Short Message Service
  • EMS Enhanced Messaging Service
  • MMS messaging Multimedia Messaging Service
  • CDMA code division multiple access
  • TDMA time division multiple access
  • PDC Personal Digital Cellular
  • WCDMA Wideband Code Division Multiple Access
  • CDMA2000 Code Division Multiple Access
  • GPRS General Packet Radio Service
  • a GPS (Global Positioning System) receiver module 570 may provide additional navigation- and location-related wireless data to the mobile computing device 550, which may be used as appropriate by applications running on the mobile computing device 550.
  • the mobile computing device 550 may also communicate audibly using an audio codec 560, which may receive spoken information from a user and convert it to usable digital information.
  • the audio codec 560 may likewise generate audible sound for a user, such as through a speaker, e.g., in a handset of the mobile computing device 550.
  • Such sound may include sound from voice telephone calls, may include recorded sound (e.g., voice messages, music files, etc.) and may also include sound generated by applications operating on the mobile computing device 550.
  • the mobile computing device 550 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a cellular telephone 580. It may also be implemented as part of a smart-phone 582, personal digital assistant, or other similar mobile device.
  • Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof.
  • ASICs application specific integrated circuits
  • These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
  • machine -readable medium and computer- readable medium refer to any computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal.
  • machine-readable signal refers to any signal used to provide machine instructions and/or data to a programmable processor.
  • the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer.
  • a display device e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor
  • a keyboard and a pointing device e.g., a mouse or a trackball
  • Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.
  • the systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components.
  • the components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.
  • LAN local area network
  • WAN wide area network
  • the Internet the global information network
  • the computing system can include clients and servers.
  • a client and server are generally remote from each other and typically interact through a communication network.
  • the relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
  • modules described herein can be separated, combined or incorporated into single or combined modules.
  • the modules depicted in the figures are not intended to limit the systems described herein to the software architectures shown therein.
  • systems, architectures, devices, methods, and processes of the claimed invention encompass variations and adaptations developed using information from the embodiments described herein. Adaptation and/or modification of the systems, architectures, devices, methods, and processes described herein may be performed, as contemplated by this description.
  • Headers are provided for the convenience of the reader - the presence and/or placement of a header is not intended to limit the scope of the subject matter described herein.
  • a method of treating a subject suffering from a disease, disorder, or condition with an anti- TNF therapy comprising a step of: administering the anti-TNF therapy to subjects who have been determined not to display a gene expression response signature ; wherein the gene expression response signature has been derived by analysis of gene expression levels in biological samples from subjects who have previously received the anti-TNF therapy (“prior subjects”) and have been determined to respond or not to respond to the anti-TNF therapy; and wherein the gene expression response signature comprises an expression level of PKM and
  • the gene expression response signature comprises an expression level of one or more genes selected from The method of Embodiment 1, wherein the gene expression response signature comprises an expression level of one or more genes selected from The method of Embodiment 1, wherein the gene expression response signature comprises an expression level of one or more genes selected from:
  • the method of any one of Embodiments 1-5 wherein the anti-TNF therapy is or comprises administration of infliximab, adalimumab, etanercept, cirtolizumab pegol, golilumab, or bio similars thereof.
  • the therapy alternative to anti-TNF therapy is selected from rituximab, sarilumab, tofacitinib citrate, lefunomide, vedolizumab, tocilizumab, anakinra, and abatacept.
  • the method of Embodiments 10 or 11, wherein the step of determining comprises measuring gene expression by at least one of a microarray, RNA sequencing, real-time quantitative reverse transcription PCR (qRT-PCR), bead array, and ELISA.
  • Embodiments 1-22 wherein the disease, disorder, or condition is selected from rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, Crohn’s disease (adult or pediatric), ulcerative colitis, inflammatory bowel disease, chronic psoriasis, plaque psoriasis, hidradenitis suppurativa, asthma, uveitis, juvenile idiopathic arthritis, vitiligo, Graves’ ophthalmopathy (also known as thyroid eye disease, or Graves’ orbitopathy), and multiple sclerosis.
