WO2019224385A2

WO2019224385A2 - Combined bispecific antibody and immuno-oncology therapies

Info

Publication number: WO2019224385A2
Application number: PCT/EP2019/063544
Authority: WO
Inventors: Venkateshwar REDDY
Original assignee: Glenmark Pharmaceuticals S.A.
Priority date: 2018-05-24
Filing date: 2019-05-24
Publication date: 2019-11-28
Also published as: WO2019224385A3

Abstract

The present invention relates to combinations of a T cell redirecting antibody and a second immuno-oncology or immunomodulatory agent to treat diseases, including cancer.

Description

Combined bispecific antibody and immuno-oncology therapies

TECHNICAL FIELD

The present invention relates to combinations of a T cell redirecting antibody and a second immuno- oncology or immunomodulatory or immunotherapy agent to treat diseases, including cancer.

BACKGROUND

The present invention relates to methods of treating patients in need thereof by administering a bispecific antibody(ies) and in particular T cell redirecting antibodies and immuno-oncology or immunomodulatory agents.

HER2 positive breast cancer constitutes ~20% of all breast cancers. Oncogenic HER2 is mainly overexpressed and activated in metastatic breast cancer. Targeted therapy by monoclonal antibodies against HER2, such as trastuzumab and pertuzumab, emerged as an important modality. Despite the promising outcome, one of the main hurdles with this anti HER2 therapy however is the likelihood of inherent and acquired resistance resulting in relapse and progression of the disease. Multiple intervention strategies have evolved to overcome the resistance. One of the promising approaches is the use of HER2 targeted bispecific antibody which also a T-Cell engager.

Immunotherapy/immuno-oncology has become a major focus of innovation in the development of anti-cancer therapies, as when successful patients have long-lasting anti-tumour immune responses that not only eradicate primary tumours but also metastatic lesions and can lead to the establishment of a protective anti-tumour memory immune response. Investigators have focused and had great success with therapies which offset checkpoint inhibitors, such as CTLA-4 and PD-1. These remove in vivo inhibition of anti-tumor T cell responses through antibody-mediated antagonism of these receptors. It is increasingly clear however that removing the effects of one or more checkpoint inhibitor is not sufficient to promote tumor regression in a majority of patients. Generating a robust therapeutic immune response requires not only removing inhibitory pathways but also activating stimulatory pathways or combinations with other mechanisms to achieve a robust therapeutic immune response.

Within a tumour the presence of checkpoint inhibitors, can inhibit T cell function to suppress anti tumor immune responses. Checkpoint inhibitors, such as CTLA-4 and PD-1, attenuate T cell proliferation and cytokine production. CD8 T cell responses also requires T cell receptor activation plus co-stimulation, which can be provided through ligation of tumor necrosis factor receptor family members, including 0X40 (CD134) and 4-1BB (CD137). When used as single agents, these drugs can induce potent clinical and immunologic responses in patients with metastatic disease. However, each of these agents only benefits a subset of patients, highlighting the critical need for more effective combinatorial therapeutic strategies acting via more pathways/components of the immune system and combining different mechanisms of action so as to maximise the therapeutic effect of the combined therapies.

The idea that a bispecific antibody might activate and simultaneously engage T cells to kill targeted tumor cells was conceived more than 30 years ago. Since then, two T-cell-redirecting bispecific antibodies (bsAbs) of different designs, catumaxomab and blinatumomab, have been approved by regulatory agencies, with many others at various stages of preclinical and clinical development. To date, the ongoing efforts to optimize the therapeutic potential of bispecific antibodies in general, and those intended to redirect immune cells, including T cells, NK cells, and Treg cells, in particular, have led to a plethora of functional constructs distinguishable by diverse formats and different specificities as well as a range of binding affinities and epitopes to the target antigen and effector cell antigen.

These bispecific therapies have shown efficacy in patients that do not respond to other therapies, but even in indications where efficacy has been established, various resistance mechanisms and non responding patient sub-populations have been discovered and will likely be elucidated further as more patients receive these medicines.

Therefore although immuno-oncology medicines such as an anti-CTLA-4 or PD-1 antibodiesy or bispecific antibodies such as blinatumomab have allowed the treatment of previously untreatable patients, unfortunately a large fraction of the patient population remain untreatable. This gap being due to the complexity of attempting to modulate the human immune system per se and in particular a patient's immune system which may be compromised due to the patent's general poor health and potential modulation by cancer cells or other factors associated with a disease, which allow the avoidance of detection and elimination by the patient's immune system.

Medicines and particularly combination therapies that address these problems are therefore required.

SUMMARY OF THE INVENTION

The present invention relates to combinations of a bispecific and in particular a T cell redirecting antibody and a second immuno-oncology, immunotherapy or immunomodulatory agent to treat a disease such as cancer.

The present invention also provides a method for the treatment or prophylaxis of a disease or disorder which may be ameliorated by modulating a patient's immune system e.g., autoimmune, neurodegenerative, cancer, neurological, inflammatory, hyperproliferative, and cardiovascular diseases and disorders, comprising administering to a patient in need thereof an effective amount of bispecific and in particular a T cell redirecting antibody and a second immuno-oncology agent.

Use of these new anti-human CD3 bispecific antibodies is not limited to but includes treatments of various human cancers and autoimmune and inflammatory diseases. The specific destruction of cancer cells over healthy cells and tissues represents a primary objective in oncology. Therapeutics that could safely redirect T cell killing against tumour associated cell surface antigens may offer improved clinical efficacy. Known areas of clinical unmet needs in oncology include but are not limited to breast cancer, metastatic breast cancer, ovarian cancer, pancreatic cancer, lung cancer, lymphomas and multiple myeloma.

Elimination of disease-causing T cells could be more beneficial than inhibiting T cell differentiation in treating autoimmune and inflammatory diseases such as psoriasis, multiple sclerosis and diabetes.

The CD3 protein complex comprises a number of subunits, for example, delta, epsilon and gamma. In a preferred embodiment, the epitope binding region that binds to the CD3 protein complex binds to the CD3 epsilon subunit.

An epitope binding region as described herein includes the combination of one or more heavy chain variable domains and one or more complementary light chain variable domains which together form a binding site which permits the specific binding of the hetero-dimeric immunoglobulin or fragment thereof to one or more epitopes. In an embodiment of the present invention, the epitope binding region of the first poly peptide comprises a FAB and the epitope binding region of the second polypeptide comprises a scFv. Alternatively, the epitope binding region of the first poly peptide comprises a scFv and the epitope binding region of the second polypeptide comprises a FAB.

In accordance with the present invention one or both agents my comprise a portion that binds to an antigen selected from the group comprising CD19, CD20, CD22, GPNMB, EGFR, MSLN, EGFRvll I, HER2, CEACAM5, PSMA, CEACAM6, CD33, CD123, CD79b, Tn-MUCl, NY-ESO-1, MAGE-A3, MAGE-A4, MAGE- A6, MAGE-A10, CD56, gplOO, MARTI (MLANA), PSCA, CD37, GD2, IL13R«2, GPC3, CAIX, Ll-CAM (CD171), CA125 (MUC16), CD133, FAP, FR-a, CD138, CD30, CD33, ASGR1, CD7, CD74, CD70, BCMA, TACSTD2 (TROP2), Lewis Y (LeY) blood group antigen, A33, ROR1 , WT1, CCL1 (CLEC12A), AFP, CD16a, HPV-16 E6, PRAME, LAG-3, IL2R, IL12R, CCR2, CXCR1, ROR1, TGFBR2, GCSFR, VISTA, CD27, NKG2D, TLR2, CXCR2, IFNGR1, SLAMF7, CCR1, MERTK, PTGER4, SIRPA, TGFBR1, ADORA2B, CD32, IFNAR, IGF1R, IL17R, TLR5, VEGFR2, ADORA3, c-KIT, CCR7, CDllb, CX3CR1, HLA-A2, IL2RB, PVRIG, VEGFR1, ADAM17, AMHR2, CD147, CD162, CD200, CD21, CD44, CD52, CD54, CD80, CD88, CXCR5, HER3, HVEM, IL13RA1, IL21R, KIR2DL1, KIR2DL3, MICA, NKp46, ADAM9, BAFF-R, CA9, CB2, CCR9, CD13, CD130, CD160, CD200R1, CD29, CD4, CD51, CD8, Claudin 6, CLEC2D, CSPG4, CXCR3, DR3, DR5, EPHA3, FAIM-3, FasR, F0LR1, FSHR, GNRHR, ICAM, IFNGR2, IGF2R, IL10RA, IL12RB1, IL15RA, IL1R1, IL1R3, IL27R, IL9R, Integrin beta-7, ITGB5, KIR2DL2, L1CAM, MRP-3, MRP1, NGcGM3, PCDH18, PTGER2, RNF43, ROR2, SLAMF1, TIE2, TM4SF5, TRBC1, TRBC2, TSHR, GPNMB, CD79b, CD137, CD134 , CDIL13Ra2, ASGR1, A33, CD22, DLL3, PSCA, CA125 (MUC16), CD133, CD138, TACSTD2 (TROP2), CCL1 (CLEC12A), EPCAM, TPBG, FLT-3, CD276 (B7-H3), gpA33, CDH3, MOSPD2, SSTR2, TAG72.

In accordance with the present invention one or both agents my comprise a portion that binds to or otherwise modulates a Co-stimulatory Immune Checkpoint Targets selected from the group comprising CD155 / PVR, CD226 / DNAM-1, CD137 / 4-1BB, CD40 / TNFRSF5, CD40L / CD154 / TNFSF5, 4-1BBL / CD137L, 0X40 / CD134, OX-40L / TNFSF4 / CD252, CD27, HVEM / TNFRSF14, TNFSF14 / LIGHT / CD258 CD70 / CD27L / TNFSF7, CD28 / TP44, CD80 / B7-1, CD86 / B7-2, GITR / TNFRSF18, GITR Ligand/TNFSF18, ICOS / AILIM / CD278, ICOS Ligand / B7-H2.

In accordance with the present invention one or both agents my comprise a portion that binds to or otherwise modulates a Co-inhibitory Immune Checkpoint Targets selected from the group comprising PD1 / PDCD1 / CD279 PD-L1 / B7-H1 / CD274, PD-L2 / B7-DC / CD273, CTLA-4 / CD152, CD80 / B7-1, CD86 / B7-2, B7-H3 / CD276 B7-H4 / B7S1 / B7x, VISTA / B7-H5 / GI24, HVEM / TNFRSF14, BTLA,

CD160, LAG3 / CD223 / Lymphocyte activation gene 3, CEACAM1 / CD66a, Indoleamine 2,3- dioxygenase/IDO Galectin-9 / LGALS9, TIM-3 / HAVCR2 2B4 / CD244, SIRP alpha / CD172a CD47, CD48 / SLAMF2, TIGIT / VSTM3, CD155 / PVR.

In accordance with the present invention, an immuno-oncology agent means a substance or composition which when administered to a patient leads to an increased chance of the patient's immune system eliciting a response against a cancer cell population.

In accordance with the present invention, an immunomodulatory agent is a substance or composition able to up or down regulate a component or components of a patient's immune system.

In accordance with the present invention, an immunotherapy is a substance or composition when administered to a patient which leads to a therapeutic effect.

In accordance with the present invention the second immuno-oncology agent is a PD1 antagonist antibody such as Pembrolizumab or Nivolumab and in particular Pembrolizumab.

In accordance with another aspect of the present invention the T cell redirecting antibody is selected from the group comprising GBR 1302 (SEQ ID NOs: 1-6), GBR 1342 (SEQ ID NOs: 7-9), GBR 1372 (SEQ ID NOs: 10-12). In accordance with the present invention the combination of a T cell redirecting antibody and a second immuno-oncology agent is suitable for treating a cancer characterised by the overexpression of HER2 and in particular selected from the group breast, ovarian, bladder, salivary gland, endometrial, pancreatic and non-small-cell lung cancer (NSCLC).

According to another aspect of the present invention, there is provided a method of treating a patient in need thereof using combinations of a bispecific antibody and in particular a T cell redirecting antibody and a second immuno-oncology agent, comprising administering the bispecific antibody and second immuno-oncology agent to the patient either sequentially or simultaneously.

For the purposes of interpreting this specification, the following definitions will apply and whenever appropriate, terms used in the singular will also include the plural and vice versa. It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

The terms "polypeptide" and "protein" refer to a polymer of amino acid residues wherein amino acids are combined via peptide bonds to form a chain of amino acids that have been linked together via dehydration synthesis. Polypeptides and proteins can be synthesized through chemical synthesis or recombinant expression and are not limited to a minimum amino acid length.

In accordance with the invention, the group of polypeptides comprises "proteins" as long as the proteins consist of a single polypeptide chain. Polypeptides may further form multimers such as dimers, trimers and higher oligomers, i.e. consisting of more than one polypeptide molecule. Polypeptide molecules forming such dimers, trimers etc. may be identical or non-identical. The corresponding higher order structures of such multimers are, consequently, termed homo- or hetero dimers, homo- or hetero-trimers etc. An example for a hetero-multimer is an antibody molecule, which, in its naturally occurring form, consists of two identical light polypeptide chains and two identical heavy polypeptide chains. The terms "polypeptide" and "protein" also refer to naturally modified polypeptides/proteins wherein the modification is effected e.g. by post-translational modifications like glycosylation, acetylation, phosphorylation and the like. Such modifications are well known in the art. Furthermore, for purposes of the present invention, a "polypeptide" refers to a protein which includes modifications, such as deletions, additions and substitutions (which can be conservative in nature) to the native sequence. These modifications may be deliberate, as through site-directed mutagenesis, or may be accidental, such as through mutations of hosts which produce the proteins or errors due to PCR amplification. The term "CD3 complex" as used herein refers to the protein complex known as the CD3 (cluster of differentiation 3) T-cell co-receptor (Wucherpfennig KW et al., (2010) Cold Spring Harb Perspect Biol, 2(4): a005140). The CD3 protein complex is composed of four distinct chains. In mammals, the complex contains a CD3y chain, a CD36 chain, and two CD3e chains. These chains associate with a molecule known as the T-cell receptor (TCR) and the z-chain to generate an activation signal in T lymphocytes (van der Merwe PA & Dushek O (2011) Nat Rev Immunol, 11(1): 47-55). The TCR, z-chain, and CD3 molecules together comprise the TCR complex. The CD3y, CD36, and CD3e chains are highly related cell-surface proteins of the immunoglobulin superfamily containing a single extracellular immunoglobulin domain. The intracellular tails of the CD3 molecules contain a single conserved motif known as an immunoreceptor tyrosine-based activation motif or ITAM for short, which is essential for the signalling capacity of the TCR. Since CD3 is required for T-cell activation, drugs (often monoclonal antibodies) that target CD3 have and are being investigated as immunosuppressant therapies.

