Fucosylated PD-1 variants for determining an immuno-oncology response

Abstract

A method of classifying a biological sample from a subject with respect to responsiveness to immune checkpoint inhibitor therapy comprising analyzing an amount or absence of a fucosylated PD-1 variant from the biological sample from the subject and generating a diagnosis output based on the amount or absence of the fucosylated PD-1 variant which can be used to classify whether the subject is likely to benefit or not likely to benefit from the immune checkpoint inhibitor therapy.

Description

FUCOSYLATED PD-1 VARIANTS FOR DETERMINING AN IMMUNO¬ONCOLOGY RESPONSECROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the priority benefit of U.S. Provisional Patent Application Serial No. 63 / 515,544, filed July 25, 2023 and U.S. Provisional Patent Application Serial No. 63 / 518,064, filed August 7, 2023, the disclosures of each of which are hereby incorporated by reference herein in their entireties.FIELD

[0002] The present disclosure generally relates to methods and systems for analyzing an amount or absence of a fucosylated PD-1 variant from a biological sample from a subject. More particularly, the present disclosure relates to generating a diagnosis output based on the amount or absence of the fucosylated PD-1 variant that classifies whether the subject is likely to benefit or not likely to benefit from anti -PD-1 antibody therapy or immune checkpoint inhibitor therapy.BACKGROUND

[0003] PD-1 is a type-I transmembrane glycoprotein expressed in immune cells, predominantly in T cells, that functions as a checkpoint: engagement of PD-1 by its ligands PD-L1 and PD-L2 suppresses T cell receptor (TCR)-driven cell activation and induces apoptosis. This mechanism can be detrimental in the tumor microenvironment, where a combination of exhausted T cells displaying PD-1 and PD-L1 expression by cancer and myeloid cells cause ineffective antitumor immune responses. Consequently, monoclonal antibodies that target PD-1 or its ligands and prevent their interaction have been developed as anticancer therapeutics. Whereas immune checkpoint inhibitors have revolutionized cancer treatment across many tumor indications and demonstrated the usefulness of interjecting the PD-1 :PD-L1 axis, only a fraction of patients benefit from these drugs and develop durable clinical responses. This suggests that critical information about the PD-1 pathway and the mechanisms of signaling blockade may still be unavailable. Moreover, the intrinsic properties of anti -PD-1 antibodies might be related to differences in efficacy or safety observed in the clinical settings.

[0004] PD-1 and PD-L1 are regulated both at the transcriptional and post-translational level. PD-1 is rapidly induced upon TCR-mediated activation, decreases with antigen clearance, but it’s maintained on antigen-specific T cells in chronic settings as cancer. PD-1 stability at the cell surface is controlled by ubiquitination that leads to protein degradation and by glycosylation that may occur at four asparagine sites (N49, N58, N74 and N119). CRISPR-based screening identified Fut8, which encodes for a fucosyltransferase that adds alphal,6 core fucose to glycans, as a mechanism that regulates cell surface expression of PD- 1 by modification of glycans at sites N49 and N7411. In line with this observation, T cells exposed to a fucose inhibitor produce stronger antitumor responses. Similarly, PD-L1 expression and stability are regulated at the post-translational level by glycosylation and ubiquitination. PD-L1 glycosylation is also critical for interaction with PD-1; in contrast, PD- 1 glycosylation appears to be dispensable for binding to PD-L1 as its glycosylation sites are distant from the ligand binding interface. Interestingly, some antibodies, including cemiplimab and camrelizumab, interact with a region surrounding the N58 glycosylation site and require the N58 glycan for efficient binding. This property is not shared by other antibodies, such as nivolumab and pembrolizumab, that have partly overlapping binding epitopes interacting with the flexible N- and C’D-loops of the PD-1 molecule, respectively.

[0005] In addition to the transmembrane forms, soluble PD-1 and PD-L1 variants (sPD-1 and sPD-Ll) can be generated by protease-based cleavage and accumulate in blood. While the function of the released protein variants is not fully established, it has been reported that sPD-Ll retains its inhibitory capacity and elevated sPD-Ll levels have been associated with advanced disease and worse prognosis. sPD-1 without glycosylation status has not been found to be informative as a prognostic or predictive biomarker. Notably, the glycosylation status of sPD-1 or sPD-Ll has not been determined.

[0006] Here, we observed that camrelizumab and cemiplimab specifically interact with a fucose moiety within the N58 glycan of PD-1. These data illustrate a path towards the development of personalized immunotherapeutic approaches for cancer treatment and highlight the potential of glycosylation analysis of targets to guide the development of differentiated therapeutics.SUMMARY

[0007] In embodiments, the disclosure provides a method of classifying a biological sample from a subject with respect to a responsiveness to immune checkpoint inhibitor therapy, the method comprising: analyzing an amount or absence of a fucosylated PD-1variant from the biological sample from the subject; and generating a diagnosis output based on the amount or absence of the fucosylated PD-1 variant that classifies whether the subject is likely to benefit or not likely to benefit from the immune checkpoint inhibitor therapy.

[0008] In various embodiments, the analyzing an amount or absence of the fucosylated PD-1 variant comprises receiving an abundance value from a mass spectrometer.

[0009] In various embodiments, the mass spectrometer is configured to perform multiple reaction monitoring mass spectrometry (MRM-MS).

[0010] In various embodiments, the mass spectrometer is configured to perform a shotgun analysis.

[0011] In various embodiments, the analyzing an amount or absence of the fucosylated PD-1 variant comprises: receiving an optical signal value from a plate reader or a flow cytometer.

[0012] In various embodiments, the analyzing an amount or absence of the fucosylated PD-1 variant comprises incubating a coated surface with the biological sample from the subject, wherein the coated surface, before the incubation, comprises a layer of an immune checkpoint inhibitor for the immune checkpoint inhibitor therapy, wherein the immune checkpoint inhibitor is configured to bind to a fucosylated PD-1 variant. When the biological sample contains the fucosylated PD-1 variant, it binds to the coated surface to form a bound fucosylated PD-1 variant. An optical signal is measured corresponding to the amount or absence of the fucosylated PD-1 variant in the sample.

[0013] In various embodiments, after the fucosylated PD-1 variant is bound to the coated surface and before measuring the optical signal, the method further comprises adding a PD-1 detection antibody to the coated surface, wherein the PD-1 detection antibody is configured to bind to the bound fucosylated PD-1 variant, and wherein the PD-1 detection antibody is coupled to a peroxidase. The PD-1 detection antibody is bound to the fucosylated PD-1 variant to form a bound fucosylated PD-1 variant / PD-1 detection antibody complex. A peroxidase substrate and a dye are added onto the coated surface. The dye is oxidized to form an oxidized dye, wherein the amount of the oxidized dye is proportional to the amount or absence of the fucosylated PD-1 variant and the amount of the oxidized dye is measured with an optical signal.

[0014] In various embodiments, the immune checkpoint inhibitor therapy includes an immune checkpoint inhibitor selected from the group consisting of camrelizumab, cemiplimab, and a combination thereof.

[0015] In variousembodiments, the fucosylated PD-1 variant comprises a fucosylated glycan at position N58 of a PD-1 protein.