  • the method of Embodiment 23 wherein the disease, disorder, or condition is ulcerative colitis.
  • a kit comprising a gene expression response signature established to distinguish between responsive and non-responsive prior subjects suffering from a disease, disorder, or condition and who have received anti-TNF therapy, wherein the gene expression response signature comprises an expression level of PKM and SUMO2.
  • kits of any one of Embodiments 25-30, wherein the disease, disorder, or condition is ulcerative colitis, inflammatory bowel disease, or Crohn’s disease.
  • the kit of any one of Embodiments 25-31, wherein the disease, disorder, or condition is ulcerative colitis.
  • the kit of any one of Embodiments 25-32, wherein the anti-TNF therapy is or comprises administration of infliximab, adalimumab, etanercept, cirtolizumab pegol, golilumab, or bio similars thereof.
  • the kit of any one of Embodiments 25-33, wherein the anti-TNF therapy is or comprises administration of infliximab or adalimumab.
  • the kit of any one of Embodiments 25-36, wherein the levels of gene expression of the subject are measured by at least one of a microarray, RNA sequencing, real-time quantitative reverse transcription PCR (qRT-PCR), bead array, and ELISA.
  • a method of administering anti-TNF therapy to a subject suffering from a disease, disorder, or condition the improvement that comprises administering the anti-TNF therapy to subjects who have been determined not to display a gene expression response signature established to distinguish between responsive and non-responsive prior subjects who have received the anti-TNF therapy, wherein the gene expression response signature comprises an expression level of PKM and SUMO2.
  • a method for treating a patient suffering from a disease, disorder or condition with anti- TNF therapy comprising the steps of: determining whether the patient is a likely responder to anti-TNF therapy by: obtaining or having obtained a biological sample from the patient; and performing or having performed an assay on the biological sample to determine if the patient displays a particular gene expression response signature, wherein the gene expression response signature has been derived by analysis of gene expression levels in biological samples from subjects who have previously received the anti- TNF therapy (“prior subjects”) and have been determined to respond or not to respond to the anti-TNF therapy; and if the performing determines that the patient is a likely responder, then administering the anti-TNF therapy; and if the performing determines that the patient is a likely non-responder, then administering an alternative therapy.
  • the method of Embodiment 40 wherein the performing determines that the subject is a likely non-responder if the subject displays a gene expression response signature determined to correlate with non-responsiveness. 42. The method of Embodiment 40, wherein the performing determines that the subject is a likely non-responder if the subject does not display a gene expression response signature determined to correlate with responsiveness.
  • Embodiment 43 The method of Embodiment 40, wherein the performing determines that the subject is a likely responder if the subject displays a gene expression response signature determined to correlate with responsiveness.
  • Embodiment 44 The method of Embodiment 40, wherein the performing determines that the subject is a likely responder if the subject does not display a gene expression response signature determined to correlate with non-responsiveness.
  • a method of treating subjects suffering from an inflammatory disorder with an alternative to anti-TNF therapy comprising a step of: administering the alternative to anti-TNF therapy to subjects who have been determined to display a particular gene expression response signature, wherein the gene expression response signature has been derived by retrospective analysis of gene expression levels in biological samples from subjects who have previously received the anti-TNF therapy (“prior subjects”) and have been determined to respond or not to respond to anti-TNF therapy.
  • Example 1 Determining Responder and Non-Responder Patient Populations - Ulcerative Colitis [00175]
  • gene expression data from subjects diagnosed with ulcerative colitis (UC) who had received anti-TNF therapy was used to determine patients who are responders and non-responders to anti-TNF therapy.
  • This UC cohort (GSE12251) included 23 patients diagnosed with UC, 11 of which did not respond to anti-TNF-therapy.
  • the gene expression data for this cohort were generated using the Affymetrix platform .
  • the gene expression data was analyzed define a set of genes (response signature genes) whose expression patterns distinguish responders and non-responders. To do this, genes with significant gene expression deviations between responders and non-responders were relied on. Unlike conventional differential expression methods that look for high fold changes in gene expression between two groups, the present disclosure provides the insight that small but significant changes between two groups of patients should be included. The present disclosure thus identifies the source of a problem with conventional differential expression technologies.