The term "disease associated antigen" as used herein refers to molecules that are involved in a disease process. Examples of disease associated antigens are found in a broad range of therapeutic areas such as inflammation, cancer and autoimmune diseases. In oncology, disease associated antigens are molecules that can broadly be used for the screening and/or monitoring and/or therapeutic targeting of cancers within a patient population, for example EpCAM antigen in prostate cancer. Tumour antigens can be produced directly by the tumour or by non-tumour cells as a response to the presence of a tumour and preferred tumour antigens are cell-surface molecules. Inflammatory disease associated antigens are known, which include but are not limited to, pro-inflammatory cytokines such as TNF-a and IL-1. Autoimmune disease associated antigens are also known; examples of these include but are not limited to antibodies against double-stranded DNA in systemic lupus erythematosus and amyloid beta peptide in Alzheimers disease.

The term "immunoglobulin" as referred to herein can be used interchangeably with the term "antibody". Immunoglobulin includes full-length antibodies and any antigen binding fragment or single chains thereof. Immunoglobulins can be homo-dimeric or hetero-dimeric. Immunoglobulins and specifically naturally occurring antibodies are glycoproteins which exist as one or more copies of a Y- shaped unit, composed of four polypeptide chains. Each "Y" shape contains two identical copies of a heavy (H) chain and two identical copies of a light (L) chain, named as such by their relative molecular weights. Each light chain pairs with a heavy chain and each heavy chain pairs with another heavy chain. Covalent interchain disulfide bonds and non-covalent interactions link the chains together. Immunoglobulins and specifically naturally occurring antibodies contain variable regions, which are the two copies of the antigen binding site. Papain, a proteolytic enzyme splits the "Y" shape into three separate molecules, two so called "Fab" or "FAB" fragments (Fab = fragment antigen binding) and one so called "Fc" fragment or "Fc region" (Fc = fragment crystallizable). A Fab fragment consists of the entire light chain and part of the heavy chain. The heavy chain contains one variable region (VH) and either three or four constant regions (CHI, CH2, CH3 and CFI4, depending on the antibody class or isotype). The region between the CH 1 and CH2 regions is called the hinge region and permits flexibility between the two Fab arms of the Y-shaped antibody molecule, allowing them to open and close to accommodate binding to two antigenic determinants separated by a fixed distance. The "hinge region" as referred to herein is a sequence region of 6-62 amino acids in length, only present in IgA, IgD and IgG, which encompasses the cysteine residues that bridge the two heavy chains. The heavy chains of IgA, IgD and IgG each have four regions, i.e. one variable region (VH) and three constant regions (CHI-3). IgE and IgM have one variable and four constant regions (CFI1-4) on the heavy chain. The constant regions of the immunoglobulins may mediate the binding to host tissues or factors, including various cells of the immune system (e.g., effector cells) and the first component (Clq) of the complement system classical pathway. Each light chain is usually linked to a heavy chain by one covalent disulfide bond. Each light chain contains one variable region (VL) and one light chain constant region. The light chain constant region is a kappa light chain constant region designated herein as IGKC or is a lambda light chain constant region designated herein as IGLC. IGKC is used herein equivalently to CK or CK and has the same meaning. IGLC is used herein equivalently to CX or CL and has the same meaning. The term "an IGLC region" as used herein refer to all lambda light chain constant regions e.g. to all lambda light chain constant regions selected from the group consisting of IGLC1, IGLC2, IGLC3, IGLC6 and IGLC7. The VH and VL regions can be further subdivided into regions of hypervariability, termed complementarity determining regions (CDR), interspersed with regions that are more conserved, termed framework regions (FR or FW). Each VH and VL is composed of three CDRs and four FRs, arranged from amino- terminus to carboxy-terminus in the following order: FR1, CDR1, FR2, CDR2, FR3, CDR3, FR4. The variable regions of the heavy and light chains contain an epitope- binding region that interacts with an antigen.

Engineered immunoglobulins can encompass different epitope binding region formats such as scFv, FAB or dAb fragments. These fragments are usually assembled in an antibody-like structure by genetic fusion to a IgG Fc region. Engineered immunoglobulins can be constructed as homo or hetero-dimers with or without the use of hetero-dimerization enhancing techniques, and can have mono- or bispecific binding properties.

The term "full length antibody" as used herein includes the structure that constitutes the natural biological form of an antibody, including variable and constant regions. For example, in most mammals, including humans and mice, the full length antibody of the IgG class is a tetramer and consists of two identical pairs of two immunoglobulin chains, each pair having one light and one heavy chain, each light chain comprising immunoglobulin regions VL and a light chain constant region, and each heavy chain comprising immunoglobulin regions VH, CHI (Cyl), CH2 (Cy2), CH3 (Cy3) and CH4 (Cy4), depending on the antibody class or isotype). In some mammals, for example in camels and llamas, IgG antibodies may consist of only two heavy chains, each heavy chain comprising a variable region attached to the Fc region.

Antibodies are grouped into classes, also referred to as isotypes, as determined genetically by the constant region. Human constant light chains are classified as kappa (CK) and lambda (CX) light chains. Heavy chains are classified as mu (m), delta (m), gamma (y), alpha (a), or epsilon (e) and define the antibody's isotype as IgM, IgD, IgG, IgA and IgE, respectively. Thus, "isotype" as used herein is meant any of the classes and/or subclasses of immunoglobulins defined by the chemical and antigenic characteristics of their constant regions. The known human immunoglobulin isotypes are IGHG1 (IgGl), IGHG2 (lgG2), IGHG3 (lgG3), IGHG4 (lgG4), IGHA1 (IgAl), IGHA2 (lgA2), IGHM (IgM), IGHD (IgD) and IGHE (IgE). The so-called human immunoglobulin pseudo-gamma IGHGP gene represents an additional human immunoglobulin heavy constant region gene which has been sequenced but does not encode a protein due to an altered switch region (Bensmana M et al., (1988) Nucleic Acids Res, 16(7): 3108). In spite of having an altered switch region, the human immunoglobulin pseudo-gamma IGHGP gene has open reading frames for all heavy constant regions (CH1-CH3) and hinge. All open reading frames for its heavy constant regions encode protein regions which align well with all human immunoglobulin constant regions with the predicted structural features. This additional pseudo gamma isotype is referred herein as IgGP or IGHGP. Other pseudo immunoglobulin genes have been reported such as the human immunoglobulin heavy constant region epsilon PI and P2 pseudo-genes (IGHEP1 and IGHEP2). The IgG class is the most commonly used for therapeutic purposes. In humans this class comprises subclasses IgGl, lgG2, lgG3 and lgG4. In mice this class comprises subclasses IgGl, lgG2a, lgG2b, lgG2c and lgG3.

The term "Immunoglobulin fragments" as used herein include, but is not limited to, (i) a region including for example a CHI, a CH2 or a CH3 region, (ii) the Fab fragment consisting of VL, VH, CL or CK and CHI regions, including Fab' and Fab'-SH, (ii) the Fd fragment consisting of the VH and CHI regions, (iii) the dAb fragment (Ward ES et al., (1989) Nature, 341(6242): 544-6) which consists of a single variable region (iv) F(ab’)2 fragments, a bivalent fragment comprising two linked Fab fragments (v) single chain Fv fragments (scFv), wherein a VH region and a VL region are linked by a peptide linker which allows the two regions to associate to form an antigen binding site (Bird RE et al., (1988) Science, 242(4877): 423-6; Huston JS et al., (1988) Proc Natl Acad Sci U S A, 85(16): 5879-83), (vi) "diabodies" or "triabodies", multivalent or multispecific fragments constructed by gene fusion (Holliger P et al., (1993) Proc Natl Acad Sci U S A, 90(14): 6444-8; Tomlinson I & Holliger P, (2000) Methods Enzymol, 326:461-79), (vii) scFv, diabody or region antibody fused to an Fc region and (viii) scFv fused to the same or a different antibody.

The term "variable region" refers to the regions or domains that mediates antigen-binding and defines specificity of a particular antibody for a particular antigen. In naturally occurring antibodies, the antigen-binding site consists of two variable regions that define specificity: one located in the heavy chain, referred herein as heavy chain variable region (VH) and the other located in the light chain, referred herein as light chain variable region (VL). In humans, the heavy chain variable region (VH) can be divided into seven subgroups or subclasses: VH1, VH2, VH3, VH4, VH5, VH6 and VH7. In some cases, specificity may exclusively reside in only one of the two regions as in single-domain antibodies from heavy-chain antibodies found in camelids. The V regions are usually about 110 amino acids long and consist of relatively invariant stretches of amino acid sequence called framework regions (FRs or "non-CDR regions") of 15-30 amino acids separated by shorter regions of extreme variability called "hypervariable regions" that are 7-17 amino acids long. The variable domains of native heavy and light chains comprise four FRs, largely adopting a beta-sheet configuration, connected by three hypervariable regions, which form loops. The hypervariable regions in each chain are held together in close proximity by FRs and, with the hypervariable regions from the other chain, contribute to the formation of the antigen binding site of antibodies (see Kabat EA et al., supra.). The term "hypervariable region" as used herein refers to the amino acid residues of an antibody which are responsible for antigen binding. The hypervariable region generally comprises amino acid residues from a "complementary determining region" or "CDR", the latter being of highest sequence variability and/or involved in antigen recognition. For all variable regions numbering is according to Kabat (Kabat EA et al., supra.).

A number of CDR definitions are in use and are encompassed herein. The Kabat definition is based on sequence variability and is the most commonly used (Kabat EA et al., supra.). Chothia refers instead to the location of the structural loops (Chothia & Lesk J. (1987) Mol Biol, 196: 901-917). The AbM definition is a compromise between the Kabat and the Chothia definitions and is used by Oxford Molecular's AbM antibody modelling software (Martin ACR et al., (1989) Proc Natl Acad Sci USA 86:9268-9272; Martin ACR et al., (1991) Methods Enzymol, 203: 121-153; Pedersen JT et al., (1992) Immunomethods, 1: 126-136; Rees AR et al., (1996) In Sternberg M.J.E. (ed.), Protein Structure Prediction. Oxford University Press, Oxford, 141-172). The contact definition has been recently introduced (MacCallum RM et al., (1996) J Mol Biol, 262: 732-745) and is based on an analysis of the available complex structures available in the Protein Databank. The definition of the CDR by IMGT^®, the international ImMunoGeneTics information system^® (http://www.imgt.org) is based on the IMGT numbering for all immunoglobulin and T cell receptor V-REGIONs of all species (IMGT^®, the international ImMunoGeneTics information system^®; Lefranc MP et al., (1999) Nucleic Acids Res, 27(1): 209-12; Ruiz M et al., (2000) Nucleic Acids Res, 28(1): 219-21; Lefranc MP (2001) Nucleic Acids Res, 29(1): 207-9; Lefranc MP (2003) Nucleic Acids Res, 31(1): 307-10; Lefranc MP et al., (2005) Dev Comp Immunol, 29(3): 185-203; Kaas Q et al., (2007) Briefings in Functional Genomics & Proteomics, 6(4): 253-64). All Complementarity Determining Regions (CDRs) as referred to in the present invention, are defined preferably as follows (numbering according to Kabat EA et al., supra): LCDR1: 24-34, LCDR2: 50-56, LCDR3: 89-98, HCDR1: 26-35, HCDR2: 50-65, HCDR3: 95-102.

The "non-CDR regions" of the variable domain are known as framework regions (FR). The "non-CDR regions" of the VL region as used herein comprise the amino acid sequences: 1-23 (FRI), 35-49 (FR2), 57-88 (FR3) and 99-107 (FR4). The "non-CDR regions" of the VH region as used herein comprise the amino acid sequences: 1-25 (FRI), 36-49 (FR2), 66-94 (FR3) and 103-113 (FR4).

The CDRs of the present invention may comprise "extended CDRs" which are based on the aforementioned definitions and have variable domain residues as follows: LCDR1: 24-36, LCDR2: 46- 56, LCDR3:89-97, HCDR1: 26-35, HCDR2:47-65, HCDR3: 93-102. These extended CDRs are numbered as well according to Kabat et al., supra. The "non-extended CDR region" of the VL region as used herein comprise the amino acid sequences: 1-23 (FRI), 37-45 (FR2), 57-88 (FR3) and 98- approximately 107 (FR4). The "non-extended CDR region" of the VH region as used herein comprise the amino acid sequences: 1-25 (FRI), 37-46 (FR2), 66-92 (FR3) and 103- approximately 113 (FR4).

The term "Fab" or "FAB" or "Fab region" or "FAB region" as used herein includes the polypeptides that comprise the VH, CHI, VL and light chain constant immunoglobulin regions. Fab may refer to this region in isolation, or this region in the context of a full length antibody or antibody fragment.

The term "Fc" or "Fc region", as used herein includes the polypeptide comprising the constant region of an antibody heavy chain excluding the first constant region immunoglobulin region. Thus Fc refers to the last two constant region immunoglobulin regions of IgA, IgD and IgG or the last three constant region immunoglobulin regions of IgE and IgM, and the flexible hinge N-terminal to these regions. For IgA and IgM, Fc may include the J chain. For IgG, Fc comprises immunoglobulin regions Cgamma2 and Cgamma3 (Cy2 and Cy3) and the hinge between Cgammal (Cyl) and Cgamma2 (Cy2). Although the boundaries of the Fc region may vary, the human IgG heavy chain Fc region is usually defined to comprise residues C226 or P230 to its carboxyl-terminus, wherein the numbering is according to the EU index. Fc may refer to this region in isolation or this region in the context of an Fc polypeptide, for example an antibody.