[0016] In various embodiments, the fucosylated PD-1 variant comprises one or more glycan compositions selected from the group consisting of H4N4S1F1, H3N4F1, H3N5F1, H4N5S1F1, H5N4S1F1, H4N4F1, and combinations thereof

[0017] In various embodiments, the fucosylated glycan includes a core fucose.

[0018] In various embodiments, the immune checkpoint inhibitor is configured to bind to the fucosylated PD-1 variant with a higher affinity than a non-fucosylated PD-1 variant.

[0019] In various embodiments the disclosure provides, a method of treating a subject includes analyzing an amount or absence of a fucosylated PD-1 variant from a biological sample from the subject; generating a diagnosis output based on the amount or absence of the fucosylated PD-1 variant that classifies whether the subject is likely to benefit or not likely to benefit from the immune checkpoint inhibitor therapy, and administering to the subject a therapeutically effective amount of an immune checkpoint inhibitor, wherein the immune checkpoint inhibitor is configured to bind to the fucosylated PD-1 variant with a higher affinity than a non-fucosylated PD-1 variant.

[0020] In various embodiments, the analyzing an amount or absence of the fucosylated PD-1 variant includes receiving an abundance value from a mass spectrometer.

[0021] In various embodiments, the mass spectrometer is configured to perform multiple reaction monitoring mass spectrometry (MRM-MS).

[0022] In various embodiments, the mass spectrometer is configured to perform a shotgun analysis.

[0023] In various embodiments, the analyzing an amount or absence of the fucosylated PD-1 variant comprises: receiving an optical signal value from a plate reader or a flow cytometer.

[0024] In various embodiments, the analyzing an amount or absence of the fucosylated PD-1 variant includes incubating a coated surface with the biological sample from the subject, wherein the coated surface, before the incubation, comprises a layer of an immune checkpoint inhibitor for the immune checkpoint inhibitor therapy, wherein the immune checkpoint inhibitor is configured to bind a fucosylated PD-1 variant. When the biological sample contains the fucosylated PD-1 variant, it binds to the coated surface to form a bound fucosylated PD-1 variant. An optical signal is measured corresponding to the amount or absence of the fucosylated PD-1 variant in the sample.

[0025] In various embodiments, after the fucosylated PD-1 variant is bound to the coated surface and before measuring the optical signal, the method further comprises adding a PD-1 detection antibody to the coated surface, wherein the PD-1 detection antibody is configured to bind to the bound fucosylated PD-1 variant, and wherein the PD-1 detection antibody is coupled to a peroxidase. The PD-1 detection antibody is bound to the bound fucosylated PD- 1 variant to form a bound fucosylated PD-1 variant / PD-1 detection antibody complex. A peroxidase substrate and a dye is added to the coated surface. The dye is oxidized to form an oxidized dye, wherein the amount of the oxidized dye is proportional to the amount or absence of the fucosylated PD-1 variant and the amount of the oxidized dye is measured with an optical signal.

[0026] In various embodiments, the immune checkpoint inhibitor therapy comprises an immune checkpoint inhibitor selected from the group consisting of camrelizumab, cemiplimab, and a combination thereof.

[0027] In various embodiments, the fucosylated PD-1 variant comprises a fucosylated glycan at position N58 of a PD-1 protein.

[0028] In various embodiments, the fucosylated PD-1 variant comprises one or more glycan compositions selected from the group consisting of H4N4S1F1, H3N4F1, H3N5F1, H4N5S1F1, H5N4S1F1, H4N4F1, and combinations thereof.

[0029] In various embodiments, the fucosylated glycan includes a core fucose.

[0030] In various embodiments the disclosure provides, a method of screening a drug candidate for potential use in a subject comprising incubating a coated surface with a biological sample from the subject, wherein the coated surface, before the incubation, comprises an adsorbed layer of the drug candidate. When the biological sample contains a fucosylated PD-1 variant, a fucosylated PD-1 variant is bound to the coated surface. An optical signal is measured corresponding to the amount or absence of the fucosylated PD-1 variant in the sample and classifying whether the subject is likely to benefit or not likely to benefit from the drug candidate based on the optical signal.

[0031] In various embodiments, after the fucosylated PD-1 variant is bound to the coated surface and before measuring the optical signal, the method further compries adding a PD-1 detection antibody to the coated surface, wherein the PD-1 detection antibody is configured to bind to the bound fucosylated PD-1 variant, and wherein the PD-1 detection antibody is coupled to a peroxidase. The PD-1 detection antibody is bound to the bound fucosylated PD- 1 variant to form a bound fucosylated PD-1 variant / PD-1 detection antibody complex. A peroxidase substrate and a dye is added to the coated surface. The dye is oxidized to form anoxidized dye, wherein the amount of the oxidized dye is proportional to the amount or absence of the fucosylated PD-1 variant and the amount of the oxidized dye is measured with the optical signal.

[0032] In variousemboiments, the drug candidate comprises an anti -PD-1 antibody.

[0033] In various embodiments, the fucosylated PD-1 variant comprises a fucosylated glycan at position N58 of a PD-1 protein.

[0034] In various embodiments, the fucosylated PD-1 variant comprises one or more glycan compositions selected from the group consisting of H4N4S1F1, H3N4F1, H3N5F1, H4N5S1F1, H5N4S1F1, H4N4F1, and combinations thereof.

[0035] In various embodiments, the fucosylated glycan comprises a core fucose.

[0036] In various embodiments, the drug candidate is configured to bind to the fucosylated PD-1 variant with a higher affinity than a non-fucosylated PD-1 variant.

[0037] In various embodiments, the subject has lung cancer.

[0038] In various embodiments, the mass spectrometer configured to perform a shotgun analysis is an ion trap mass analyzer or a qTOF mass spectrometer,

[0039] In various embodiments, the ion trap mass analyzer comprises an outer barrel-like electrode and a coaxial inner spindle-like electrode where the ion trap mass analyzer configured to trap ions in an orbital motion around the spindle.

[0040] In various embodiments, the fucosylated PD-1 variant is coupled to the surface of a cell.

[0041] In various embodiments, the fucosylated PD-1 variant is added to the plasma or serum portion of a sample.

[0042] In various embodiments, the fucosylated PD-1 variant is not attached to a cell.BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The present disclosure is described in conjunction with the appended figures:

[0044] Figure 1 is a schematic diagram of an exemplary workflow 100 for the detection of peptide structures associated with a disease state for use in diagnosis and / or treatment in accordance with one or more embodiments.

[0045] Figure 2A is a schematic diagram of a sample preparation workflow in accordance with one or more embodiments.

[0046] Figure 2B is a schematic diagram of data acquisition in accordance with one or more embodiments.

[0047] Figure 3 is a block diagram of an analysis system in accordance with one or more embodiments.

[0048] Figure 4 is a block diagram of a computer system in accordance with various embodiments.[00491 Figure 5A to Figure 5C illustrate the binding interface between PD-1 and the anti-PD-1 antibodies camrelizumab and cemiplimab that includes a core fucose at the PD-1 N58 glycan. Figure 5A shows the structure of PD-1 with the glycan linked to the asparagine N58 of the BC loop of PD-1. The glycan visible in the crystal structure (PDB 7CU5) includes a fucose, two N-acetyl-glucosamine units and two mannose units. The glycan structure is also represented with geometric symbols as per convention (triangle: fucose; square: N- acetyl -glucosamine; circle: mannose). Figure 5B shows the structure of PD-1 in complex with camrelizumab (PDB 7CU5) indicating potential interactions between the core fucose of PD-1 N58 glycan and the heavy chain of the antibody (residues S30 and S31 of CRD1 and S52, G53 and G54 of CRD2). The VH chain and VL chain are indicated. Figure 5C shows the structure of cemiplimab in complex with PD-1 (PDB 7WVM). The N58 residue of PD-1 is represented as sticks. Cemiplimab binds to PD-1 in a similar orientation as camrelizumab, suggesting that N58 glycan structure may influence affinity.