  • the present disclosure provides an insight that small but significant differences impact responsiveness to therapy. Indeed, the present disclosure notes that, given that patients in these cohorts are all diagnosed with the same disease, they often may not manifest big FCs across genes. The present disclosure demonstrates that even very small but significant changes in gene expression will lead to a different treatment outcome.
  • HI human interactome
  • FIGs. 1A and IB show the subnetwork containing the genes correlated to phenotypic outcome in UC cohort as well as their interactions.
  • a significant number of genes found by gene expression analysis form the LCC of the subgraph.
  • the LCC genes (classifier genes) were then utilized to feed and train a probabilistic neural network.
  • the result of the analysis shows a near perfect classifier with an Area Under the Curve (AUC) of 0.98 and with 100% accuracy in predicting non-responders.
  • AUC Area Under the Curve
  • FIGs. 2A and 2B show the receiver operator curves (ROC) as well as negative prediction power (predicting non-responders) of the classifier.
  • the classifier is able to detect 70% of the non-responders within a cohort.
  • Table 2 represents the number and topological properties (i.e., the size of the largest component on the network and its significance) of response signature genes when mapped onto the network.
  • FIGs. 3A and 3B show the ROC and negative prediction curves associated with cross-cohort performance of the designed classifier.
  • the trained classifier shows significantly high performance in the independent cohort with AUC of 0.78.
  • NPV Negative Predictive Value
  • TNR True Negative Rate
  • the network defined by the analysis described herein provides insights into underlying biology of this response prediction.
  • the classifier genes within the response module were analyzed using GO terms to identify the most highly enriched pathways.
  • inflammatory signaling pathways including TNF signaling
  • TNF signaling were highly enriched, as were pathways linked to sumoylation, ubiquitination, proteasome function, proteolytic degradation and antigen presentation in immune cells.
  • the network approach described herein has captured protein interactions for selecting genes within the response module that clearly reflect the biology of the disease and drug response at the independent patient level and allow the accurate prediction of response to anti-TNF therapies from a baseline sample.
  • a known and significant problem with existing anti-TNF therapy approaches is that “many patients do not respond to the . . . therapy (primary non-response - PNR), or lose response during the treatment (secondary loss of response - LOR).” See, e.g., Roda et al., Clin Gastroentorl. 7:el35, Jan 2016. Specifically, reports indicate that “around 10-30% of patients do not respond to the initial treatment and 23-46% of patients lose response over time” Id. Thus, overall, the drug response rate for anti-TNF therapy (and in particular for anti-TNF therapy to treat UC patients) is below 65%, resulting in continued disease progression and escalating treatment costs for the majority of the patient population.
  • the present disclosure demonstrates that projecting baseline gene expression profiles from UC patients that are non-responders to anti-TNF therapy on the HI, reveals that such profiles cluster and form a large connected module that describes the non- responders’ disease biology.
  • a classifier developed from genes expressed in this module predicts non-response with a high level of accuracy and has been validated in a completely independent cohort (cross-cohort validation). Furthermore, this classifier meets the commercial criteria set by insurance companies and is therefore ready for clinical development and future commercialization.
  • Cohort 1 GSE14580: Twenty-four patients with active UC, refractory to corticosteroids and/or immunosuppression, underwent colonoscopy with biopsies from diseased colon within a week prior to the first intravenous infusion of 5 mg infliximab per kg body weight. Response to infliximab was defined as endoscopic and histologic healing at 4-6 weeks after first infliximab treatment using the MAYO score. Six control patients with normal colonoscopy were included. Total RNA was isolated from colonic mucosal biopsies, labelled and hybridized to Affymetrix Human Genome U133 Plus 2.0 Arrays.
  • GSE 12251 Twenty-two patients underwent colonoscopy with biopsy before infliximab treatment. Response to infliximab was defined as endoscopic and histologic healing at week 8 using the MAYO score (P2, 5, 9, 10, 14, 15, 16, 17, 24, 27, 36, and 45 as responders; P3, 12, 13, 19, 28, 29, 32, 33, 34, and 47 as non-responders).