The term "immunoglobulin constant region" as used herein refers to immunoglobulin or antibody heavy chain constant regions from human or animal species and encompasses all isotypes. Preferably, immunoglobulin constant regions are of human origin and are selected from the group consisting of, but not limited to: IGHG1 CHI, IGHG2 CHI, IGHG3 CHI, IGHG4 CHI, IGHA1 CHI, IGHA2 CHI, IGHE CHI, IGHEP1 CHI, IGHM CHI, IGHD CHI, IGHGP CHI, IGHG1 CH2, IGHG2 CH2, IGHG3 CH2, IGHG4 CH2, IGHA1 CH2, IGHA2 CH2, IGHE CH2, IGHEP1 CH2, IGHM CH2, IGHD CH2, IGHGP CH2, IGHG1 CH3, IGHG2 CH3, IGHG3 CH3, IGHG4 CH3, IGHA1 CH3, IGHA2 CH3, IGHE CH3, IGHEP1 CH3, IGHM CH3, IGHD CH3, IGHGP CH3, IGHE CH4 and IGHM CH4. Prefered "immunoglobulin constant regions" are selected from the group consisting of human IGHE CH2, IGHM CH2, IGHG1 CH3, IGHG2 CH3, IGHG3 CH3, IGHG4 CH3, IGHA1 CH3, IGHA2 CH3, IGHE CH3, IGHM CH3, IGHD CH3 and IGHGP CH3. More prefered "immunoglobulin constant regions" are selected from the group consisting of human IGHG1 CH3, IGHG2 CH3, IGHG3 CH3, IGHG4 CH3, IGHA1 CH3, IGHA2 CH3, IGHE CH3, IGHM CH3, IGHD CH3 and IGHGP CH3.

The term "epitope binding region" includes a polypeptide or a fragment thereof having minimal amino acid sequence to permit the specific binding of the immunoglobulin molecule to one or more epitopes. Naturally occurring antibodies have two epitope binding regions which are also known as antigen binding or combining sites or paratopes. Epitope binding regions in naturally occurring antibodies are confined within the CDR regions of the VH and/or VL domains wherein the amino acid mediating epitope binding are found. In addition to naturally occurring antibodies, artificial VH domains or VL domains or fragments thereof and combinations thereof can be engineered to provide epitope binding regions (Holt LJ et al., (2003) Trends Biotechnol, 21(11): 484-490; Polonelli L et al., (2008) PLoS ONE, 3(6): e2371). Examples of non-immunoglobulin based epitope binding regions can be found in artificial protein domains used as "scaffold" for engineering epitope binding regions (Binz HK et al., (2005) Nat Biotechnol, 23(10): 1257-1268) or peptide mimetics (Murali R & Greene Ml (2012) Pharmaceuticals, 5(2): 209-235). Preferably the term 'epitope binding region' includes the combination of one or more heavy chain variable domains and one or more complementary light chain variable domains which together forms a binding site which permits the specific binding of the immunoglobulin molecule to one or more epitopes. Examples of an epitope binding region as exemplified in the present invention include scFv and FAB. As used herein, the term "epitope" includes a fragment of a polypeptide or protein or a non-protein molecule having antigenic or immunogenic activity in an animal, preferably in a mammal and most preferably in a human. An epitope having immunogenic activity is a fragment of a polypeptide or protein that elicits an antibody response in an animal. An epitope having antigenic activity is a fragment of a polypeptide or protein to which an antibody or polypeptide specifically binds as determined by any method well-known to one of skill in the art, for example by immunoassays. Antigenic epitopes need not necessarily be immunogenic. Preferably, the term "epitope" as used herein refers to a polypeptide sequence of at least about 3 to 5, preferably about 5 to 10 or 15 and not more than about 1,000 amino acids (or any integer there between), which define a sequence that by itself or as part of a larger sequence, binds to an antibody generated in response to such sequence. There is no critical upper limit to the length of the fragment, which may comprise nearly the full-length of the protein sequence, or even a fusion protein comprising one or more epitopes. An epitope for use in the subject invention is not limited to a polypeptide having the exact sequence of the portion of the parent protein from which it is derived. Thus the term "epitope" encompasses sequences identical to the native sequence, as well as modifications to the native sequence, such as deletions, additions and substitutions (generally conservative in nature).The epitopes of protein antigens are divided into two categories, conformational epitopes and linear epitopes, based on their structure and interaction with the epitope binding site (Goldsby R et al., (2003) "Antigens (Chapter 3)" Immunology (Fifth edition ed.), New York: W. H. Freeman and Company pp. 57-75, ISBN 0-7167-4947-5). A conformational epitope is composed of discontinuous sections of the antigen's amino acid sequence. These epitopes interact with the paratope based on the 3-D surface features and shape or tertiary structure of the antigen. Most epitopes are conformational. By contrast, linear epitopes interact with the paratope based on their primary structure. A linear epitope is formed by a continuous sequence of amino acids from the antigen.

The term "hetero-dimeric immunoglobulin" or "hetero-dimeric fragment" or "hetero-dimer" or "hetero-dimer of heavy chains" as used herein includes an immunoglobulin molecule or part of comprising at least a first and a second polypeptide, like a first and a second region, wherein the second polypeptide differs in amino acid sequence from the first polypeptide. Preferably, a hetero- dimeric immunoglobulin comprises two polypeptide chains, wherein the first chain has at least one non-identical region to the second chain, and wherein both chains assemble, i.e. interact through their non-identical regions. More preferably the hetero-dimeric immunoglobulin, has binding specificity for at least two different ligands, antigens or binding sites, i.e. is bispecific. Fletero-dimeric immunoglobulin as used herein includes but is not limited to full length bispecific antibodies, bispecifc Fab, bispecifc F(ab')2, bispecific scFv fused to an Fc region, diabody fused to an Fc region and domain antibody fused to an Fc region.

The term "homo-dimeric immunoglobulin" or "homo-dimeric fragment" or "homo-dimer" or "homo dimer of heavy chains" as used herein includes an immunoglobulin molecule or part of comprising at least a first and a second polypeptide, like a first and a second region, wherein the second polypeptide is identical in amino acid sequence to the first polypeptide. Preferably, a homo-dimeric immunoglobulin comprises two polypeptide chains, wherein the first chain has at least one identical region to the second chain, and wherein both chains assemble, i.e. interact through their identical regions. Preferably, a homo-dimeric immunoglobulin fragment comprises at least two regions, wherein the first region is identical to the second region, and wherein both regions assemble, i.e. interact through their protein-protein interfaces.

For all immunoglobulin constant regions included in the present invention, numbering can be according to the IMGT^® (IMGT^®; supra).

For all human CHI, CH2, CH3 immunoglobulin heavy chain constant regions selected from the group consisting of IGHG1, IGHG2, IGHG3 and IGHG4, numbering can be according to the "EU numbering system" (Edelman GM et al., (1969) Proc Natl Acad Sci USA, 63(1): 78-85). A complete correspondence for the human CHI, hinge, CH2 and CH3 constant regions of IGHG1 can be found at the IMGT database (IMGT^®; supra).

For the human kappa immunoglobulin light chain constant region (IGKC), numbering can be according to the "EU numbering system" (Edelman GM et al., supra). A complete correspondence for the human CK region can be found at IMGT database (IMGT^®; supra).

For the human lambda immunoglobulin light chain constant regions (IGLC1, IGLC2, IGLC3, IGLC6 and IGLC7), numbering can be according to the "Kabat numbering system" (Kabat EA et al., supra). A complete correspondence for human IGLC regions can be found at the IMGT database (IMGT^®; supra).

The human IGHG1 immunoglobulin heavy chain constant regions as referred to herein have the following region boundaries: CH 1 region (EU numbering: 118-215), Flinge 01 region (EU numbering: 216-230), CH2 region (EU numbering: 231-340) and CH3 region (EU numbering: 341-447). The human CK region referred herein spans residues 108 to 214 (EU numbering). The human IGLC1, IGLC2, IGLC3, IGLC6 and IGLC7 regions referred herein span residues 108-215 (Kabat numbering).

The terms "amino acid" or "amino acid residue" as used herein includes natural amino acids as well as non-natural amino acids. Preferably natural amino acids are included. The term "modification" or "amino acid modification" herein includes an amino acid substitution, insertion and/or deletion in a polypeptide sequence. The terms "substitution" or "amino acid substitution" or "amino acid residue substitution" as used herein refers to a substitution of a first amino acid residue in an amino acid sequence with a second amino acid residue, whereas the first amino acid residue is different from the second amino acid residue i.e. the substituted amino acid residue is different from the amino acid which has been substituted. For example, the substitution R94K refers to a variant polypeptide, in which the arginine at position 94 is replaced with a lysine. For example 94K indicates the substitution of position 94 with a lysine. For the purposes herein, multiple substitutions are typically separated by a slash or a comma. For example, "R94K/L78V" or "R94K, L78V" refers to a double variant comprising the substitutions R94K and L78V. By "amino acid insertion" or "insertion" as used herein is meant the addition of an amino acid at a particular position in a parent polypeptide sequence. For example, insert -94 designates an insertion at position 94. By "amino acid deletion" or "deletion" as used herein is meant the removal of an amino acid at a particular position in a parent polypeptide sequence. For example, R94- designates the deletion of arginine at position 94.

In certain embodiments, the terms "decrease", "reduce", or "reduction" in binding to Protein A refers to an overall decrease of at least 25%, 30%, 40%, 50%, 60%, 70%, 80%, 85%, 90%, 95%, 97%, or 99% up to 100% (elimination) in the binding of a modified immunoglobulin or fragment thereof to Protein A detected by standard art known methods such as those described herein, as compared to a parental i.e. unmodified immunoglobulin or wild-type IgG or an IgG having the wild-type human IgG Fc region. In certain embodiments these terms alternatively may refer to an overall decrease of 10-fold (i.e. 1 log), 100-fold (2 logs), 1,000-fold (or 3 logs), 10,000-fold (or 4 logs), or 100,000-fold (or 5 logs).

The terms "eliminate", "abrogate", "elimination" or "abrogation" of binding to Protein A refers to an overall decrease of 100% in the binding of a modified immunoglobulin or fragment thereof to Protein A i.e. a complete loss of the binding of a modified immunoglobulin or fragment thereof to Protein A, detected by standard art known methods such as those described herein, as compared to a parental i.e. unmodified immunoglobulin or wild-type IgG or an IgG having the wild-type human IgG Fc region.

Similarly, the terms "decrease", "reduce", or "reduction" in binding to an affinity reagent refers to an overall decrease of at least 25%, 30%, 40%, 50%, 60%, 70%, 80%, 85%, 90%, 95%, 97%, or 99% up to 100% (elimination) in the binding of a modified immunoglobulin or fragment thereof to the affinity reagent detected by standard art known methods such as those described herein, as compared to a parental, i.e. unmodified immunoglobulin or wild-type IgG or an IgG having the wild-type human IgG Fc region. In certain embodiments these terms alternatively may refer to an overall decrease of 10- fold (i.e. 1 log), 100-fold (2 logs), 1,000-fold (or 3 logs), 10,000-fold (or 4 logs), or 100,000-fold (or 5 logs).

The terms "eliminate" , "abrogate", "elimination" or "abrogation" of binding to an affinity reagent refers to an overall decrease of 100% in the binding of a modified immunoglobulin or fragment thereof to the affinity reagent i.e. a complete loss of the binding of a modified immunoglobulin or fragment thereof to the affinity reagent detected by standard art known methods such as those described herein, as compared to a parental, i.e. unmodified immunoglobulin or wild-type IgG or an IgG having the wild-type human IgG Fc region.

"Bispecific antibodies" are monoclonal antibodies that have binding specificities for at least two different antigens. In certain embodiments, the bispecific antibodies are bispecific antibodies with one or more amino acid modifications in the VH region relative to the parental antibody. In certain embodiments, bispecific antibodies may be human or humanized antibodies. Bispecific antibodies may also be used to localize cytotoxic agents to cells which express a target antigen. These antibodies possess a target-antigen-binding arm and an arm which binds a cytotoxic agent, such as, e.g., saporin, anti-interferon-a, vinca alkaloid, ricin A chain, methotrexate or radioactive isotope hapten. Bispecific antibodies can be prepared as full length antibodies or antibody fragments. Methods for making bispecific antibodies are known in the art. Traditionally, the recombinant production of bispecific antibodies is based on the co-expression of two immunoglobulin heavy chain-light chain pairs, where the two heavy chains have different specificities (Milstein and Cuello, (1983) Nature, 305: 537-40). Because of the random assortment of immunoglobulin heavy and light chains, these hybridomas (quadromas) produce a potential mixture of different antibody molecules, of which only one has the correct bispecific structure. The purification of the correct molecule, which is usually done by affinity chromatography steps, is rather cumbersome and the product yields are low. Similar procedures are disclosed in WO1993/08829 and in Traunecker et al., (1991) EMBO J, 10: 3655-9. According to a different approach, antibody variable regions with the desired binding specificities (antibody-antigen combining sites) are fused to immunoglobulin constant region sequences. The fusion, for example, is with an immunoglobulin heavy chain constant region, comprising at least part of the hinge, CH2 and CH3 regions. In certain embodiments, the first heavy-chain constant region (CHI), containing the site necessary for light chain binding, is present in at least one of the fusions. DNAs encoding the immunoglobulin heavy chain fusions and, if desired, the immunoglobulin light chain, are inserted into separate expression vectors and are co-transfected into a suitable host organism. This provides for flexibility in adjusting the mutual proportions of the three polypeptide fragments in embodiments when unequal ratios of the three polypeptide chains used in the construction provide the optimum yields. It is, however, possible to insert the coding sequences for two or all three polypeptide chains in one expression vector when the expression of at least two polypeptide chains in equal ratios results in high yields or when the ratios are of no particular significance.