[0050] Figure 6 A to Figure 6E illustrate that the binding of camrelizumab and cemiplimab to PD-1 depends on the core fucose of the N58 glycan of PD-1. Figure 6A shows the schematic diagram of the PD-1 proteins indicating signal peptide, extracellular domain, transmembrane domain and intracellular region, as well as the four N-linked glycosylation sites. Figure 6B shows the relative abundance of the glycoforms found at the N58 site of recombinant PD-1 proteins. Glycan structures are comparable and differ only by the fucose moiety (triangle). Glycan composition is indicated at the bottom of the graph (H: hexose; N: N-acetyl-hexosamine; S: sialic acid; F: fucose) and represented with symbols at the top. The glycan structure is also represented with geometric symbols as convention (triangle: fucose; square: N-acetyl-glucosamine; dark circle: mannose; light circles: galactose). Figure 6C shows the representative binding profiles of anti-PD-1 antibodies to recombinant PD-1 proteins. The dotted line marks the separation between the association and dissociation cycle. Figure 6D shows the binding of anti-PD-1 antibodies to CHO-K1 cells transfected with full length PD-1 constructs. Lack of fucosyltransferase FUT8 expression that adds core fucose does not alter the binding profile of pembrolizumab but results in reduced binding by cemiplimab and camrelizumab. Figure 6E (6E1 to 6E4) show charts that indicate binding of anti-PD-1 antibodies to human CD8 T cells. Cells were activated with CD3 / CD28beads in the presence or absence of fucose inhibitor and tested for expression of PD-1, fucose or CD25. Detection of PD-1 by camrelizumab depends on the fucose content of the PD-1, whereas neither pembrolizumab nor the anti-CD25 antibodies are affected by changes in fucosylation.

[0051] Figure 7A to Figure 7C show that PD-1 fucosylation impacts the ability of camrelizumab and cemiplimab to block PD-1 :PD-L1 interactions. Figure 7A illustrates the blockade of PD-L1 binding to PD-1 expressed in CHO-K1 or fucosyltransf erase 8-deficient cells by camrelizumab, cemiplimab or pembrolizumab. PD-L1 Binding was assessed by flow cytometry. Three independent assays were conducted, with each assay performed in duplicate as technical replicates. A representative figure depicting the results is provided. Interpolation was calculated using a 4-parameter logistic function. Figure 7B illustrates the blockade of PD-L1 binding to recombinant by anti -PD-1 antibodies was assessed by a plate-based assay. Three independent assays were conducted, with each assay performed in duplicate as technical replicates. A representative figure depicting the results is provided. Figure 7C shows that a reduction in fucosylation alters the ability of camrelizumab and cemiplimab to modulate PD-1 signaling. A schematic diagram of the PD-1 / PD-L1 cell-based assay, which relies on a reporter T cell line engineered to express PD-1, TCR and CD28, and an antigen- presenting cell line (APC) expressing PD-L1, antigen-MHC complex and CD80. Luciferase activity is used as an indicator of PD-1 signaling activity, which is blocked by PD-L1 on the APC. Luminescence was measured and represented as fold increased over non-induced cells. Three independent assays were conducted, with each assay performed in duplicate as technical replicates. A representative figure depicting the results is provided.

[0052] Figure 8A to Figure 8B show that the fucosylation of serum PD-1 of NSCLC patients varies in different disease stages. Figure 8A shows that the concentration of sPD-1 does not significantly change during progression of disease in this cohort of NSCLC patients. Figure 8B shows that the ratio of fucosylated sPD-1 to total sPD-1 is higher in serum for late stage lung cancer patients. The fucosylated sPD-1 fraction was calculated as the ratio of fucosylated sPD-1 divided by total sPD-1 concentration.

[0053] Figure 9A to Figure 9B illustrate the relevance of PD-1 N58 in the PD-L1 binding mechanism and the epitopes of the PD-1 antibodies. Figure 9A shows the binding surface of PD-L1 on PD-1 (PDB 4ZQK) is highlighted in green. Figure 9B shows the epitopes of PD-1 antibodies (Camrelizumab: PDB 7CU5; Cemiplimab: 7WVM; Nivolumab: 5WT9; Pembrolizumab: 5B8C) are highlighted in color. The N58 residue is located within area depicted as a circle.

[0054] Figure 10 shows that the recombinant expression of PD-1 in presence of fucose inhibitors does not alter overall glycan composition of recombinant PD-1 variant at the three sites N49, N58 and N74. Glycans are grouped as truncated, oligo-mannose, complex-hybrid and those not found in GlycoTouCan database (Modified).

[0055] Figure 11 includes two charts that show the recombinant expression of PD-1 in the presence of fucose inhibitors results in a drastic reduction of fucose content.

[0056] Figure 12 is a chart indicating that mass spectrometry analysis of the glycan structures of recombinant PD-1 variants. The 10 most abundant structures, accounting for >90% coverage, are represented for both PD-1 and PD-1 NF proteins. Glycan composition is indicated with the convention “HNFS”, which represents the number of N-acetyl-hexosamine (H), hexose (N), sialic acid (S) and fucose (F). The most abundant glycan species contained in the two proteins have comparable compositions that differ only for one fucose unit. Related glycan species are connected by lines.

[0057] Figure 13 is a representative sensogram for determination of the binding kinetics of anti-PD-1 antibodies. The sequential steps represent: 1. Baseline in assay buffer; 2. Baseline in PD-1 analyte to detect potential nonspecific interactions; 3. Baseline in assay buffer; 4. Antibody capture; 5. Baseline in assay buffer; 6. Association with PD-1 protein in different concentrations (A-G); and 7. Dissociation in assay buffer. A3=200 nM PD-1, B3 = 66.7 nM PD-1, C3=22.2 nM PD-1, D3=7.4 nM PD-1, E3=2.46 nM PD-1, F3=0.8 nM PD- l,G3=0.27 nM PD-1, and H3=0 nM PD-1 (buffer).

[0058] Figure 14 is a chart indicating that core fucose on the PD-1 N-glycans does not modulate binding to PD-L1. Flow cytometry analysis of CHO-K1 cells (triangles) or CHO- K1 Fut8 KO cells (circles) transfected with full length PD-1 and stained with PD-Ll-Fc protein. The graphs indicate average values of n=2.

[0059] Figure 15A and Figure 15B show charts indicating that fucose inhibitors efficiently reduce cell surface fucosylation and do not impact cell viability. Figure 15A shows flow cytometry analysis of Jurkat cells treated with 2F-Peracetyl-Fucose in concentrations ranging from 0 pM to 600pM. Fucose content was determined by staining with AAL or LCA lectin. Figure 15B shows flow cytometry charts indicating that cells treated with 0 pM, 50 pM, 100 pM, 300 pM, 600 pM of 2F-Peracetyl-Fucose (from left to right) were stained with Zombie red viability dye to determine the percentage of the viable cells.