  • Messenger RNA was isolated from pre-infliximab biopsies, labeled and hybridized to Affymetrix HGU133 Plus_2.0 Array.
  • the HI contains experimentally supported physical interactions between cellular components. These interactions were queried from several resources but only selected those that are supported by experimental validation. Most of the interactions in the HI are from unbiased high-throughput studies such as Y2H. All included data were experimentally supported interactions that have been reported in at least two publications. These interactions include, regulatory, metabolic, signaling and binary interactions. The HI contains about 17k cellular components and over 200K interactions among them. Unlike other interaction databases, no computationally inferred interaction were included, nor any interaction curated from text parsing of literature with no experimental validation.
  • each patient is given a score by the trained classifier.
  • the prediction accuracy is measured for the entire cohort as a whole and by checking whether given scores across patients well distinguish responders and non-responders.
  • Prediction performance is generally measured by the Area Under the Curve (AUC).
  • AUC Area Under the Curve
  • NPV negative predictive value
  • TNR true negative rate
  • the score cutoff that results in best group separation is set for future predictions.
  • Example 2 Determining Responder and Non-Responder Patient Populations - Rheumatoid Arthritis
  • Example 2 Analogous to Example 1, the present Example 2 describes prediction of response and/or non-response to anti-TNF therapy in patients suffering from rheumatoid arthritis (RA).
  • the presently described predictions satisfy the performance threshold identified by payers and physicians of Negative Predictive Value (NPV) of 0.9 and True Negative Rate (TNR) of 0.5.
  • NPV Negative Predictive Value
  • TNR True Negative Rate
  • gene expression data from baseline blood samples for two cohorts comprising a total of 89 RA patients were analyzed.
  • the methodology utilized in the present Example to develop a classifier i.e., a gene expression response signature
  • a classifier i.e., a gene expression response signature
  • initial genes were selected based on differential expression between responders and non-responders to anti- TNF therapy.
  • second, such genes were projected on the human interactome to determine which genes form a significant and biologically relevant cluster.
  • genes that cluster on the interactome were selected and fed into a probabilistic neural network (PNN) to develop the final classifiers.
  • PNN probabilistic neural network
  • each classifier was validated using leave-one-out validation in the training set, and validated cross-cohort in an independent cohort of patients (test set).
  • the final classifier contained 9 genes and reached an NPV of 0.91 and TNR of 0.67 in the test set.
  • the developed classifiers meet the performance thresholds set by payers and physicians; those skilled in the art will appreciate that these classifiers are useful tests that predict non-response to anti-TNFs prior to initiation of therapy and/or to assess desirability of altering administered therapy.
  • provided technology therefore permits selection of therapy (whether initial therapy or continued or altered therapy), including enabling patients to be switched onto alternative therapies faster, resulting in substantial clinical benefits to patients and savings to the healthcare system.
  • the response prediction analysis in RA utilized in the present Example was based on two individual cohorts (Tables 3 and 4). Response was measured 14-weeks after initiation of anti-TNF therapy, with response rates (Good responders; DAS28 improvement 1.2, corresponding to LDA or remission) in cohort 1 and 2 of 30% and 23%, respectively. Cohort 1 was used to train the classifier and cohort 2 was used as the independent test cohort to validate the predictive power of the classifier.
  • genes for inclusion in an RA classifier were selected via a multi-step analysis: First, genes were ranked based on their significance of correlation to patient’s response outcome (change in baseline DAS28 score at week 14) using Pearson correlation resulting in 200 top ranked genes (Feature set 1). Unlike conventional differential expression methods that look for highest fold changes in gene expression between two groups, the present Example captures small but significant changes between two groups of patients.
  • FIG. 6 illustrates a classifier development flowchart containing identifying features of the classifier (A), training and validation of a probabilistic neural network on cohort 1 using identified features (B) and validation of the trained classifier using identified feature genes expressions in an independent cohort (C). The final set of features are selected based on best performance.