Bispecific antibodies include cross-linked or "heteroconjugate" antibodies. For example, one of the antibodies in the heteroconjugate can be coupled to avidin, the other to biotin. Such antibodies have, for example, been proposed to target immune system cells to unwanted cells (US4,676,980) and for treatment of HIV infection (W01991/00360, WO1992/00373 and EP03089). Heteroconjugate antibodies may be made using any convenient cross-linking method. Suitable cross-linking agents are well known in the art (see US4,676,980), along with a number of cross-linking techniques. Antibodies with more than two valencies are also contemplated. For example, trispecific antibodies can be prepared (see Tutt A et al. (1991) J. Immunol. 147: 60-9).

In some embodiments the present disclosure provides a bispecific hetero-dimeric immunoglobulin or fragment thereof or a bispecific full-length antibody which binds to CD3 and a disease associated antigens selected from within the groups of: tumor antigens, cytokines, vascular growth factors and lympho-angiogenic growth factors. Preferably the bispecific hetero-dimeric immunoglobulin or fragment thereof or the bispecific antibody binds to CD3 and a disease associated antigen selected from the group consisting of: HER2, CD38, 0X40, HER3, EpCAM, CD19, CD20, EGFR, IgE and PSMA. Preferably the bispecific hetero-dimeric immunoglobulin or fragment thereof or the bispecific antibody binds to CD3 and HER2 or CD3 and CD38 or CD3 and 0X40.

Protein A: Protein A is a cell wall component produced by several strains of Staphylococcus aureus which consists of a single polypeptide chain. The Protein A gene product consists of five homologous repeats attached in a tandem fashion to the pathogen's cell wall. The five domains are approximately 58 amino acids in length and denoted EDABC, each exhibiting immunoglobulin binding activity (Tashiro M & Montelione GT (1995) Curr. Opin. Struct. Biol., 5(4): 471-481). The five homologous immunoglobulin binding domains fold into a three-helix bundle. Each domain is able to bind proteins from many mammalian species, most notably IgGs (Hober S et al., (2007) J. Chromatogr. B Analyt. Technol. Biomed. Life Sci., 848(1): 40-47). Protein A binds the heavy chain of most immunoglobulins within the Fc region but also within the Fab region in the case of the human VH3 family (Jansson B et al, (1998) FEMS Immunol. Med. Microbiol., 20(1): 69-78). Protein A binds IgG from various species including human, mouse, rabbit and guinea pig but does not bind human lgG3 (Hober S et al., (2007) supra). The inability of human lgG3 to bind Protein A can be explained by the H435R and Y436F substitutions in the human lgG3 Fc region (EU numbering, Jendeberg et al., (1997) J. Immunol. Methods, 201(1): 25-34). Besides IgG, Protein A also interacts with IgM and IgA. Amongst human VH subclasses, VH3 is the only subclass to bind Protein A (Graille M et al., (2000) Proc. Natl. Acad. Sci. USA 97(10): 5399-5404), and all five domains of Protein A are known to bind this variable domain subclass (Jansson B et al, (1998) FEMS Immunol. Med. Microbiol., 20(1): 69-78. VH3 based immunoglobulins or fragments thereof are of major importance to the biotechnology industry. VH3 based molecules have been extensively developed since their ability to bind Protein A facilitates their functional pre-screening, and as such many synthetic or donor based phage display libraries or transgenic animal technologies used for antibody discovery are based on the VH3 subclass. In addition VH3 based molecules are often selected for their good expression and stability over other known heavy chain variable domain subclasses.

The capacity of Protein A to bind antibodies with such high affinity is the driving motivation for its industrial scale use in biologic pharmaceuticals. Protein A used for production of antibodies in bio pharmaceuticals is usually produced recombinantly in E. coli and functions essentially the same as native Protein A (Liu HF et al., (2010) MAbs, 2(5): 480-499).

Most commonly, recombinant Protein A is bound to a stationary phase chromatography resin for purification of antibodies. Optimal binding occurs at pH8.2, although binding is also good at neutral or physiological conditions (pH 7.0-7.6). Elution is usually achieved through pH shift towards acidic pH (glycine-HCI, pH2.5-3.0). This effectively dissociates most protein-protein and antibody-antigen binding interactions without permanently affecting protein structure. Nevertheless, some antibodies and proteins are damaged by low pH and it is best to neutralize immediately after recovery by addition of l/10th volume of alkaline buffer such as 1 M Tris-HCI, pH 8.0 to minimize the duration of time in the low-pH condition.

There are various commercially available Protein A chromatography resins. The main differences between these media are the support matrix type, Protein A ligand modification, pore size and particle size. The differences in these factors give rise to differences in compressibility, chemical and physical robustness, diffusion resistance and binding capacity of the adsorbents (Hober S et al., (2007), supra). Examples of Protein A chromatography resins include but are not limited to the MabSelect SuRe™ Protein A resin and MabSelect™ Protein A resin from GE Healthcare as used in examples.

The term "chromatography" refers to protein liquid chromatography and includes fast protein liquid chromatography (FPLC) which is a form of liquid chromatography that is often used to analyze or purify mixtures of proteins. As in other forms of chromatography, separation is possible because the different components of a mixture have different affinities for two materials, a moving fluid (the mobile phase) which passes through a porous solid (the stationary phase). In FPLC, the mobile phase is an aqueous solution, or "buffer". The buffer flow rate can be operated under gravity flow or controlled by a positive-displacement pump which is normally kept at a constant rate, while the composition of the buffer can be varied by drawing fluids in different proportions from two or more external reservoirs. The stationary phase is a resin composed of beads, usually of cross-linked agarose, packed into a cylindrical glass or plastic column. FPLC resins are available in a wide range of bead sizes and surface ligands depending on the application.

The process of "affinity chromatography" involves the use of an affinity reagent as ligands which are cross-linked to the stationary phase and that have binding affinity to specific molecules or a class of molecules. Ligands can be bio-molecules, like protein ligands or can be synthetic molecules. Both types of ligand tend to have good specificity. The most commonly used protein ligand in production is the affinity reagent Protein A. In affinity chromatography when the solution (for example a crude cell supernatant containing a protein of interest) is loaded onto to the column the target protein is usually adsorbed while allowing contaminants (other proteins, lipids, carbohydrates, DNA, pigments, etc.) to pass through the column. The adsorbent itself is normally packed in a chromatography column; though the adsorption stage can be performed by using the adsorbent as a stirred slurry in batch binding mode. The next stage after adsorption is the wash stage, in which the adsorbent is washed to remove residual contaminants. The bound protein is then eluted in a semi-pure or pure form. Elution is normally achieved by changing the buffer or salt composition so that the protein can no longer interact with the immobilized ligand and is released. In some instances the protein of interest may not bind the affinity resin and affinity chromatography is directed at binding unwanted contaminants and the unbound fraction is therefore collected to isolate the protein of interest. Affinity chromatography can be performed in a fixed bed or a fluidised bed.

The term "gradient mode chromatography" refers to a chromatography method wherein the proportion of the "elution" buffer (buffer B) is increased from 0% to 100% in a gradual or stepwise manner.

The terms "capture-elution mode chromatography" or "capture-elution purification mode" or "capture-elution purification" refers to a chromatography method wherein the proportion of the "elution" buffer (buffer B) is not increased from 0% to 100% in a gradual or stepwise manner but rather directly applied at a 100% after capture and optionally a wash step with running buffer (buffer A).

Figure 1. Clinical information of gastric cancer disorders

Figure 2. Clinical information of metastatic breast cancer donors Figure 3. M-score data for cumulative gastric tumors (n=25). Note: M-Score predicts treatment outcome based on multiple input parameters for the given tumor specimen. Positive prediction of response: M-Score >25 or M-Score greater than 25. Negative prediction of response: M-Score < 25 or M-Score of 25 or lower. There are no M-Scores for Rxl as M-Score values are derived from parameters relative to the control untreated samples in Rxl.

Figure 4. M-score report metastatic breast cancer tumors (n=25). Note: M-Score predicts treatment outcome based on multiple input parameters for the given tumor specimen. Positive prediction of response: M-Score >25 or M-Score greater than 25. Negative prediction of response: M-Score < 25 or M-Score of 25 or lower. There are no M-Scores for Rxl as M-Score values are derived from parameters relative to the control untreated samples in Rxl.

Figure 5. M-Score Response Analysis for Gastric Tumors and Metastatic Breast Tumors (n=50) Tested with GBR 1302 (Rx2), Herceptin (Rx3) and Anti-PD-1 + GBR 1302 (Rx4). Note: M-Score predicts treatment outcome based on multiple input parameters for the given tumor specimen. Positive prediction of response: M-Score >25 or M-Score greater than 25. Negative prediction of response: M- Score < 25 or M-Score of 25 or lower. There are no M-Scores for Rxl as M-Score values are derived from parameters relative to the control untreated samples in Rxl.

Figure 6. M-Score Response Analysis of Gastric tumors (n=25) Tested with GBR 1302 (Rx2), Herceptin (Rx3) and Anti-PD-1 + GBR 1302 (Rx4).

Figure 7. M-Score Response Analysis of metastatic breast tumors (n=25) tested with GBR 1302 (Rx2), Herceptin (Rx3) and Anti-PD-1 + GBR 1302 (Rx4).

Figure 8. Score for Percentage Positivity of CD8/Ki-67 Dual IHC Stained Cells (averaged across multiple fields and sections) Denoting Proliferative Cytotoxic T Cells. Score for percentage positivity of CD8/Ki- 67 dual IHC stained cells (average across multiple fields and sections) denotes proliferative cytotoxic T cells.

Figure 9. Score for percentage positivity of CD8/Ki-67 dual IHC stained cells (averaged across multiple fields and sections) denoting proliferative cytotoxic T cells. Score for percentage positivity of CD8/Ki- 67 dual IHC stained cells (average across multiple fields and sections) denotes proliferative cytotoxic T cells.

Figure 10. M-Score and HER2 Status for Responders in Gastric & Metastatic Breast Tumors (19/50). Note: M-Score predicts treatment outcome based on multiple input parameters for the given tumor specimen. Positive prediction of response: M-score >25 or M-score greater than 25. Negative prediction of response: M-score < 25 or M-score of 25 or lower. There are no M-Scores for Rxl as M- Score values are derived from parameters relative to the control untreated samples in Rxl.

Figure 11. Analysis of HER2 Status for Responders in Gastric and Metastatic Breast Tumors

Figure 12. Response Analysis by Tissue for Responders in Gastric & Metastatic Breast Tumors (n=25 each). Note: M-Score predicts treatment outcome based on multiple input parameters for the given tumor specimen. Positive prediction of response: M-Score >25 or M-Score greater than 25. Negative prediction of response: M-Score < 25 or M-Score of 25 or lower. There are no M-Scores for Rxl as M- Score values are derived from parameters relative to the control untreated samples in Rxl.

Figure 13. Pathological evaluation of GS1 Figure 14. Pathological evaluation of GS2 Figure 15. Pathological evaluation of GS5 Figure 16. Pathological evaluation of GS6 Figure 17. Pathological evaluation of GS10 Figure 18. Pathological evaluation of GSM Figure 19. Pathological evaluation of GS15 Figure 20. Pathological evaluation of GS15 Figure 21. Pathological evaluation of GS19 Figure 22. Pathological evaluation of GS20 Figure 23. Pathological evaluation of GS21 Figure 24. Pathological evaluation of GS22 Figure 25. Pathological evaluation of GS23 Figure 26. Pathological evaluation of GS24 Figure 27. Pathological evaluation of GS29 Figure 28. Pathological evaluation of GS30 Figure 29. Pathological evaluation of GS33 Figure 30. Pathological evaluation of GS35 Figure 31. Pathologica evaluation of GS36 Figure 32. Pathologica evaluation of GS38 Figure 33. Pathologica evaluation of GS39 Figure 34. Pathologica evaluation of GS43 Figure 35. Pathologica evaluation of GS44 Figure 36. Pathologica evaluation of GS45 Figure 37. Pathologica evaluation of GS46 Figure 38. Pathologica evaluation of CaBrTl Figure 39. Pathologica evaluation of CaBrT2 Figure 40. Pathologica evaluation of CaBrT3 Figure 41. Pathologica evaluation of CaBrT4 Figure 42. Pathologica evaluation of CaBrT5 Figure 43. Pathologica evaluation of CaBrT6 Figure 44. Pathologica evaluation of CaBrT7 Figure 45. Pathologica evaluation of CaBrT8 Figure 46. Pathologica evaluation of CaBrTIO Figure 47. Pathologica evaluation of CaBrTll Figure 48. Pathologica evaluation of CaBrTM Figure 49. Pathologica evaluation of CaBrT15 Figure 50. Pathologica evaluation of CaBrT18 Figure 51. Pathologica evaluation of CaBrT19 Figure 52. Pathologica evaluation of CaBrT20 Figure 53. Pathologica evaluation of CaBrT21 Figure 54. Pathologica evaluation of CaBrT26 Figure 55. Pathologica evaluation of CaBrT27 Figure 56. Pathological evaluation of CaBrT30 Figure 57. Pathological evaluation of CaBrT31 Figure 58. Pathological evaluation of CaBrT34 Figure 59. Pathological evaluation of CaBrT38 Figure 60. Pathological evaluation of CaBrT39 Figure 61. Pathological evaluation of CaBrT42 Figure 62. Pathological evaluation of CaBrT44

Figure 63. Gastric and Metastatic Breast Tumors Selected for Phase 2 and Phase 3 Cytokine and NanoString Analysis

Figure 64. Log Cell Scores from Vehicle Arm (Rxl)

Figure 65. Heat Map of Immune Activity in the Vehicle Arm (Rxl) by Sample

Figure 66. Modulation of Published RNA Signatures

Figure 67. Modulation of Published RNA Signatures

Figure 68. Pair-wise, Differential Gene Expression: Rx2 vs Rxl

Figure 69. Differential Gene Expression: Rx2 vs Rxl, M-Score>25 (Predicted Responders) Only

Figure 70. Differential Gene Expression: Rx2 vs Rxl, M-Score < 25 (Predicted Non-Responders) Only

Figure 71. Pairwise Differential Gene Expression: Rx3 vs Rxl

Figure 72. Differential Gene Expression: Rx3 vs Rxl, of Predicted Responders Only

Figure 73. Differential Gene Expression: Rx3 vs Rxl, of Predicted Non-Responders Only

Figure 74. Pairwise Differential Gene Expression: Rx4 vs Rxl

Figure 75. Pairwise Differential Gene Expression: Rx4 vs Rxl

Figure 76. Differential Gene Expression: Rx4 vs Rxl, for Predicted Responders Only

Figure 77. Differential Gene Expression: Rx4 vs Rxl, for Predicted Non-Responders Only

Figure 78. Common Genes Found Among the List of Differentially Expressed Genes

for Predicted Responders OnlyFigure 79. Common Genes Found Among the List of Differentially Expressed Genes for Predicted Non-Responders Only. Figure 80. Differentially expressed genes unique for Predicted Responders vs Predicted Non- Responders for Rx2 (GBR 1302) Only.