[0060] Figure 16A to Figure 16D show charts of PD-1 concentration and measured absorbance values for quantification of distinct PD-1 variants by different plate-based assays.Combination of antibodies for coating and detection allows the analysis of total or non- fucosylated PD-1. Figure 16A shows that Human PD-1 Quantikine ELISA preferentially detects PD-1 with core fucose. Figure 16B shows that Human PD-1 DuoSet ELISA uses polyclonal antibodies and detects PD-1 independently from glycosylation content. Figure 16C shows that a coating of camrelizumab preferentially detects PD-1 with core fucose similar to Figure 16A. Figure 16D shows that a coating of pembrolizumab preferentially detects PD-1 independently from glycosylation content similar to Figure 16B. The term PD- 1 STD represents the PD-1 reagent provided with the Human PD-1 Quantikine ELISA test kit, rPD-1 represents a recombinant PD-1, rPD-1 NF represents recombinant PD-1 that is not fucosylated, rPD-1 N58Q represents a recombinant mutant protein where asparagine was replaced with glutamine at position 58.DETAILED DESCRIPTIONI. Overview

[0061] The embodiments described herein recognize that glycoproteomics is an emerging field that can be used in the overall diagnosis and / or treatment of subjects with various types of diseases. Glycoproteomics aims to determine the positions, identities, and quantities of glycans and glycosylated proteins in a given sample (e.g., blood sample, serum sample, cell, tissue, etc.). Protein glycosylation is one of the most common and most complex forms of post-translational protein modification, and can affect protein structure, conformation, and function. For example, glycoproteins may play crucial roles in important biological processes such as cell signaling, host-pathogen interactions, and immune response and disease. Glycoproteins may therefore be important to diagnosing different types of diseases.

[0062] Although protein glycosylation provides useful information about cancer and other diseases, analysis of protein glycosylation may be difficult as the glycan typically cannot be traced back to the protein site of origin with currently available methodologies. Glycoprotein analysis can be challenging in general due to several reasons. For example, a single glycan composition in a peptide may contain a large number of isomeric structures because of different glycosidic linkages, branching, and many monosaccharides having the same mass. Further, the presence of multiple glycans that share the same peptide sequence may cause the mass spectrometry (MS) signal to split into various glycoforms, lowering their individual abundances compared to the peptides that are not glycosylated (aglycosylated peptides).

[0063] However, to understand various disease conditions and to diagnose and prognose certain diseases, such as lung cancer (e.g., non-small cell lung cancer), more accurately, it may be important to perform analysis of glycoproteins and to identify not only the glycan but also the linking site (e.g., the amino acid residue of attachment) within the protein. Thus, there is a need to provide a method for site-specific glycoprotein analysis to obtain detailed information about protein glycosylation patterns that may be able to provide information about a disease state (e.g., a lung cancer disease state). This information can be used to distinguish the disease state from other states, diagnose a subject as having or not having the disease state, determine a likelihood that a subject has the disease state, determine the responsiveness of a disease to a particular treatment, or a combination thereof. For example, such analysis may be useful in diagnosing a lung cancer disease state for a subject (e.g., a negative diagnosis for the lung cancer disease state, a positive diagnosis for the lung cancer disease state). Sample collection and analysis can be collected at different time points for comparing lung cancer disease states over time for a subject. For example, the negative diagnosis may include a healthy state. An example of the positive diagnosis includes the subject suffering from lung cancer. A diagnosis can also assess a malignancy status of a previously identified lung cancer. Further, a prognosis can assess whether a lung cancer is or is not responsive to (or likely to be responsive to) a particular therapy such as immunotherapy (e.g., immune checkpoint inhibitors such as Cemiplimab and / or Camrelizumab).Cemiplimab can be used to treat cutaneous squamous cell carcinoma, basal cell carcinoma, or non-small cell lung cancer. Camrelizumab can be used to treat relapsed or refractory classical Hodgkin lymphoma, B cell lymphoma, oesophageal squamous cell carcinoma, gastric / gastroesophageal junction cancer, hepatocellular carcinoma, nasopharyngeal cancer, and non-squamous, non-small cell lung cancer.

[0064] Accordingly, the embodiments described herein provide various methods and systems for analyzing proteins in subjects and, in particular, glycoproteins. In one or more embodiments, one or more machine learning models are trained to analyze peptide structure data, monomer weight data, or a combination thereof and generate a disease indicator that provides information relating to one or more diseases. For example, in various embodiments, the peptide structure data comprises quantification metrics (e.g., abundance or concentration data) for peptide structures. A peptide structure may be defined by an aglycosylated peptide sequence (e.g., a peptide or peptide fragment of a larger parent protein) or a glycosylated peptide sequence. A glycosylated peptide sequence (also referred to as a glycopeptide structure) may be a peptide sequence having a glycan structure that is attached to a linkingsite (e.g., an amino acid residue) of the peptide sequence, which may occur via, for example, a particular atom of the amino acid residue). Non-limiting examples of glycosylated peptides include N-linked glycopeptides and O-linked glycopeptides. In some aspects, the monomer weight data comprises one or more monomer weight scores for one or more linker sites. One or more monomer weight scores may be used to generate a disease indicator.

[0065] The embodiments described herein recognize that the abundance of one or more monomer type at one or more particular linker sites may be used to determine the likelihood of that subject evidencing a lung cancer disease state. Certain peptide structures and / or monomer weights that are associated with a lung cancer disease state may be more relevant to that disease state than other peptide structures that are also associated with that disease state.

[0066] Analyzing the abundance of peptide structures and glycosylated peptide structures in a biological sample, along with the monomer weights obtained from analysis of such peptide structures, may provide a more accurate way in which to distinguish a positive lung cancer disease state (e.g., a state including the presence of lung cancer) from a negative lung cancer disease state (e.g., healthy state, an absence of lung cancer, etc. . Additionally or alternatively, the disclosed methods may provide a more accurate way in which to predict the responsiveness of a lung cancer to immunotherapy (or other) treatment. This type of analysis may be more conducive to generating accurate diagnoses and / or prognoses as compared to glycoprotein analysis that focuses on analyzing glycoproteins that are too large to be resolved via mass spectrometry. Further, with glycoproteins, there may be too many potential proteoforms to consider. Still further, analysis of peptide structure data in the manner described by the various embodiments herein may be more conducive to generating accurate diagnoses as compared to glycomic analysis that provides little to no information about what proteins and to which amino acid residue sites various glycan structures attach.

[0067] Further, the methods, systems, and compositions provided by the embodiments described herein may enable an earlier, more accurate and / or less invasive diagnosis of lung cancer in a subject as compared to currently available diagnostic modalities (e.g., biopsies, imaging, biochemical tests) used for determining whether immunotherapy (e.g., immune checkpoint inhibitors such as Cemiplimab and / or Camrelizumab) is indicated.