  • a response classifier was trained by feeding a probabilistic neural network with Feature set 1 and 2. Training the classifier on Feature set 1 significantly predicted response using leave-one-out cross validation and reached an AUC of 0.69, an NPV of 0.9 and a TNR of 0.52 (FIG. 4A, and FIG. 4B, respectively), outperforming Feature set 2. Having a smaller number of classifier genes also opens up the opportunity to use a variety of lower cost, FDA-approved expression platforms with a broad installed base to generate the required gene expression data sets. The classifier was therefore further trained to see if performance holds up when reducing the number of genes in Feature set 1 by training on top n-ranked genes where n goes from 1 to 20.
  • FIG. 5A is an ROC curve of cross-cohort classifier test results.
  • the present Example documents effectiveness of a classifier, as described herein, that predicts non-response to anti-TNF drugs before therapy is prescribed in patients suffering from RA.
  • the present disclosure has demonstrated an AUC of 0.78, an NPV of 0.91 and a TNR of 0.67, resulting in the matrix below (Table 7). That is, the classifier identifies 67% of true non-responders with a 91% accuracy. Stratifying patients using this classifier would increase the response rate for the anti-TNF treated group by 71% from 34% to 58%. By comparison, the highest cross-cohort performance reported for classifiers developed by others had an NPV of 0.71 and a TNR of 0.71. See Toonen EJ. et al. “Validation study of existing gene expression signatures for anti-TNF treatment in patients with rheumatoid arthritis.” PLoS One. 2012;7(3):e33199. Using that classifier would significantly misclassify the genuine responders leading to a worse overall response rate than not using it at all. The presently described classifiers clearly meet the performance targets when tested in an independent cohort of patients.
  • Nanostring nCounter system uses digital barcode technology to count nucleic acid analytes for panels of up to several hundred genes on an FDA approved platform.
  • Multiplexed qRT-PCR is the gold standard for quantifying gene expression for panels of less than -20 genes and would enable the test to be offered as a distributable kit.
  • RA is a chronic, complex auto-immune diseases, where many genetic risk factors have been identified but none of them are of sufficient impact to be useful as diagnostic or prognostic markers.
  • the present disclosure provides a ranked list of candidate genes based on correlation of baseline expression level with response outcomes.
  • the rank order is derived from the significance of the correlation.
  • the present disclosure does not prioritize genes with larger fold change across the category of responders and nonresponders. It is common practice in the field to give preference to genes that show the highest fold change. This is because it is generally believed that large changes in expression levels are biologically more meaningful, and because of the technical advantage of high signal to noise ratios to compensate for high background and other sources of technical variability.
  • the present disclosure appreciates that small differences, which are ignored or overlooked in many conventional technologies, can provide important, and even critical, discriminating capability.
  • the present disclosure proposes that subtle differential perturbations may be particularly relevant and/or important in situations, like the present, where subjects suffering from the same disease, disorder, or condition are compared with one another (e.g., rather than with “control” subjects not suffering from the disease, disorder, or condition). It may be that small yet statistically significant differences in gene expression differentiate patient populations in complex diseases such as RA. This study shows that even very small but significant changes in gene expression will lead to a different treatment outcome. This method captures genes that are overlooked by conventional differential expression analysis.
  • the present disclosure utilizes the highly unbiased and independently validated map of the protein-protein interactions in cells, the human interactome.
  • mapping the prioritized genes to the interactome distinct and statistically significant clusters appear.
  • the identified clusters also provide biological insights into the biology and causal genes of anti-TNF response.
  • the genes corresponding to the top 9 genes in RA are valuable in immunological pathways and functions linked to ER stress, the protein quality control pathway, control of the cell cycle and the ubiquitin proteasome system, primarily in targeting key regulators of the cell cycle to the proteasome through ubiquitinyation.
  • the classifiers described here serve as the basis for diagnostic tests to predict anti- TNF non-response for patients with moderate to severe disease and considering initiating biologic therapy. Patients identified as non-responders will be offered alternative, approved mechanism of action therapies. These tests will provide significant improvements to current clinical practice by increasing the proportion of patients reaching treatment goals, making the treatment assignment based on scientific data and as a result decrease waste of resources and generate significant financial savings within the health care system.