Figure 81. Analysis summary as a boxplot within predicted responders of RX2 versus Rxl, showing the top 10 genes.

Figure 82. Analysis summary as a boxplot within predicted responders of RX4 versus Rxl, showing the top 10 genes.

Figure 83. Analysis summary as a boxplot within predicted responders of similarities between RX4 and Rx2 showing 6 genes.

Figure 84. Analysis summary as a boxplot within predicted responders of T-cell proliferation genes Rx2 versus Rxl (circle n=l) and between RX4 versus Rxl (n=0).

Figure 85. Analysis summary as a boxplot within predicted responders of immuno-suppressive genes Rx2 versus Rxl (circle n=3) and between RX4 versus Rxl (n=0).

Figure 86. Cumulative Cytokine Data - IFNy

Figure 87. Cumulative Cytokine Data - TN Fa

Figure 88. Cumulative Cytokine Data - IL-2

Figure 89. Cumulative Cytokine Data - IL-6

Figure 90. Cumulative Cytokine Data - IL-8

Figure 91. Cumulative Cytokine Data - IL-10

Figure 92. Cumulative Cytokine Data - IL-12

Figure 93. Cumulative Cytokine Data - IL-17A

Figure 94. Cumulative Cytokine Data - Granzyme B

Figure 95. Cumulative Cytokine Data - Perforin

Figure 96. Gastric Tumor (GS5), Individual Cytokine Data for Phase 2 Gastric (n=4) and Metastatic Breast (n=19) Tumors

Figure 97. Gastric Tumor (GS6), Individual Cytokine Data for Phase 2 Gastric (n=4) and Metastatic Breast (n=19) Tumors Figure 98. Gastric Tumor (GS10), Individual Cytokine Data for Phase 2 Gastric (n=4) and Metastatic Breast (n=19) Tumors

Figure 99. Gastric Tumor (GSM), Individual Cytokine Data for Phase 2 Gastric (n=4) and Metastatic Breast (n=19) Tumors

Figure 100. Breast Tumor (CaBrTl), Individual Cytokine Data for Phase 2 Gastric (n=4) and Metastatic Breast (n=19) Tumors

Figure 101. Breast Tumor (CaBrT3), Individual Cytokine Data for Phase 2 Gastric (n=4) and Metastatic Breast (n=19) Tumors

Figure 102. Breast Tumor (CaBrT4), Individual Cytokine Data for Phase 2 Gastric (n=4) and Metastatic Breast (n=19) Tumors

Figure 103. Breast Tumor (CaBrT5), Individual Cytokine Data for Phase 2 Gastric (n=4) and Metastatic Breast (n=19) Tumors

Figure 104. Breast Tumor (CaBrT6), Individual Cytokine Data for Phase 2 Gastric (n=4) and Metastatic Breast (n=19) Tumors

Figure 105. Breast Tumor (CaBrT7), Individual Cytokine Data for Phase 2 Gastric (n=4) and Metastatic Breast (n=19) Tumors

Figure 106. Breast Tumor (CaBrT8), Individual Cytokine Data for Phase 2 Gastric (n=4) and Metastatic Breast (n=19) Tumors

Figure 107. Breast Tumor (CaBrTIO), Individual Cytokine Data for Phase 2 Gastric (n=4) and Metastatic Breast (n=19) Tumors

Figure 108. Breast Tumor (CaBrT18), Individual Cytokine Data for Phase 2 Gastric (n=4) and Metastatic Breast (n=19) Tumors

Figure 109. Breast Tumor (CaBrT19), Individual Cytokine Data for Phase 2 Gastric (n=4) and Metastatic Breast (n=19) Tumors

Figure 110. Breast Tumor (CaBrT20), Individual Cytokine Data for Phase 2 Gastric (n=4) and Metastatic Breast (n=19) Tumors

Figure 111. Breast Tumor (CaBrT27), Individual Cytokine Data for Phase 2 Gastric (n=4) and Metastatic Breast (n=19) Tumors Figure 112. Breast Tumor (CaBrT30), Individual Cytokine Data for Phase 2 Gastric (n=4) and Metastatic Breast (n=19) Tumors

Figure 113. Breast Tumor (CaBrT31), Individual Cytokine Data for Phase 2 Gastric (n=4) and Metastatic Breast (n=19) Tumors

Figure 114. Breast Tumor (CaBrT34), Individual Cytokine Data for Phase 2 Gastric (n=4) and Metastatic Breast (n=19) Tumors

Figure 115. Cumulative Cytokine Data - IFNy Figure 116. Cumulative Cytokine Data - TNFa Figure 117. Cumulative Cytokine Data - IL-2 Figure 118. Cumulative Cytokine Data - IL6 Figure 119. Cumulative Cytokine Data - IL8 Figure 120. Cumulative Cytokine Data - IL10 Figure 121. Cumulative Cytokine Data - IL12 Figure 122. Cumulative Cytokine Data - IL-17A Figure 123. Cumulative Cytokine Data - Granzyme B Figure 124. Cumulative Cytokine Data - Perforin

Figure 125. Gastric Tumor (GS23), Individual Cytokine Data for Phase 3 Gastric (n=8) and Metastatic Breast (n=2) Tumors

Figure 126. Gastric Tumor (GS24), Individual Cytokine Data for Phase 3 Gastric (n=8) and Metastatic Breast (n=2) Tumors

Figure 127. Gastric Tumor (GS33), Individual Cytokine Data for Phase 3 Gastric (n=8) and Metastatic Breast (n=2) Tumors

Figure 128. Gastric Tumor (GS35), Individual Cytokine Data for Phase 3 Gastric (n=8) and Metastatic Breast (n=2) Tumors

Figure 129. Gastric Tumor (GS36), Individual Cytokine Data for Phase 3 Gastric (n=8) and Metastatic Breast (n=2) Tumors

Figure 130. Gastric Tumor (GS39), Individual Cytokine Data for Phase 3 Gastric (n=8) and Metastatic Breast (n=2) Tumors Figure 131. Gastric Tumor (GS43), Individual Cytokine Data for Phase 3 Gastric (n=8) and Metastatic Breast (n=2) Tumors

Figure 132. Gastric Tumor (GS44), Individual Cytokine Data for Phase 3 Gastric (n=8) and Metastatic Breast (n=2) Tumors

Figure 133. Breast Tumor (CaBrT15), Individual Cytokine Data for Phase 3 Gastric (n=8) and Metastatic Breast (n=2) Tumors

Figure 134. Breast Tumor (CaBrT38), Individual Cytokine Data for Phase 3 Gastric (n=8) and Metastatic Breast (n=2) Tumors

Figure 135. RNA Yield of Metastatic Breast Tumors for Phase 2 and Phase 3

Figure 136. RNA Yield of Gastric Tumors for Phase 2 and Phase 3

Figure 137. Effect on immuno-stimulatory and immuno-suppressive genes. The heat-map represents log2zscores for the immune suppressive and immune stimulatory genes along with clustering using correlation distance and average linkage.

Figure 138. Cytokine secretion profile with time (24, 48 and 72 hours of respective drug treatment), data represents meaniSEM of log2 Fold Change (Log2FC) calculated with respect to the vehicle control.

Figure 139. Volcano plot of DESeq2 for GBR1302 vs vehicle, listing important upregulated and downregulated genes. Padj<0.05 and abs(log2fc)>0.5.

Figure 140. Ingenuity Pathway Analysis (IPA) of the deregulated genes as observed in fig.139.

Figure 141. Change in Variance of different immune signatures generated from vehicle vs GBR1302/ Flerceptin.

Figure 142 The response rate of the Top 6 gene signatures which shows maximum change in variance upon GBR1302 treatment. The data represents, induced (top 35%) and reduced (bottom 35%) patient cohorts from the waterfall plot of each signature Log2(Z-Score).

Figure 143 Waterfall plot of Activated CD8 gene signature of Log2 (Z-Score) showing the induced (blue) and reduced (red) cohort.

Figure 144. Cytokine release at 24, 48 and 72 hrs of GBR1302 treatment with respect to vehicle control for Induced and reduced cohorts for activated CD8 gene signature. Data represents the meaniSEM of the Log2FC of the respective cohorts Figure 145. T cell related cytokine profiling of the CANscriptTM predicted responders and non responders in the induced activated CD8 cohort. Data represents the meaniSEM of the Log2FC of the responders and non-responders groups.

Figure 146. Log2(expression counts) plotted for vehicle control, GBR1302 and combination arm for four immune suppressive genes.

Figure 147. Waterfall plot of Activated CD8 gene-signature (log2 (Z-Score) of GBR1302+KeytrudaTM (Combination) vs vehicle control. The data highlights the non-responders (Red), common responders (both in GBR1302 and the combination arm (Blue), and only combination arm responders (green).

Figure 148. Deseq2 between the combination vs vehicle control for responders (left) and non respoders (right) in the ActCD8 reduced cohort from Fig4b.

Figure 149. Ingenuity Pathway Analysis (IPA) of the deregulated genes as observed in Fig.4d.

Figure 150. IL17a cytokine levels (averaged) in control, GBR1302 and GBR1302+anti-PDl.

EXAMPLES

Example 1 Materials and Methods

1.1 Procedure for detection of CD8 and Ki-67 by Immunohistochemistrv

The basic general principle that underpins this technique and those detailed in paragraphs 1.2 and 1.3, is the antigen-antibody reaction which is amplified and visualized. The target antigen may be physically inaccessible to the antibody due to protein folding caused during fixation. This is overcome by a procedure called antigen retrieval, where heat is used to alter the protein folding and the antigens become more accessible under a well-defined buffer condition. Quenching the endogenous peroxidase and protein block are important steps to avoid background staining and non-specific binding. This standardized protocol uses a three-layered detection system that involves the primary antibody (usually rabbit /mouse mAb or rabbit pAb) which binds to the target antigen; FIRP-conjugated secondary antibody (usually goat anti-rabbit or anti mouse IgG, depending on compatibility) which binds the primary antibody. The antibodies aid detection of antigen and signal amplification. The peroxidase enzyme, which is present in the secondary antibody, catalyses a reaction where DAB (3,3'- diaminobenzidine) produces a relatively stable brown precipitate which can be visualized under a microscope, ultimately detecting the target antigen. The main advantage of the automation is the retrieval and staining protocols for standardization and staffing considerations and better reproducibility with the results obtained.

Three to four micron (3-4 pm) thick tissue sections are obtained from FFPE blocks on Tomo hydrophilic adhesion slides that offer superior tissue adhesion. Specimen Rejectin Criteria: Qualifying status is determined depending on the adequacy of tumor in the histopathological examination. If there is no tumor or minimal tumor, the specimen is rejected for that particular study. Sample storage: FFPE blocks and slides can be stored for up to 10 years.

Detailed protocol: 1. To start the run, empty the waste container according to the laboratory standards and replace it.

2. Prepare the bulk solutions according to the manufacturer's instructions.

3. Replace the bulk bottles in the desired slots. Note: DO NOT exchange the slots of the specified reagents.

4. Switch ON the power button and click Ventana icon.

5. Label the slides by clicking on the slide label icon at the bottom of the main screen. Select the protocol and enter the details such as Sample ID, Cancer Type, Study Name and date and print the labels.

6. To load the slides, open the slide tray and on the thermopad, load the slides with barcode away from the centre.

7. Before starting the run, load the reagents in the reagent rack and load it on the reagent compartment. Two secondary antibody kits should also be placed on the reagent rack (UltraView Universal DAB detection kit and UltraView Universal AP RED detection kit).

8. To start the run, click on the start button in the Ventana nexus software and enter the number of slides.

9. After dewaxing and cell conditioning instrument will give the alarm to add the primary antibody

(Ki-67).

10. During next titration pop-up add the second primary antibody (CD8).

11. Add the antibody at 45^Q and close the compartment once done followed by pushing Start button on the bottom panel.

12. After the staining is done, dip the slides in two changes of 100% alcohol for 4 min.

13. Dip in xylene and then mount

14. Register reagents and dispensers appropriately every time a new lot is used.

Table 1: Antibody specifications. *(HKi-67 will be stained brown and CD8 will be stained red. Proliferating CD8 cells will be quantified by a qualified pathologist).

1.2 Procedure for Detection of Her2/neu by Immunohistochemistrv

The aim of this method is to operate Benchmark GX (Roche) for performing automated IHC staining for Her2/neu. The purpose of this SOP is to provide knowledge about the principle and steps involved in the immunohistochemical staining of tissue sections from FFPE blocks using Ventana Auto Stainer for Fler2/neu. The study involves assessment of Fler2/neu status at baseline in metastatic breast and gastro-oesophageal tumor specimens.

Three-Four micron (3-4pm) thick tissue sections are obtained from FFPE blocks on Tomo hydrophilic adhesion slides that offer superior tissue adhesion. Specimen Rejection Criteria: Qualifying status is determined depending on the adequacy of tumor in the histopathological examination. If there is no tumor or minimal tumor, the specimen is rejected for that particular study. Sample storage: FFPE blocks and slides can be stored for up to 10 years.