[0068] The description below provides exemplary implementations of the methods and systems described herein for analysis of peptide structures and for research, diagnosis, and / or treatment of lung cancer. Various examples implement the methods and systems described herein as a screening tool. Descriptions and examples of various terms, as used herein, are provided in Section II below.IL Exemplary Descriptions of Terms

[0069] As used herein the specification, “a” or “an” may mean one or more. As used herein in the claim(s), when used in conjunction with the word “comprising,” the words “a” or “an” may mean one or more than one. Some embodiments of the disclosure may consist of or consist essentially of one or more elements, method steps, and / or methods of the disclosure. It is contemplated that any method or composition described herein can be implemented with respect to any other method or composition described herein and that different embodiments may be combined.The use of the term “or” in the claims is used to mean “and / or” unless explicitly indicated to refer to alternatives only or the alternatives are mutually exclusive, although the disclosure supports a definition that refers to only alternatives and “and / or.” For example, “x, y, and / or z” can refer to “x” alone, “y” alone, “z” alone, “x, y, and z,” “(x and y) or z,” “x or (y and z),” or “x or y or z.” It is specifically contemplated that x, y, or z may be specifically excluded from an embodiment. As used herein “another” may mean at least a second or more.

[0071] The term “ones” means more than one.

[0072] As used herein, the term “plurality” may be 2, 3, 4, 5, 6, 7, 8, 9, 10, or more.[00731 As used herein, the term “set of’ means one or more. For example, a set of items includes one or more items.

[0074] As used herein, the phrase “at least one of,” when used with a list of items, means different combinations of one or more of the listed items may be used and only one of the items in the list may be needed. The item may be a particular object, thing, step, operation, process, or category. In other words, “at least one of’ means any combination of items or number of items may be used from the list, but not all of the items in the list may be required. For example, without limitation, “at least one of item A, item B, or item C” means item A; item A and item B; item B; item A, item B, and item C; item B and item C; or item A and C. In some cases, “at least one of item A, item B, or item C” means, but is not limited to, two of item A, one of item B, and ten of item C; four of item B and seven of item C; or some other suitable combination.

[0075] As used herein, “substantially” means sufficient to work for the intended purpose. The term “substantially” thus allows for minor, insignificant variations from an absolute or perfect state, dimension, measurement, result, or the like such as would be expected by a person of ordinary skill in the field but that do not appreciably affect overall performance.

[0076] Throughout this specification, unless the context requires otherwise, the words “comprise”, “comprises” and “comprising” will be understood to imply the inclusion of a stated step or element or group of steps or elements but not the exclusion of any other step or element or group of steps or elements. By “consisting of’ is meant including, and limited to, whatever follows the phrase “consisting of.” Thus, the phrase “consisting of’ indicates that the listed elements are required or mandatory, and that no other elements may be present. By “consisting essentially of’ is meant including any elements listed after the phrase, and limited to other elements that do not interfere with or contribute to the activity or action specified in the disclosure for the listed elements. Thus, the phrase “consisting essentially of’ indicates that the listed elements are required or mandatory, but that no other elements are optional and may or may not be present depending upon whether or not they affect the activity or action of the listed elements.

[0077] Reference throughout this specification to “one embodiment,” “an embodiment,” “a particular embodiment,” “a related embodiment,” “a certain embodiment,” “an additional embodiment,” or “a further embodiment” or combinations thereof means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of the foregoing phrases in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in various embodiments.

[0078] “Treating” or treatment of a disease or condition refers to executing a protocol, which may include administering one or more drugs to an individual, such as a patient, in an effort to alleviate signs or symptoms of the disease. Desirable effects of treatment include decreasing the rate of disease progression, ameliorating or palliating the disease state, and remission or improved prognosis. Alleviation can occur prior to signs or symptoms of the disease or condition appearing, as well as after their appearance. Thus, “treating” or “treatment” may include “preventing” or “prevention” of disease or undesirable condition. In addition, “treating” or “treatment” does not require complete alleviation of signs or symptoms, does not require a cure, and specifically includes protocols that have only a marginal effect on the patient.

[0079] The term “therapeutically effective” as used throughout this application refers to anything that promotes or enhances the well-being of the subject with respect to the medical treatment of this condition. This includes, but is not limited to, a reduction in the frequency or severity of one or more signs or symptoms of a disease, including melanoma.

[0080] The term “lung cancer disease state” as used herein refers to the presence in an individual of lung cancer of any type and of any stage.

[0081] The term “early stage” as used herein refers to stage 0, stage 1, or stage 2 cancer, such as defined by the American Joint Committee on Cancer (AJCC) TNM system and based on the size of the tumor, whether or not it has spread to nearby lymph nodes, and whether or not it has spread to distant sites.

[0082] The term “late stage” as used herein refers to stage 3 or stage 4 cancer, such as defined by the American Joint Committee on Cancer (AJCC) TNM system and based on the size of the tumor, whether or not it has spread to nearby lymph nodes, and whether or not it has spread to distant sites.

[0083] The term “amino acid,” as used herein, generally refers to any organic compound that includes an amino group (e.g., -NH2), a carboxyl group (-COOH), and a side chain group (R) which varies based on a specific amino acid. Amino acids can be linked using peptide bonds.

[0084] The term “alkylation,” as used herein, generally refers to the transfer of an alkyl group from one molecule to another. In various embodiments, alkylation is used to react with reduced cysteines to prevent the re-formation of disulfide bonds after reduction has been performed.

[0085] The term “linking site” or “glycosylation site” (or, in some cases, simply “site”) as used herein generally refers to the location where a sugar molecule of a glycan or glycan structure is directly bound (e.g., covalently bound) to an amino acid of a peptide, a polypeptide, or a protein. For example, the linking site may be an amino acid residue and a glycan structure may be linked via an atom of the amino acid residue. Non-limiting examples of types of glycosylation can include N-linked glycosylation, O-linked glycosylation, C-linked glycosylation, S-linked glycosylation, and glycation.

[0086] The terms “biological sample,” “biological specimen,” or “biospecimen” as used herein, generally refers to a specimen taken by sampling so as to be representative of the source of the specimen, typically, from a subject. A biological sample can be representative of an organism as a whole, specific tissue, cell type, or category or sub-category of interest. Biological samples may include, but are not limited to stool, synovial fluid, whole blood, blood serum, blood plasma, urine, sputum, tissue, saliva, tears, spinal fluid, tissue section(s) obtained by biopsy; cell(s) that are placed in or adapted to tissue culture; sweat, mucous, gastric fluid, abdominal fluid, amniotic fluid, cyst fluid, peritoneal fluid, pancreatic juice, breast milk, lung lavage, marrow, gastric acid, bile, semen, pus, aqueous humor, transudate,and the like including derivatives, portions and combinations of the foregoing. In some examples, biological samples include, but are not limited, to stool, biopsy, blood and / or plasma. In some examples, biological samples include, but are not limited, to urine or stool. Biological samples include, but are not limited, to biopsy. Biological samples include, but are not limited, to tissue dissections and tissue biopsies. Biological samples include, but are not limited, any derivative or fraction of the aforementioned biological samples. The biological sample can include a macromolecule. The biological sample can include a small molecule. The biological sample can include a virus. The biological sample can include a cell or derivative of a cell. The biological sample can include an organelle. The biological sample can include a cell nucleus. The biological sample can include a rare cell from a population of cells. The biological sample can include any type of cell, including without limitation prokaryotic cells, eukaryotic cells, bacterial, fungal, plant, mammalian, or other animal cell type, mycoplasmas, normal tissue cells, tumor cells, or any other cell type, whether derived from single cell or multicellular organisms. The biological sample can include a constituent of a cell. The biological sample can include nucleotides (e.g., ssDNA, dsDNA, RNA), organelles, amino acids, peptides, proteins, carbohydrates, glycoproteins, or any combination thereof. The biological sample can include a matrix (e.g., a gel or polymer matrix) comprising a cell or one or more constituents from a cell (e.g., cell bead), such as DNA, RNA, organelles, proteins, or any combination thereof, from the cell. The biological sample may be obtained from a tissue of a subject. The biological sample can include a hardened cell. Such hardened cells may or may not include a cell wall or cell membrane. The biological sample can include one or more constituents of a cell but may not include other constituents of the cell. An example of such constituents may include a nucleus or an organelle. The biological sample may include a live cell. The live cell can be capable of being cultured.