  • EULAR DAS28 scoring criteria assessed 14 weeks after anti-TNF treatment.
  • EULAR response rates for female TNF naive patients are given in Table 1.
  • EULAR response characterizes patients into good responders, moderate responders and non-responders.
  • response was defined as EULAR good response, or DAS28 improvement 1.2. This corresponds to LDA or remission.
  • RNAse-free water for analysis on the Illumina Human HT-1.2v4 chip (cohort 1 samples) and 1.2pg was re-suspended in lOpl of RNAse-free water for analysis Illumina WG6v3 Bead Chip (cohort 2 samples). All samples were processed according to the manufacturer’s instructions.
  • Raw data were exported from GenomeStudio and further analyzed with the R programming language. All datasets were background corrected using the R/B ioconductor package “lumi.” Data were further transformed using variance stabilization transformational (vst) and quantile normalized.
  • Genes with expression values that are significantly correlated to clinical measures after treatment are selected as the best determinants of response.
  • Expression correlation of gene expression to response outcome is measured by Pearson correlation.
  • Genes are ranked based on the correlation value and the performance of the classifier is assessed when using top n ranked genes.
  • mapping the ranked genes on the interactome forms a significant cluster reflecting the underlying biology of response. It is observed that the ranked genes are not randomly scattered on the network. Instead, they significantly interact with each other, reflecting the existence of an underlying disease biology module that explains response.
  • the human interactome contains experimentally supported physical interactions between cellular components. These interactions are collected from several resources but only those supported by a rigorous experimental validation confirming the existence of a physical interaction between proteins are selected. Most of the interactions in the interactome are from unbiased high-throughput studies such as yeast 2-hybrid. Experimentally supported interactions that that have been reported in at least two publications are also included. These interactions include regulatory, metabolic, signaling and binary interactions.
  • the interactome contains about 17,000 cellular components and over 200,000 interactions. Unlike other interaction databases the present methods do not include any computationally inferred interactions, nor any interaction curated from text parsing of literature with no experimental validation. Therefore, the interactome used is the most complete, carefully selected and quality controlled version to date.
  • the present examples provide a network-based response module comprised of gene expression biomarkers that predict response or non-response to an anti-TNF therapy (also referred to as TNF inhibitors, or, “TNFi” or “TNFis”, including infliximab) at treatment initiation in ulcerative colitis.
  • TNF inhibitors also referred to as TNF inhibitors, or, “TNFi” or “TNFis”, including infliximab
  • Cohort A included twenty-four patients with active ulcerative colitis (UC), refractory to corticosteroids and/or immunosuppression, and underwent colonoscopy with biopsies from diseased colon within a week prior to the first intravenous infusion of 5 mg infliximab per kg body weight.
  • Response to infliximab was defined as endoscopic and histologic healing at 4-6 weeks after first infliximab treatment.
  • Eight patients were determined to be responders, sixteen were determined to be non- responsive.
  • Six control patients with normal colonoscopy were included.
  • Total RNA was isolated from colonic mucosal biopsies, labelled, and hybridized to Affymetrix Human Genome U133 Plus 2.0 Arrays.
  • Cohort B included twenty-two patients who underwent colonoscopy with biopsy before infliximab treatment. Response to infliximab was defined as endoscopic and histologic healing at week 8 (12 patients as responders and 11 patients as non-responders). Messenger RNA was isolated from pre-infliximab biopsies, labeled and hybridized to Affymetrix Human Genome U133 Plus_2.0 Array.
  • the Human Interactome previously described in Menche et al., Science, 347(6224): 1257601 (Feb. 20, 2015), contains experimentally determined physical interactions between proteins. These interactions include, regulatory, metabolic, signaling, and binary interactions. The Human Interactome amalgamates data from more than 300 thousand interactions among them.
  • Classifier Genes i.e., Genes of the Gene Expression Response Signature
  • Pearson correlation between their gene expression values and response to treatment was determined.
  • the signal-to-noise ratio of each gene correlation was calculated by randomly shuffling of the response outcome 100 times. Selected genes were then mapped onto the consolidated Human Interactome, and the largest connected component (LCC), was determined.