Detailed protocol:

1. To start the run, empty the waste container according to the laboratory standards and replace it.

2. Prepare the bulk solutions according to the manufacturer's instructions.

4. Switch ON the power button and click Ventana icon.

5. Label the slides by clicking on the slide label icon at the bottom of the main screen. Select the protocol and enter the details such as Sample ID, Cancer Type, Study Name and Date, and print the labels.

7. Before starting the run, load the reagents in the reagent rack and load the rack on the reagent compartment.

9. After dewaxing and cell conditioning, the instrument will sound the alarm to add the primary antibody (Her2/neu).

10. Add the antibody at 45^Q, close the compartment, and push the Start button on the bottom panel.

11. After the staining is done, dip the slides in two changes of 100% alcohol for 4 min.

12. Dip in xylene and then mount.

13. Register reagents and dispensers appropriately every time for each new lot.

Table 2: Antibody specifications

1.3 Procedure for Detection of CD8 by Immunohistochemistrv

This procedure is to operate Benchmark GX (Roche) for performing automated IHC staining for CD8. The purpose of this SOP is to provide knowledge about principle and steps involved in the immunohistochemical staining of tissue sections from FFPE blocks using Ventana Autostainer for CD8. The study involves assessment of CD8 status post-treatment with the suggested drugs and appropriate controls in metastatic breast and gastro-oesophageal tumor specimens.

Detailed protocol:

2. Prepare the bulk solutions according to the manufacturer's instructions.

4. Switch ON the power button and click Ventana icon.

9. After dewaxing and cell conditioning, the instrument will sound the alarm to add the primary antibody (CD8).

10. Add the antibody at 45^Q, close the compartment and push the Start button on the bottom panel.

12. Dip in xylene and then mount.

13. Register reagents and dispensers appropriately every time a new lot is used.

Table 3. Antibody specifications

1.4 Canscript - M-Score

Mitra Biotech's (Woburn, Massachusetts, USA) CANscript platform uniquely provides a tumor model platform that preserves the native-state proliferation, morphology and viability of tumor cells within the context of the original TME. The platform consists of an ex vivo patient tumor culture model that uses intact tumor slices cultured with autologous plasma and autologous peripheral blood mononuclear cells.

By maintaining the complex structure, heterogeneity and behavior of tumors in culture, CANscript can be used to predict the response of individual patient tumors to monotherapies and combination therapies of many classes of drugs with high accuracy.

CANscript monitors a number of phenotypic readouts, both terminal and kinetic, including tumor cell proliferation, cell death, viability and tumor morphology. Data are analyzed using proprietary machine-learning algorithms that connect ex vivo data with clinical outcomes.

In the present invention 25 metastatic breast cancer (Met CaBr) and 25 primary gastric or primary gastroesophageal (Gastric) human tumors were obtained and cultured in the CANscript TME assay platform.

The study was performed as follows:

Phase 1

• Procure 25 samples of metastatic breast cancer (Met CaBr) and 25 samples of either gastric or gastroesophageal cancer (Gastric).

• Assess M-Score for all 50 samples in the CANscript culture system.

• Determine HER2 status at baseline (TO) for all 50 samples.

• Run IHC for total CD8 and dual staining for CD8 and Ki67 for all 50 samples.

Phase 2

• Once Phase 1 data from samples 1-25 was reported, Glenmark would determine which samples would be further analyzed using Cytokine and NanoString Panels.

• Perform RNA profiling using the NanoString PanCancer Immune Profiling Panel at T72 on all study arms.

• Perform 10-plex Cytokine/Chemokine analysis at T24, T48 and T72 time points (IFN-g, IL-6, IL- 10, TNF-a, Granzyme B, Perforin, IL-2, IL-8, IL-12, IL-17A). Phase 3

• Once Phase 1 data for samples 26-50 was reported, Glenmark would determine which

samples would be further analyzed using the Cytokine and NanoString Panels.

• Perform RNA profiling using the NanoString PanCancer Immune Profiling Panel at T72 on all study arms

• Perform 10-plex Cytokine/Chemokine analysis at T24, T48 and T72 time points (IFN-g, IL-6, IL- 10, TNF-a, Granzyme B, Perforin, IL-2, IL-8, IL-12, IL-17A).

RNA was isolated from FFPE tumor fragments in the vehicle arm (Rxl) in the absence of culture PBMC's. RNA applied to the NanoString PanCancer Immune Profiling panel. Normalized log2 Cell Scores shown as calculated using the nSolver Advanced Analysis software. All settings used default parameters with the exception of the "omit low count data" parameter which was manually set to 10. The different treatment arms were as shown in Table 4

Table 4. Treatments arms

Example 2. Results - M-Score

The inventors set out to undertake translational studies to identify responder and non-responders to GBR1302 in metastatic Breast cancer and Gastric cancer, this study was also undertaken to evaluate the rationale of using a checkpoint inhibitor as a combination agent with GBR1302. Finally this study set out to identify differentiation features of CD3 engagers and in particular the induction of T cell memory responses, the induction of Effector T cells and elucidate mechanisms that tilt the balance between regulatory T cells vs Effector T cells.

This study tested GBR 1302 as a single agent (Rx2), and in combination with an anti-PD-1 agent (Rx4), as well as in comparison to Flerceptin (Rx3), in human metastatic breast and primary gastric or gastroesophageal tumors cultured in the Mitra CANscript TME assay platform.

The first outcome of this study is a prediction, called the M-Score, of whether individual patient tumors would respond to the monotherapies (Rx2, Rx3) and combination therapies (Rx4).

The predicted response rate based on M-score (>25 is a predictive responder with 90% confidence to respond in clinic) of 1302 monotherapy and GBR 1302 + anti-PDl combination therapy is based upon the following molecular basis, immune cell activation, potential immune mediated resistance mechanisms (allowing for further translational based development of other combinations), impact on Immune cell proliferation, impact of treatment on immuno-suppressive mechanisms.

The M-score is based on a proprietary trained alogorithm on ~2000 patient samples predicting responder and non-responders and takes into account a composite scoring analysis which includes both kinetic and endpoint readouts. A M-Score >25 is considered a responder and has a 90% confidence to be a responder to the treatment in clinical setting.

A subset of the assays used to generate the score are shown in Table 5.

ATP Level

Table 5. Subset of the assays used to generate the score

Tumor Procurement

A total of 46 metastatic breast cancer samples and 47 gastric cancer samples were collected from different cancer hospitals after receiving approvals from Institutional Review Board (IRB) or Institutional Ethics Committee (IEC). Donors infected with HIV, TB and HPV were excluded from this study. Tumor tissues and autologous blood samples were collected after obtaining informed consent from the enrolled donors, and were transported to the facility at Mitra Biotech, Bangalore, India (Refer to 'Sample Collection and Transport'). Donor demographic details for gastric cancer and metastatic breast cancer donors are shown in Figure 1 and Figure 2. For the metastatic breast tumors, 25 out of 46 tumors procured passed Mitra's internal quality control, and were utilized in this study. For the gastric tumors, 25 out of 47 tumors procured passed quality control and were utilized in this study. Ex vivo Culture of Tumor Specimens

Tumor sections were cultured in the CANscript system, in the presence of autologous immune components. Tumors were treated with test arms Rxl, Rx2, Rx3 or Rx4 (as described above), and the response of a given tumor to the drug treatment was expressed as M-Score.

M-Score Analysis

All input parameters from the CANscriptTM assays have been integrated to generate single M-Score values using Mitra's proprietary algorithm (Refer to CANscript Technology). M-Score is used to predict clinical response outcome of drug treatment for a given tumor specimen. In Table 3 below, M-Scores highlighted in yellow indicate responders and M-Scores which are not highlighted indicate non responders to the treatment arms studied.

The outcome for Gastric tumors is shown in Figure 3 and Figure 4for metastatic Breast Tumors. M- Score predicts the in-patient treatment outcome based on multiple input parameters for the given tumor specimen. Positive prediction of response: M-score >25 or M-score greater than 25. Negative prediction of response: M-score < 25 or M-score of 25 or lower.

In summary out of 50 gastric and metastatic breast tumors, 19/50 tumors responded to at least one treatment arm (Rx2, 3, 4). Maximum predicted efficacy (18/50 tumors (36%)) was observed in the combination arm Rx4: (Anti-PD-1 + GBR1302). 10 out of 50 tumors (20%) responded to Rx2 arm (GBR1302), 6 out 50 tumors (12%) responded to Rx3 arm (Flerceptin) Figure 5.

The observed 36% response rate observed for GBR 1302 in combination with Pembrolizumab, compares to published clinical data of Pembrolizumab of response rates between 10-20%.

The number of tumors responding to Flerceptin was therefore 12%, the number of responders to GBR 1302 was 20% and the number of responders to the combination of GBR 1302 and Pembrolizumab was 36%.

In Gastric tumors, 9/25 tumors responded to at least one treatment arm (Rx2, 3, 4) Figure 4. Maximum predicted efficacy (8/25 tumors (32%)) was observed in the combination arm Rx4: (Anti-PD-1 + GBR1302). 5 out of 25 tumors (20%) responded to Rx2 arm (GBR1302), 4 out 25 tumors (16%) responded to Rx3 arm (Flerceptin) Figure 6.

In metastatic breast tumors, 10/25 tumors responded to at least one treatment arm (Rx2, 3, 4) Figure 4. Maximum predicted efficacy (10/25 tumors (40%)) was observed in the combination arm Rx4: (Anti- PD-1 + GBR1302). 5 out of 25 tumors (20%) responded to Rx2 arm (GBR1302), 2 out 25 tumors (8%) responded to Rx3 arm (Flerceptin) Figure 7. In Figure 8, the Cumulative Analysis of CD8/Ki67 for Gastric Cancer Tumors is presented. Ki-67 (also known as MKI67) is a cellular marker for proliferation and CD8 is a marker for cytotoxic T cells. Score for percentage positivity of CD8/Ki-67 dual IHC stained cells (averaged across multiple fields and sections) denotes proliferative cytotoxic T cells. Predicted responders are shaded.

In Figure 9, the Cumulative Analysis of CD8/Ki67 for Metastatic Breast Cancer Tumors is presented. Ki- 67 (also known as MKI67) is a cellular marker for proliferation and CD8 is a marker for cytotoxic T cells. Score for percentage positivity of CD8/Ki-67 dual I HC stained cells (averaged across multiple fields and sections) denotes proliferative cytotoxic T cells. Predicted responders are shaded.

In Figure 10 the relationship between M-score and HER2 status is shown. It is known for Herceptin that tumor killing is dependent in part upon the H ER2 status of a patient's tumor and Herceptin is only licensed for the treatment of patients with 2+ and 3+ tumors. The efficacy of GBR 1302 and particularly GBR 1302 in combination with Pembrolizumab, is less dependent upon the HER2 status of the tumor, greatly expanding the potential patient population treatable with GBR 1302 and a combination of GBR 1302 and Pembrolizumab.

Differences in response rates by HER2 status (for all arms combined) are shown in Figure 11 and Figure 12.

Individual Tumor Data for 25 Gastric Tumors

Molecular pathological evaluation was performed independently by multiple pathologists and representative images of histology, tumor cell proliferation and active caspase-3 are provided below for individual gastric tumors at 20x magnification (Figure 13-Figure 37).

Individual Tumor Data for 25 Metastatic Brest Cancer Tumors

Molecular pathological evaluation was performed independently by multiple pathologists and representative images of histology, tumor cell proliferation and active caspase-3 are provided below for individual gastric tumors at 20x magnification (Figure 38-Figure 62).

In Phase 2 of this study, 15 metastatic breast tumors and 4 gastric tumors were selected (shaded) for further analysis as set out in Figure 63. To investigate the molecular basis of responders, as well as further elucidate relevant molecular immune cell signatures. A Nanostring pan Cancer panel at T72 time point was generated using a770 gene signature, as well as a 10-plex Cytokine/Chemokine analysis at T24, T48 and T72 time points of IFN-g, IL-6, IL-10, TNF-a, Granzyme B, Perforin, IL-2, IL-8, IL-12 and IL-17A. In Phase 3 of this study 2 metastatic breast tumors and 8 gastric tumors were selected (shaded) for further analysis as set out in Figure 63. Example 3. Phase 2 Nanostring data

Log cell scores from the Rxl arm are shown in Figure 64.

RNA was isolated from FFPE tumor fragments in the vehicle arm (Rxl) in the absence of culture PBMC's. RNA was applied to the NanoString PanCancer Immune Profiling panel. Normalized log 2 Cell Scores shown as calculated using the nSolver Advanced Analysis software. All settings used default parameters with the exception of the "omit low count data" parameter which was manually set to 10.

Cell Score values from Figure 64, clustered and displayed as a heat map in Figure 54. RNA was isolated from FFPE tumor fragments in the vehicle arm (Rxl) in the absence of culture PBMC's. RNA applied to the Nanostring PanCancer Immune Profiling panel. Samples and immune subsets clustered hierarchically based on Euclidian distance. Heat map is used to indicate abundance where the color range reflects the min and max of each column in isolation.

In Figure 65as per Nanostring guidance, this analysis allows for a relative assessment of immune cell abundance between samples but the data does not enable comparison between subsets either at the individual level or between samples.

Therefore, we can conclude that among the 29 tumors interrogated, we see diverse abundance on the subset level potentially reflecting different propensity to respond to the challenges represented by Rx2 through Rx4. Furthermore, the diversity does not appear to be skewed by tumor type.

Two published RNA-based signatures with relevance to clinical response to Pembrolizumab in melanoma and HNSCC were examined. Z-scores were calculated for each of the genes in the signatures across all samples and treatment arms (Figure 66). Those Z-scores were then averaged per sample and regression analysis was performed to explore the relationship between the signatures as well as the values associated with responders (M-Score predicted responders in blue).

The two signatures correlate very strongly (R2=0.894). Responders are distributed across all scores suggesting either the absolute Z-score is less important than the relative change under drug pressure or that these signatures do not predict response to the mechanisms represented in Rx2 through Rx4.