[0087] The term “biomarker,” as used herein, generally refers to any measurable substance taken as a sample from a subject whose presence is indicative of some phenomenon. Non-limiting examples of such phenomenon can include a disease state, a condition, or exposure to a compound or environmental condition. In various embodiments described herein, biomarkers may be used for diagnostic purposes (e.g., to diagnose a health state, a disease state). The term “biomarker” can be used interchangeably with the term “marker.”

[0088] The term “denaturation,” as used herein, generally refers to any molecule that loses quaternary structure, tertiary structure, and secondary structure which is present intheir native state. Non-limiting examples include proteins or nucleic acids being exposed to an external compound or environmental condition such as acid, base, temperature, pressure, radiation, etc.

[0089] The term “denatured protein,” as used herein, generally refers to a protein that loses quaternary structure, tertiary structure, and secondary structure which is present in their native state.

[0090] The terms “digestion” or “enzymatic digestion,” as used herein, generally refers to a biological process that employs enzymes to break specific amino acid peptide bonds. For example, digesting a peptide includes contacting the peptide with an digesting enzyme, e.g., trypsin to produce fragments of the glycopeptide. In some examples, a protease enzyme is used to digest a glycopeptide. The term “protease” refers to an enzyme that performs proteolysis or breakdown of large peptides into smaller polypeptides or individual amino acids. Examples of a protease include, but are not limited to, one or more of a serine protease, threonine protease, cysteine protease, aspartate protease, glutamic acid protease, metalloprotease, asparagine peptide lyase, and any combinations of the foregoing.Enzymatic digestion may be used in preparation for mass spectrometry using trypsin digestion protocols. Proteins may be digested using other proteases in preparation for mass spectrometry if access is limited to cleavage sites.

[0091] The term “disease state” as used herein, generally refers to a condition that affects the structure or function of an organism. Non-limiting examples of causes of disease states may include pathogens, immune system dysfunctions, cell damage caused by aging, cell damage caused by other factors (e.g., trauma and cancer). Disease states can include any state of a disease whether symptomatic or asymptomatic. Disease states can include disease stages of a disease progression. Disease states can cause minor, moderate, or severe disruptions in structure or function of an organism (e.g., a subject).

[0092] The term “fragment,” as used herein, generally refers to an ion fragmentation process which occurs in a MRM-MS instrument. Fragmenting may produce various fragments having the same mass but varying with respect to their charge, e.g., some biomarkers described herein produce more than one product m / z.

[0093] The terms “glycan” or “polysaccharide” as used herein, both generally refer to a carbohydrate residue of a glycoconjugate, such as the carbohydrate portion of a glycopeptide, glycoprotein, glycolipid, or proteoglycan. Glycans can include monosaccharides.

[0094] The term “glycopeptide” or “glycopolypeptide” as used herein, generally refers to a peptide or polypeptide comprising at least one glycan residue. In various embodiments,glycopeptides comprise carbohydrate moi eties e.g., one or more glycans) covalently attached to a side chain (z.e. R group) of an amino acid residue.

[0095] The term “glycopeptide fragment” or “glycosylated peptide fragment” or “glycopeptide” as used herein, generally refers to a glycosylated peptide (or glycopeptide) having an amino acid sequence that is the same as part (but not all) of the amino acid sequence of the glycosylated protein from which the glycosylated peptide is obtained, e.g., ion fragmentation within a MRM-MS instrument. MRM refers to multiple-reactionmonitoring. Unless specified otherwise, within the specification, “glycopeptide fragments” or “fragments of a glycopeptide” refer to the fragments produced directly by using a mass spectrometer optionally after the glycoprotein has been digested enzymatically to produce the glycopeptides.

[0096] The term “glycoprotein,” as used herein, generally refers to a protein having at least one glycan residue bonded thereto. In some examples, a glycoprotein is a protein with at least one oligosaccharide chain covalently bonded thereto. Examples of glycoproteins include but are not limited to the peptide structures including glycan molecules shown in the various Tables presented herein. A glycopeptide, as used herein, refers to a fragment of a glycoprotein, unless specified otherwise to the contrary.

[0097] The term “liquid chromatography,” as used herein, generally refers to a technique used to separate a sample into parts. Liquid chromatography can be used to separate, identify, and quantify components.

[0098] The term “mass spectrometry,” as used herein, generally refers to an analytical technique used to identify molecules. In various embodiments described herein, mass spectrometry can be involved in characterization and sequencing of proteins.

[0099] The term “glycopeptide structure monomer weight score,” (also “peptide structure monomer weight score,” used interchangeably) as used herein, generally refers to a value calculated as a function of a site occupancy score of a given peptide structure at a given site and the number of a specific monomer (e.g., specific monosaccharide) for the given glycopeptide structure. In some cases, a glycopeptide structure monomer weight score is a product of the site occupancy score and the number of a specific monomer for the given glycopeptide structure. Thus, as one example, a glycopeptide structure monomer weight score for glycan 5402 at site 33 of the AGP1 protein is the product of the number of a particular type of monomer (e.g., hexose) on that structure and the site occupancy of the 5402 structure at that site. A “glycopeptide structure monomer weight score” may, in some embodiments, be described in terms of a particular type of monomer and / or a particular typeof peptide structure. For example, a specific glycopeptide structure monomer weight score may be a glycan 5402 hexose weight score, i.e., a glycopeptide structure monomer weight score calculated as a product of the number of hexose molecules on glycan 5402 (i.e., 5) and the site occupancy of glycan 5402 at a particular site.

[0100] The term “monomer weight score,” as used herein, generally refers to a value calculated as a sum of individual glycopeptide structure monomer weight scores for all peptide structures at a particular site. In some embodiments, the monomer weight score is a sum of the individual glycopeptide structure monomer weight scores for all peptide structures at a particular site. Thus, as one example, a monomer weight score for hexose at site 33 of the protein AGP1 is a sum of the individual hexose weight scores for each glycan at site 33.[010.1] The term “monomer,” as used herein, generally refers to a single or type of unit of a glycan structure. In some aspects, the term “monomer” describes a monosaccharide.Examples of monomers include hexose (e.g., mannose or galactose), HexNac (e.g., GlcNAc or GalNAc), fucose, sialic acid (e.g., NeuAc), mannose, galactose, GlcNAc, and GalNAc.

[0102] The term “m / z” or “mass-to-charge ratio,” as used herein, generally refers to an output value from a mass spectrometry instrument. In various embodiments, m / z can represent a relationship between the mass of a given ion and the number of elementary charges that it carries. The “m” in m / z stands for mass and the “z” stands for charge. In some embodiments, m / z can be displayed on an x-axis of a mass spectrum.