  • LCC largest connected component
  • the classifier provided a probability for each patient reflecting whether or not that individual responded to infliximab.
  • the log likelihood ratio of response and non-response probabilities were used to define a score for each patient and draw the receiver operating characteristic (ROC) curves by comparing the score to actual response outcomes.
  • the area under the curve (AUC) determined the performance of the classifiers.
  • the trained classifiers were blind to the outcome of the independent cohort.
  • UBC shared genes between the two top-200 gene sets was a high degree node in the Human Interactome, that could have caused a high degree of perceived connectedness between set of LCC genes from the two cohorts.
  • nodes were randomly assigned to one cohort while the shared genes were preserved between two sets during the randomization.
  • the Human Interactome network map of protein-protein interactions can serve as a blueprint to better understand the interconnectivity and underlying biology of the response prediction genes.
  • the top 200 genes from each cohort whose expression values across patients were significantly correlated to clinical outcome after infliximab treatment were selected and mapped onto the Human Interactome (FIGs. 7B-7C). Although these genes were identified from gene expression data only, the proteins encoded by these genes formed a significant cluster on the Human Interactome, with 182 and 193 proteins for Cohort A and B, respectively.
  • the LCC on the Human Interactome for each set of response prediction genes was larger than expected by chance; the cohort A LLC was 39 genes (z-score of 2.91) and the cohort B LCC was 41 genes (z-score of 2.33).
  • the LCC genes were used to train a probabilistic neural network. See Specht DF. IEEE Transactions on Electronic Computers. EC-16(3):308-19 (1967); Specht DF. IEEE Trans Neural Netw. 1(1): 111-21 (1990).
  • a probabilistic neural network is an optimum pattern classifier that minimizes the risk of incorrectly classifying an object with high efficiency.
  • Gonzalez-Camacho JM et al., BMC Genomics. 17:208 (2016).
  • the probabilistic neural networks were trained using the LCC genes and patient data to teach the predictive classifiers the appropriate patient outcome (i.e., response or inadequate response to infliximab) for each input (i.e. gene expression levels of LCC genes).
  • the UC infliximab response module is a sub-network on the Human Interactome [00239]
  • the genes were not randomly scattered on the network, but instead significantly interacted with each other (z-score of 8.34) forming a common LCC (FIG.
  • This present example describes two predictive classifiers developed using knowledge from the Human Interactome map of protein-protein interactions and a probabilistic neural network machine learning algorithm.
  • the genes predictive of response to infliximab identified from baseline colon biopsy samples from two separate patient cohorts showed limited overlap in identity but significant overlap on the Human Interactome and were predictive of response to infliximab in a cross-cohort validation.
  • the patients in these two cohorts are all diagnosed with UC, and as such, differences in the biology between these individuals may not manifest in large fold-changes in gene expression. These subtle differences in transcript levels may be overlooked in conventional differential gene expression analyses.
  • this study identified small but significant changes in gene expression that may lead to different treatment outcomes.
  • a gene array study of UC mucosal biopsies identified gene panels predictive of response to infliximab with 95% sensitivity and 85% specificity.
  • Arijs I et al. Gut. 58(12): 1612- 9 (2009).
  • Rismo R et al. Scand J Gastroenterol.
  • This network-based approach evaluates protein interactions to select genes that reflect the biology of disease at the individual patient level.
  • the cross-cohort validation of two predictive classifiers, developed using a response module found in the Human Interactome, suggests the existence of a molecular signature in baseline tissue samples that characterizes UC patients who will have an inadequate response to TNFi therapy. Further development of such a test would decrease the time to treatment response, thus allowing patients to get back to their normal, productive lives sooner while decreasing the burden on supportive family members.
  • this method of biomarker discovery and classifier development can be applied across multiple disease areas with complex phenotypes and datasets containing molecular information.
  • the platform described herein opens new, unprecedented opportunities to create new drug response modules, predict drug response in complex diseases, and achieve the goal of treating patients with the most effective treatment for their unique disease biology.

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