With reference to Figure 67, two published RNA-based signatures with relevance to clinical response to Pembrolizumab in melanoma and HNSCC were examined for the selected tumors in each arm of the study. The two signatures were TIS1 = CCL5, LAG 3, TIG IT, HLA-E, CXCR6, PSMB10, CD274, CXCL9, CMKLR1, CD8A, IDOl, PDCD1LG2, HLA-DQA1, ST ATI, CD27, CD276 and GAJ2 = CXCL9, CD8A, CCL2, CCL3, CCL4, CXCL10, HLA-DMA, H LAD MB, ICOS, GZMK, IRF1. These signatures have general biological significance surrounding T-cell biology, but have not been qualified for predicting clinical response to anti-PD-1 in breast or gastric cancer. In addition, the TIS was trained on clinical response to Pembrolizumab specifically. Therefore, applicability for a T-cell agonist or HER2 blockade is unknown.

Z-scores were calculated for each of the genes across all samples and treatment arms Rx2, Rx3 and Rx4. Those Z-scores were then averaged per sample and used to create the heat map shown in Figure 67 which shows directional change to expression for the whole signature across the arms of challenge (Rx2, RxX3, and Rx4) compared to vehicle (Rxl). The colour range reflects the min and max of each row in isolation. Sample/arm combinations with M-score predictive of clinical response are indicated with an asterisk (*).

Across many samples, relative to Rxl these signature increase when samples are treated with Rx2 and Rx4 compared to Rx3, implying shared molecule mechanisms.

These signatures have general biological significance surrounding T-cell biology, but to our knowledge have not been qualified for predicting clinical response to anti-PD-1 in breast or gastric cancer. In addition, the TIS was trained on clinical response to Pembrolizumab specifically. Therefore, applicability for a T-cell agonist or HER2 blockade is unknown. Finally, this analysis should be considered qualitative rather than quantitative.

Pairwise differential gene expression conducted comparing responders to non-responders across all samples. A p-value cutoff of <0.05 was applied. The top 25 genes by average fold change in the responder population that met the p-value criteria are shown in Table 13. The functional and/or evolutionary relationship between all 25 genes was explored using the STRING database

(https://string-db.org/) and is presented in Figure 68. Shown in Figure 68 in grey, are those that did not meet statistical significance (80%, 95% confidence).

Exposure of breast and gastric tumors to Rx2 appears to result in statistically-significant increases and decreases in genes that may provide information about the direct and indirect mechanism of action for this molecule in tumors that respond in a clinically meaningful way.

Tumors/treatment arm combinations with M-Score for greater than 25 (responders) only were used to perform differential gene expression profiling comparing Rxl to Rx2 using default parameters within nSolver. The analysis included breast and gastric tumor samples. At p-value cutoff of <0.05 was applied. From this analysis, a list of 22 genes were identified (Figure 69). The functional and/or evolutionary relationship between all 22 genes was explored using the STRING database

(https://string-db.org/) and is presented in Figure 69. Exposure of breast and gastric tumors to Rx2 appears to result in statistically-significant increases and decreases in genes that may provide information about the direct or indirect mechanism of action for this molecule in tumors that respond in a clinically meaningful way.

Tumors/treatment arm combinations for M-Score less than 25 (non-responders) were used to perform differential gene expression profiling comparing Rxl to Rx2 using default parameters within nSolver. The analysis included breast and gastric tumor samples. At p-value cutoff of <0.05 was applied. From this analysis, a list of 340 genes were identified. The top 10 up and down regulated genes are listed in Figure 70. The functional and/or evolutionary relationship between these 20 genes was explored using the STRING database (https://string-db.org/) and is presented in Figure 70.

Exposure of breast and gastric tumors to Rx2 appears to result in statistically-significant increases and decreases in genes that may provide information about the direct or indirect mechanism of action for this molecule for those tumors that do not respond in a clinically meaningful way.

Pairwise differential gene expression conducted comparing responders to non-responders across all samples. A p-value cutoff of <0.05 was applied. The top 25 genes by average fold change in the responder population that met the p-value criteria are shown in the Figure 71. The functional and/or evolutionary relationship between all 25 genes was explored using the STRING database (https://string-db.org/) and is presented in Figure 71. Conditions in gray are those that did not meet statistical significance (80%, 95% confidence).

Exposure of breast and gastric tumors to Rx3 appears to result in statistically-significant increases and decreases in genes that may provide information about the direct and indirect mechanism of action for this molecule in tumors that respond in a clinically meaningful way. The magnitude of the changes were very modest compared to Rx2 and Rx4.

Tumors/treatment arm combinations for M-Score greater than 25 (responders) were used to perform differential gene expression profiling comparing Rxl to Rx3 using default parameters within nSolver. The analysis included breast and gastric tumor samples. A p-value cutoff of <0.05 was applied. From this analysis, a list of 40 genes was identified (Figure 72). The functional and/or evolutionary relationship between all 40 genes was explored using the STRING database (https://string-db.org/) and is presented in Figure 72.

Exposure of breast and gastric tumors to Rx3 appears to result in statistically-significant increases and decreases in genes that may provide information about the direct or indirect mechanism of action for this molecule in tumors that respond in a clinically meaningful way. Tumors/treatment arm combinations for M-Score less than 25 (non-responders) were used to perform differential gene expression profiling comparing Rxl to Rx3 using default parameters within nSolver. The analysis included breast and gastric tumor samples. A p-value cutoff of <0.05 was applied. From this analysis, a list of 78 genes were identified. The top 10 up-regulated or down-regulated genes are listed in the Figure 73. The functional and/or evolutionary relationship between these 20 genes was explored using the STRING database (https://string-db.org/) and is presented in Figure 73.

Exposure of breast and gastric tumors to Rx3 appears to result in statistically-significant increases and decreases in genes that may provide information about the direct or indirect mechanism of action for this molecule for those tumors that do not respond in a clinically meaningful way.

Pairwise differential gene expression was conducted comparing responders to non-responders across all samples. A p-value cutoff of <0.05 was applied. The top 25 genes by average fold change in the responder population that met the p-value criteria are shown in Figure 74. The functional and/or evolutionary relationship between all 25 genes was explored using the STRING database (https://string-db.org/) and is presented in Figure 74. Conditions in grey are those that did not meet statistical significance (80%, 95% confidence).

Exposure of breast and gastric tumors to Rx4 appears to result in statistically-significant increases and decreases in genes that may provide information about the direct and indirect mechanism of action for this molecule in tumors that respond in a clinically meaningful way.

Pairwise differential gene expression was conducted comparing responders to non-responders across all samples. A p-value cutoff of <0.05 was applied. The genes identified from the analysis of Rx4 versus Rxl are profiled in Figure 75 as a means of understanding whether the combination of GBR-1302 with anti-PDl (Rx4) changes the same genes. Conditions in grey are those that did not meet statistical significance (80%, 95% confidence).

Comparing this fold change data with Rx4 versus Rxl genes, it is clear that the same patterns persist. Interestingly though, most of these genes do not reach significance in paired T-test. Furthermore, the magnitude of the effect may be slightly blunted when comparing the two arms Rx2 and Rx4 (see box plot in Figure 75).

Tumors/treatment arm combinations for M-Score greater than 25 (responders) were used to perform differential gene expression profiling comparing Rx4 to Rxl using default parameters within nSolver. The analysis included breast and gastric tumor samples. A p-value cutoff of <0.05 was applied. From this analysis, a list of 39 genes were identified (Figure 76). The functional and/or evolutionary relationship between all 39 genes was explored using the STRING database (https://string-db.org/) and is presented in Figure 76.

Exposure of breast and gastric tumors to Rx4 appears to result in statistically-significant increases and decreases in genes that may provide information about the direct or indirect mechanism of action for this molecule in tumors that respond in a clinically meaningful way.

Tumors/treatment arm combinations for M-Score less than 25 (non-responders) were used to perform differential gene expression profiling comparing Rx4 to Rxl using default parameters within nSolver. The analysis included breast and gastric tumor samples. A p-value cutoff of <0.05 was applied. From this analysis, a list of 82 genes were identified. The top 10 up-regulated or down-regulated genes are listed in Figure 77. The functional and/or evolutionary relationship between these 20 genes was explored using the STRING database (https://string-db.org/) and is presented in Figure 77.

Exposure of breast and gastric tumors to Rx4 appears to result in statistically-significant increases and decreases in genes that may provide information about the direct or indirect mechanism of action for this molecule for those tumors that do not respond in a clinically meaningful way. M-Score for responders only tumor/arm combinations were used to perform differential gene expression profiling comparing Rxl to Rx2, Rx3 or Rx4 independently using default parameters within nSolver. The analysis included breast and gastric tumor samples. At p-value cutoff of <0.05 was applied. From this analysis, a list of 22 (Rx2 vs Rxl), 40 (Rx3 vs Rxl) and 39 (Rx4 vs Rxl) genes were identified including genes that were up-regulated or down-regulated Figure 78. In examining the overlapping genes between the three lists, the majority were unique to each comparison. However, five genes were shared and unique to the comparison between Rx2 and Rx4, potentially pointing to a shared mechanism of action: CXCL10, CXCL11, CCL8, CCL16, ATG5. Many of these genes shared common function as chemokines/ chemoattractants that can recruit T-cells to the tumor microenvironment. M-Score for non-responders only tumor/arm combinations were used to perform differential gene expression profiling comparing Rxl to Rx2, Rx3 or Rx4 independently using default parameters within nSolver. The analysis included breast and gastric tumor samples. A p-value cutoff of <0.05 was applied. From this analysis, the top 10 up-regulated and down-regulated genes were identified for each comparison Figure 79. In examining the overlapping genes between the three lists, the majority were unique to each comparison. However, four genes were shared between Rx2 and Rx4, potentially pointing to a shared mechanism of action: CXCL9, CXCL10, CXCL11, IDOl.

Tumors/treatment arm combinations for M-Score less than 25 (non-responders) were used to perform differential gene expression profiling comparing Rxl to Rx2 using default parameters within nSolver. Similarly, M-Score > 25 (responders) tumor/arm combinations were used to perform differential gene expression profiling comparing Rxl to Rx2. The analysis included breast and gastric tumor samples. A p-value cutoff of <0.05 was applied resulting in a list of 340 genes in the non responder samples and 22 genes in the responder samples Figure 80. In examining the overlapping genes between the two lists, the majority were shared with three exceptions: ATG5, ATG16L1, PRPF38A.

ATG5 and ATG16L1 are functionally related and have roles in controlling autophagy of T-cells resulting in proliferation of cytotoxic cells and suppression of regulatory species.

This analysis may be useful in identifying biological activity that is unique to tumors that respond to the drug, as a means of uncovering the underlying biology behind the response.

To determine whether Common Genes found amongst the list of differentially expressed genes in M- high (Predicted Responders) only, M-high only tumor/arm combinations were used to perform differential gene expression profiling comparing Rxl to Rx2, Rx3 or Rx4 independently using default parameters within nSolver. The analysis included breast and gastric tumor samples. At p-value cutoff of <0.05 was applied. From this analysis, a list of 22 (Rx2 vs Rxl), 40 (Rx3 vs Rxl) and 39 (Rx4 vs Rxl) genes were identified including genes that were up and down regulated.

In examining the overlapping genes between the three lists, the majority were unique to each comparison. However, five genes were shared and unique to the comparison between Rx2 and Rx4 potentially pointing to shared mechanism of action: CXCL10, CXCL11, CCL8, CCL16, ATG5. Many of these genes shared common function as chemokines/chemoattractants that can recruit T-cells to the tumor microenvironment.

In order to determine whether there are common genes found amongst the list of differentially expressed genes in M-low (Predicted Non-Responders), M-low only tumor/arm combinations were used to perform differential gene expression profiling comparing Rxl to Rx2, Rx3 or Rx4 independently using default parameters within nSolver. The analysis included breast and gastric tumor samples. At p-value cutoff of <0.05 was applied. Finally, the top 10 up and down regulated genes were identified for each comparison. The up group is shown in Figure 81. In examining the overlapping genes between the three lists, the majority were unique to each comparison. However, four genes were shared between Rx2 and Rx4 potentially pointing to a shared mechanism of action: CXCL9, CXCL10, CXCL11, IDOl.

In particular CXCL9, CXCL10, CXCL11 show significant effect for both Rx2 and Rx4 in comparison to Rxl and Rx3, before and after treatment in responders. Each of CXCL9, CXCL10, CXCL11 genes regulated by IFNgamma, appear to be genes predictive of responders to GBR 1302.

Up regulation of LAG3, IDO, CTLA4 could also highlight the expression of potential Immune resistance mechanism genes in response to the Rx2 arm.

With reference to Figure 82, differences in immune signatures with the Rx4 arm compared to control are shown.

Differentially expressed genes greater than log2 >2 fold with a FDR of <0.1 are shown. The Total number of DE expressed genes =25 (770)

CXCL9, CXCL10, CXCL11 genes regulated regulated by IFNgamma, appear to be genes predictive of responders to GBR 1302/PD1 combination.

Potential immune resistance mechanism genes such as LAG3, IDO, CTLA4 are not differentially expressed in the Rx4 arm.

With reference to Figure 83, similarities of immune signatures between GBR 1302 (Rx2) and GBR 1302/PD1 combination (Rx4) arms were analysed. CXCL8, CXCL9, CXCL10, CXCL11 genes regulated by IFNgamma are predictive single agent and combo of responders.

The following genes IDOl, CD28, CD3E, CD38, ICOSLG, IL12B, LILRB1, PTPRC, RPS6, SLC11A1, TNFSF14, TNFSF18, TP53 were evaluated that could steer towards T cell clonality, expansion T effector, T memory.

The following genes evaluated CTLA4, LAG3, IL-10, IDOl, TGFB1, TGFB2, CD160, BTLA that could suppression of immuno suppressive cells. Immuno-suppressive genes seem up-regulated in Rx2 as compared to Rxl-Vehicle Control, but are closer to control levels in the Rx4 arm supporting the hypothesis that the combination treatment shows higher efficacy due to the suppression of Immuno suppressive cells and indicating that combinations with other checkpoint inhibitors such as an anti- CTLA4 antibody would lead to a similar responder group expansion.