[0103] The term “patient,” as used herein, generally refers to a mammalian subject. The mammal can be a human, or an animal including, but not limited to an equine, porcine, canine, feline, ungulate, and primate animal. In one embodiment, the individual is a human. The methods and uses described herein are useful for both medical and veterinary uses. A “patient” is a human subject unless specified to the contrary.

[0104] The term “peptide,” as used herein, generally refers to amino acids linked by peptide bonds. Peptides can include amino acid chains between 10 and 50 residues. Peptides can include amino acid chains shorter than 10 residues, including, oligopeptides, dipeptides, tripeptides, and tetrapeptides. Peptides can include chains longer than 50 residues and may be referred to as “polypeptides” or “proteins.” As used herein, the phrase “peptide,” is meant to include glycopeptides unless stated otherwise.10105] The terms “protein” or “polypeptide” or “peptide” may be used interchangeably herein and generally refer to a molecule including at least three amino acid residues. Proteins can include polymer chains made of amino acid sequences linked together by peptide bonds. Proteins may be digested in preparation for mass spectrometry using trypsin digestionprotocols. Proteins may be digested using other proteases in preparation for mass spectrometry if access is limited to cleavage sites.10106] The term “peptide structure,” as used herein, generally refers to peptides or a portion thereof or glycopeptides or a portion thereof. In various embodiments described herein, a peptide structure can include any molecule comprising at least two amino acids in sequence.|0107] The term “reduction,” as used herein, generally refers to the gain of an electron by a substance. In various embodiments described herein, a sugar can directly bind to a protein, thereby, reducing the amino acid to which it binds. Such reducing reactions can occur in glycosylation. In various embodiments, reduction may be used to break disulfide bonds between two cysteines.

[0108] The term “sample,” as used herein, generally refers to a sample from a subject of interest and may include a biological sample of a subject. The sample may include a cell sample. The sample may include a cell line or cell culture sample. The sample can include one or more cells. The sample can include one or more microbes. The sample may include a nucleic acid sample or protein sample. The sample may also include a carbohydrate sample or a lipid sample. The sample may be derived from another sample. The sample may include a tissue sample, such as a biopsy, core biopsy, needle aspirate, or fine needle aspirate. The sample may include a fluid sample, such as a blood sample, urine sample, or saliva sample. The sample may include a skin sample. The sample may include a cheek swab. The sample may include a plasma or serum sample. The sample may include a cell-free or cell free sample. A cell-free sample may include extracellular polynucleotides. The sample may originate from blood, plasma, serum, urine, saliva, mucosal excretions, sputum, stool, or tears. The sample may originate from red blood cells or white blood cells. The sample may originate from feces, spinal fluid, CNS fluid, gastric fluid, amniotic fluid, cyst fluid, peritoneal fluid, marrow, bile, other body fluids, tissue obtained from a biopsy, skin, or hair.

[0109] The term “sequence,” as used herein, generally refers to a biological sequence including one-dimensional monomers that can be assembled to generate a polymer. Nonlimiting examples of sequences include nucleotide sequences (e.g., ssDNA, dsDNA, and RNA), amino acid sequences (e.g., proteins, peptides, and polypeptides), and carbohydrates (e.g., compounds including Cm (IfcOjn).[0110 The term “site monomer,” (also “monomer weight feature”), as used herein, generally refers to a single type of glycan monomer at a particular glycopeptide structure site. Types of glycan monomers include, for example, hexose (i.e., mannose and galactose;referred to herein in some aspect as “hex”), HexNac (i.e., GlcNAc and GalNAc; referred to herein in some aspect as “hexnac”), fucose (i.e., deoxyhexose; referred to herein in some aspect as “fuco”), and sialic acid (i.e., NeuAc; referred to herein in some aspect as “sial”). Thus, as one example, site monomers for site 33 of protein AGP1 include AGPl_33_hex (i.e., a hexose monomer at site 33 of AGP1), AGPl_33_hexnac (i.e., a GlcNAc or GalNAc monomer at site 33 of AGP1), AGPl_33_fuco (i.e., a fucose monomer at site 33 of AGP1), and AGPl_33_sial (i.e., a sialic acid monomer at site 33 of AGP1).| 01111 The term “subject,” as used herein, generally refers to an animal, such as a mammal (e.g., human) or avian (e.g., bird), or other organism, such as a plant. For example, the subject can include a vertebrate, a mammal, a rodent (e.g., a mouse), a primate, a simian or a human. Animals may include, but are not limited to, farm animals, sport animals, and pets. A subject can include a healthy or asymptomatic individual, an individual that has or is suspected of having a disease (e.g., cancer) or a pre-disposition to the disease, and / or an individual that is in need of therapy or suspected of needing therapy. A subject can be a patient. A subject can include a microorganism or microbe (e.g., bacteria, fungi, archaea, viruses). A subject may be one who has been previously identified as having a disease or a condition, and optionally has already undergone, or is undergoing, a therapeutic intervention for the disease or condition. Alternatively, a subject can also be one who has not been previously diagnosed as having a disease or a condition. For example, a subject can be one who exhibits one or more risk factors for a disease or a condition, or a subject who does not exhibit disease risk factors, or a subject who is asymptomatic for a disease or a condition. A subject can also be one who is suffering from or at risk of developing a disease or a condition. A subject may also be referred to as an individual or patient.[0112 j The term “training data,” as used herein generally refers to data that can be input into models, statistical models, algorithms and any system or process able to use existing data to make predictions.[(H13| As used herein, a “model” may include one or more algorithms, one or more mathematical techniques, one or more machine learning algorithms, or a combination thereof.

[0114] As used herein, “machine learning” may be the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world. Machine learning uses algorithms that can learn from data without relying on rules- based programming. A machine learning algorithm may include a parametric model, a nonparametric model, a deep learning model, a neural network, a linear discriminant analysis model, a quadratic discriminant analysis model, a support vector machine, a random forestalgorithm, a nearest neighbor algorithm, a combined discriminant analysis model, a k-means clustering algorithm, a supervised model, an unsupervised model, logistic regression model, a multivariable regression model, a penalized multivariable regression model, or another type of model.

[0115] As used herein, an “artificial neural network” or “neural network” (NN) may refer to mathematical algorithms or computational models that mimic an interconnected group of artificial nodes or neurons that processes information based on a connectionistic approach to computation. Neural networks, which may also be referred to as neural nets, can employ one or more layers of nonlinear units to predict an output for a received input. Some neural networks include one or more hidden layers in addition to an output layer. The output of each hidden layer is used as input to the next layer in the network, z.e., the next hidden layer or the output layer. Each layer of the network generates an output from a received input in accordance with current values of a respective set of parameters. In the various embodiments, a reference to a “neural network” may be a reference to one or more neural networks.[Oil 6] A neural network may process information in two ways: when it is being trained it is in training mode and when it puts what it has learned into practice it is in inference (or prediction) mode. Neural networks learn through a feedback process (e.g., backpropagation) which allows the network to adjust the weight factors (modifying its behavior) of the individual nodes in the intermediate hidden layers so that the output matches the outputs of the training data. In other words, a neural network learns by being fed training data (learning examples) and eventually learns how to reach the correct output, even when it is presented with a new range or set of inputs. A neural network may include, for example, without limitation, at least one of a Feedforward Neural Network (FNN), a Recurrent Neural Network (RNN), a Modular Neural Network (MNN), a Convolutional Neural Network (CNN), a Residual Neural Network (ResNet), an Ordinary Differential Equations Neural Networks (neural-ODE), or another type of neural network.