To determine for Rx2 only, which differentially expressed genes are unique to M-high (Predicted Responders) vs. M-low (Predicted Non-Responders), M-low only tumor/arm combinations were used to perform differential gene expression profiling comparing Rxl to Rx2 using default parameters within nSolver. Similarly, M-high only tumor/arm combinations were used to perform differential gene expression profiling comparing Rxl to Rx2. The analysis included breast and gastric tumor samples. At p-value cutoff of <0.05 was applied resulting in a list of 340 genes in the m-low samples and 22 genes in the m-high samples. In examining the overlapping genes between the two lists, the majority were shared with three exceptions: ATG5, ATG16L1, PRPF38A, ATG5 and ATG16L1 are functionally related and have roles in controlling autophagy of T-cells resulting in proliferation of cytotoxic cells and suppression of regulatory species.

This analysis may be useful in identifying biological activity that is unique to M-high tumors as a means of uncovering the underlying biology behind response.

In addition differentially expressed genes associated with T cell proliferation in predicted responders in the Rx2 and Rx4 arms are shown in Figure 83.

In addition Immunosuppressive genes in predicted responders in the Rx2 and Rx4 arms are shown in Figure 85. Significant for the RX2 vs. Rxl comparison are CTLA4, LAG3, IDOl and CD274. Significant for the RX4 vs. Rxl comparison is CD274.

This analysis therefore supports the hypothesis that the Rx2/Rx4 have a mechanism of action involving the induction of INF-gamma mechanisms and hence of immunity mechanisms such as MHC- restricted immunity (such as via CD8 or CD4 T cell populations) or via effector cell populations such as NK cells.

It is also the case that GBR 1302 treatment induces IL-10, thereby simultaneously stimulating antitumor immunity and inhibits tumor-associated inflammation.

The observed activation of escape/resistance particularly by induction of negative regulators such as IDOl, CTLA-4 or LAG-3 also potentially allow the preclinical anticipation of such mechanisms and based upon the proof of concept Rx4 arm suggest that such escape/resistance mechanisms can be reverted by means of combination therapy with PD-1 blockade or other complimentary therapy.

Phase 2 Cytokine Data (n=19) Gastric Tumors (n=4) and Metastatic Breast Tumors (n=15) are shown from Figure 86 to Figure 95. Individual Cytokine Data for Phase 2 Gastric (n=4) and Metastatic Breast (n=19) Tumors are shown from Figure 96 to Figure 114.

Phase 3 Cytokine Data (n=10) for Gastric Tumors (n=8) and Metastatic Breast Tumors (n=2) are shown from Figure 115 to Figure 124. Individual Cytokine Data for Phase 3 Gastric (n=8) and Metastatic Breast (n=2) Tumors are shown from Figure 125 to Figure 134. RNA Yield of Metastatic Breast Tumors for Phase 2 and Phase 3 is shown in Figure 135. RNA Yield of Gastric Tumors for Phase 2 and Phase 3 is shown in Figure 136.

Example 4. Summary

Characterization of GBR1302 immune related response in clinically relevant ex-vivo explant culture and response prediction platform: CANscriptTM

CANscript, a clinically-validated patient derived explant culture platform that preserves the fidelity of the tumor microenvironment along with critical immune phenotypes (Majumder B et al, Nature Communications 2015) and was used to characterize the effect of GBR1302 on modulating the immune microenvironment of explant patient dervired tumors. For this study 25 pathologically qualified (based on tumor content) fresh metastatic breast cancer (CaBr) samples were used to generate the M-Score for each drug tested. Baseline HER2 status and analysis of other biomarkers for all or selected (based on HER2 status) samples post culture in the presence or absence of the drugs was established.

The mechanism of action of Herceptin primarily involves cell cycle arrest, the suppression of cell growth and proliferation resulting in targeted cell death of HER2-positive tumor cells (Adv Exp Med Biol. 2003;532:253-68.). In-vitro and in-vivo mouse studies have revealed that GBR1302 mainly causes tumor cell death by Re-Directed Lysis (RDL) where GBR1302 engages the CD3 ,a T cell co-receptor that associates noncovalently with the T cell receptor (TCR), to selectively activate T cells for cytotoxic effector function to target HER2 positive tumor cells, the other antibody binding portion of GBR1302 binds to HER2.

To determine if this mechanism of action of GBR1302 is preserved in the explant culture system, a mechanistic evaluation of multiple aspects of immune modulatory and immune effector functions was made in comparison to the effects of Herceptin in treated explants. The analysis of gene expression of immune-stimulatory and immune-suppressive pathway genes showed that GBR1302 iinduced a broader modulation compared to Herceptin as indicated in the heatmap (Figure 137). Genes linked to T cell activation such as CD3e, CD28, chemokine signalling such as CXCL9, CXCL11 and exhaustion phenotypes CTLA4, LAG3 showed upregulation in GBR1302 treated tumors in ex vivo Figure 137.

The functional manifestation of the immune population due to GBR1302 treatment in ex vivo was studied. For this the explant culture supernatant was collected after 24, 48 and 72 hours of drug treatment and Luminex based evaluation of multiple cytokines was performed. The change in cytokines present in the culture supernatant post treatment of GBR1302 and Herceptin with respect to the control at the 3 time points was evaluated (Figure 138). Significant augmentation in the levels of cytokines, mainly related to T cell functionality, (IFNy, GranzymeB (GNZB), TNF-ot ) along with IL17A, IL2 and IL10 was observed at least in one time point upon GBR1302 treatment (Figure 138). The effect of GBR1302 on eliciting cytolytic activity of CD8 T cells was further confirmed by a significant increase in total CD8 and proliferating (Ki67+CD8) CD8 population in tumors that were treated with GBR1302.).

This was accompanied with an increase of cytokines associated with CD8 cytolytic activity such as IFNy, GranzymeB and Perforin in GBR1302 treated samples in comparison to the control. A concomitant upregulation of IFNy gene expression was also observed. To further elucidate this modulatory effect of GBR1302, DESeq2 analysis (Love, Michael I et al. "Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2." Genome biology vol. 15,12 (2014): 550.) was performed for GBR1302 vs vehicle using nanostring 770 immune gene panel and IPA for the significantly deregulated genes (Figure 139 and Figure 140). The pathway analysis data illustrated the effect of GBR1302 in preferentially upregulating the genes linked to T helper pathways in concert with NK-DC cross talk and granulocyte adhesion. A reciprocal downregulation of p38MAPK, TLR signaling, JNK and NF0B other signaling pathways was observed under this treatment condition (Figure 140).

Biomarker and pharmacodynamic profiling on CANscriptTM platform for single-agent GBR 1302 To delineate the immune population which could be critically affected by GBR1302, we evaluated the change of variance of the various immune gene signatures defining the key immune populations. The change in variance in the GBR1302 arm clearly demonstrated the significant effect the drug on the different immune compartments as most of the signatures have a positive change of variance in comparison to single agent Herceptin (Figure 141). To further understand the relation of the predicted response of GBR1302 (i.e., Mscore>25) with some of these enriched signatures (for e.g., activated CD8, Central Memory CD4, Effector Memory CD8, IDC, TCR and Ml), we plotted a waterfall graph for the 17 patients. Then we divided each gene signature into two cohorts- reduced and induced. For each of the cohorts the percent predicted response was evaluated. For Activated CD8, Central Memory CD4, Effector Memory CD8, IDC, TCR gene signature the percent responders were higher in the induced cohort than reduced cohort while for Ml gene signature the percent responders were more in the reduced cohort (Figure 141). As an activated CD8 gene signature has been found to be directly related to the GBR1302 mechanism of action, we studied a number of key biomarkers and their pharmacodynamics in the induced and reduced cohort generated from the waterfall plot (Figure 142).

Since activation of CD8 is functionally associated with increased proliferation and cytolytic activity, for further understanding the effect of GBR1302 on proliferation, we evaluated the total CD8 and Ki67+CD8 score by IHC in the induced and reduced cohorts. While a significant increase in total CD8 and Ki67+CD8 was observed in the induced cohort, the change was not significant in the reduced cohort (Figure 142).

To understand the effect of functional modulation, we evaluated the change in cytokine released in the culture supernatant with respect to the control for each cohort at 24hrs, 48hrs and 72hrs post treatment. The cytokines associated with activation of T cells such as IFNy, Granzyme B, IL2, IL17A and IL12 all were higher in the induced cohort more uniformly at 72 hrs of drug treatment compared to the reduced cohort (Figure 143 and Figure 144), whilst cytokines like IL10, IL6 were down regulated at 72 hrs (Figure 143 and Figure 144). We further analysed the levels of immune response related polyfunctionality cytokines such as IFNy, IL2 and immune suppressing cytokines IL10 in parallel in the predicted responders and the non-responders.

IFNy related to CD8 activity and Thl response increase with time for the responders while it showed a decrease for the non-responders. IL2 which is a general cytokine released by various immune cells in response to activation also had a similar trend in both responders and non-responders, but its level remained higher in the responders compared to non-responders throughout the treatment (Figure

145). While the level of IL10, related to immune suppression and predominantly released by Th2 and Treg populations, remained unchanged for the responders but showed a comparatively higher level in non-responders (Figure 145). Thus, the variability that exists among patients was preserved in the explant culture platform. At the same time, the data showed that this ex vivo immune modelling can capture the pharmacodynamic profile of GBR1302 treatment. The results also provide critical insight into the underlying mechanisms of resistance which either exists in patients TME context or may be induced due to GBR1302 treatment.

PD-1 checkpoint blockade cooperates with GBR-1302 to improve response by perturbing immuno suppressive phenotypes

T cell activation is controlled by several regulatory mechanisms which counteracts the activation leading in particular to the upregulation of inhibitory pathway proteins. The expression of some of the inhibitory pathway genes (IDOl, CTLA4, LAG3, SOCS1 and PDL1) upon GBR1302 treatment was evaluated and observed a significant increase of these genes in comparison to the control arm (Figure

146). PD-1-PD-L1 interaction plays an important role in the inhibition of T cell activation. GBR1302 didn't elicit any synergistic or additive effect upon the cytotoxicity in combination with PD1 blockade (Figure 147).

In the CANscript platform, when the explant was treated with GBR1302 in combination with anti-PDl, the inhibitory pathway genes expression maintained at the level similar to the control arm (Figure 146). The combination treatment also showed a comparatively better response rate (40% of the 25 patients were responders) than GBR1302 alone (20% response rate). The analysis of activated CD8 gene signature upon treatment of GBR1302+anti-PDl showed a reduction in gene expression in 11/17 samples 72hrs post treatment (Figure 147). To further understand the underlying profile of reduced activated CD8 gene signature in this cohort mechanistically, we performed a deseq2 gene analysis on the predicted responders and the predicted non-responders in the 11 samples of activated CD8 reduced cohort (Figure 148) along with an ingenuity pathway analysis (IPA, Qiagen) of the significantly altered genes (Figure 149).

Pathways that showed upregulation in non-responders includes lymphotoxin, TLR and NF0B signalling. We also observed a downregulation in this set of genes linked to Thl/Th2 activation and granulocyte adhesion. However, in the responders a different perturbation pattern was observed. Thl7 in inflammation and Thl7 signalling are two major pathways that displayed upregulation and T helper differential and allograft rejection pathway genes showed negative expression (Figure 149). The increase in IL17a was also observed in culture supernatant of GBR1302+anti-PDl in comparison to the control samples (Figure 150). This increase was also prevalent in the GBR1302 treated samples (*figure 151). In humans, two main population are identified to secrete IL17: Thl7 and Effector Memory CD8 T cells (J Immunol 2008; 180:7948-7957, Sci Transl Med. 2011 Oct 12;3(104):104ral00, Immunol Lett. 2016 Oct; 178: 20-26). We compared the Thl7 gene signature and CD8 effector memory gene signature between GBR1302 and GBR1302+anti-PDl. We observed that the change in Thl7 gene signature with respect to control was positive for both GBR1302 (Mean= 0.4305) and the combination (Mean=0.1236). In contrast, the change in CD8 effector memory T in GBR1302 treatment (Mean= - 0.2559) was negative and the combination (Mean= 0.1055) was positive (Figure 150). The z-score generated from the respective means also showed a similar trend. As the IL17 signalling pathway was detected in the responder cohort, we further analysed the CD8 effector memory gene signature in the responders of the combination which were predicted as non-responders for the single agent GBR1302. We observed that the CD8 effector memory gene signature was upregulated in the combination arm whereas it was down regulated in GBR1302 single agent arm. This indicates that the CD8-effector memory was more stably preserved in the combination arm. Together, these data indicate that the explant culture model, where the patient native tumor microenvironment is contextually preserved, recapitulate the T-cell exhaustion due to activation of T cells by GBR1302 and administration of GBR1302 in combination with anti-PDl can overcome this suppressive phenotype. The combination also generated a better response along with better retention of CD8 effector memory suggesting the potential to contribute toward long-term survival and reduced frequency of tumor relapse.

Claims

1. A T cell redirecting antibody and second immuno-oncology agent for use in treating a disease.

2. A T cell redirecting antibody and second immuno-oncology agent for treating cancer.

3. A T cell redirecting antibody and an anti-PDl antibody for treating cancer.

4. A T cell redirecting antibody selected from the group comprising GBR 1302, GBR 1342, GBR 1372 and an anti-PDl antibody for treating cancer.

5. A T cell redirecting antibody and an anti-PDl antibody according to claim 4 wherein said anti-PDl antibody is Pembrolizumab.

6. A T cell redirecting antibody and second immuno-oncology agent for treating cancer according to anyone of claims 2, 3 or 5 wherein said cancer is characterised by the overexpression of HER2 and in particular selected from the group breast, ovarian, bladder, salivary gland, endometrial, pancreatic and non-small-cell lung cancer (NSCLC).