[0117] As used herein, a “target glycopeptide analyte,” may refer to a peptide structure (e.g., glycosylated or aglycosylated / non-glycosylated), a fraction of a peptide structure, a sub-structure (e.g., a glycan or a glycosylation site) of a peptide structure, a product of one or more of the above listed structures and sub-structures, associated detection molecules (e.g., signal molecule, label, or tag), or an amino acid sequence that can be measured by mass spectrometry.(0118] As used herein, a “peptide data set,” may be used interchangeably with “peptide structure data” and can refer to any data of or relating to a peptide from a resulting mass spectrometry run. A peptide data set can comprise data obtained from a sample or biological sample using mass spectrometry. A peptide dataset can comprise data relating to an external standard, data relating to an internal standard, and data relating to a target glycopeptide analyte of a sample. A peptide data set can result from analysis originating from a single run. In some embodiments, the peptide data set can include raw abundance and mass to charge ratios for one or more peptides.

[0119] As used herein, a “a transition,” may refer to or identify a peptide structure. In some embodiments, a transition can refer to the specific pair of m / z values associated with a precursor ion and a product or fragment ion.10120] As used herein, a “non-glycosylated endogenous peptide” (“NGEP”) may refer to a peptide structure that does not comprise a glycan molecule. In various embodiments, an NGEP and a target glycopeptide analyte can originate from the same subject. In various embodiments, an NGEP and a target glycopeptide analyte may be derived from the same protein sequence. In some embodiments, the NGEP and the target glycopeptide analyte may be derived from or include the same peptide sequence. In various embodiments, an NGEP can be labeled with an isotope in preparation for mass spectrometry analysis.

[0121] As used herein, “abundance,” may refer to a quantitative value generated using mass spectrometry. In various embodiments, the quantitative value may relate to the amount of a particular peptide structure. In some embodiments, the quantitative value may comprise an amount of an ion produced using mass spectrometry. In some embodiments, the quantitative value may be expressed as an m / z value. In other embodiments, the quantitative value may be expressed in atomic mass units.

[0122] As used herein, “relative abundance,” may refer to a comparison of two or more abundances. In various embodiments, the comparison may comprise comparing one peptide structure to a total number of peptide structures. In some embodiments, the comparison may comprise comparing one peptide glycoform (e.g., two identical peptides differing by one or more glycans) to a set of peptide glycoforms. In some embodiments, the comparison may comprise comparing a number of ions having a particular m / z ratio by a total number of ions detected. In various embodiments, a relative abundance can be expressed as a ratio. In other embodiments, a relative abundance can be expressed as a percentage. Relative abundance can be presented on a y-axis of a mass spectrum plot.

[0123] As used herein, an “internal standard,” may refer to something that can be contained (e.g., spiked-in) in the same sample as a target glycopeptide analyte undergoing mass spectrometry analysis. Internal standards can be used for calibration purposes. Additionally, internal standards can be used in the systems and method described herein. In some aspects, an internal standard can be selected based on similarity m / z and or retention times and can be a “surrogate” if a specific standard is too costly or unavailable. Internal standards can be heavy labeled or non-heavy labeled.

[0124] As used herein, a “fucose inhibitor” can refer to the use of a reagent (e.g., 2- Fluoro-peracetyl-fucose or 2FPF) that inhibits fucosyltransferases. The use of a fucose inhibitor can cause the expression of proteins like PD-1 to not have attached glycans that have a fucose moiety.III. Overview of Exemplary Workflow

[0125] Figure 1 is a schematic diagram of an exemplary workflow 100 for the detection of peptide structures associated with a disease state for use in diagnosis and / or treatment in accordance with one or more embodiments. Workflow 100 may include various operations, for example, sample collection 102, sample intake 104, sample preparation and processing 106, data analysis 108, and output generation 110.

[0126] Sample collection 102 may include, for example, obtaining a biological sample 112 of one or more subjects, such as subject 114. Biological sample 112 may take the form of a specimen obtained via one or more sampling methods. Biological sample 112 may be representative of subject 114 as a whole or of a specific tissue, cell type, or other category or sub-category of interest. Biological sample 112 may be obtained in any of a number of different ways. In various embodiments, biological sample 112 can include whole blood sample 116 obtained via a blood draw. In other embodiments, biological sample 112 can include set of aliquoted samples 118, for example, a serum sample, a plasma sample, a blood cell (e.g., white blood cell (WBC), red blood cell (RBC) sample, another type of sample, or a combination thereof. Biological samples 112 may include nucleotides (e.g., ssDNA, dsDNA, RNA), organelles, amino acids, peptides, proteins, carbohydrates, glycoproteins, or any combination thereof.

[0127] In various embodiments, a single run can analyze a sample (e.g., the sample comprising a peptide analyte), an external standard (e.g., an NGEP of a serum sample), and an internal standard. As such, abundance or raw abundance for the external standard, the internalstandard, and target glycopeptide analyte can be determined by mass spectrometry in the same run.

[0128] In various embodiments, external standards may be analyzed prior to analyzing samples. In various embodiments, the external standards can be run independently between the samples. In some embodiments, external standards can be analyzed after every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more experiments. In various embodiments, external standard data can be used in some or all of the normalization systems and methods described herein. In additional embodiments, blank samples may be processed to prevent column fouling.

[0129] Sample intake 104 may include one or more various operations such as, for example, aliquoting, registering, processing, storing, thawing, and / or other types of operations. In one or more embodiments, when biological sample 112 comprises whole blood sample 116, sample intake 104 can include aliquoting whole blood sample 116 to form a set of aliquoted samples that can then be sub-aliquoted to form set of samples 120.

[0130] Sample preparation and processing 106 may include, for example, one or more operations to form set of peptide structures 122. In various embodiments, set of peptide structures 122 may include various fragments of unfolded proteins that have undergone digestion and may be ready for analysis.

[0131] Further, sample preparation and processing 106 may include, for example, data acquisition 124 based on set of peptide structures 122. For example, data acquisition 124 may include use of, for example, but is not limited to, a liquid chromatography / mass spectrometry (LC / MS) system.

[0132] Data analysis 108 may include, for example, peptide structure analysis 126. In some embodiments, data analysis 108 also comprises output generation 110. In other embodiments, output generation 110 may be considered a separate operation from data analysis 108. Output generation 110 may include, for example, generating final output 128 based on the results of peptide structure analysis 126. Final output 128 may be used for determining research, diagnosis, and / or treatment.

[0133] In various embodiments, final output 128 is comprised of one or more outputs. Final output 128 may take various forms. For example, final output 128 may be a report that includes, for example, a diagnosis output, a treatment output (e.g., a treatment design output, a treatment plan output, or combination thereof), analyzed data (e.g., relativized and normalized) or combination thereof. In some embodiments, report can comprise a target glycopeptide analyte concentration as a function of the NGEP concentration value and the normalizedabundance. In some embodiments, final output 128 may be an alert (e.g., a visual alert, an audible alert, etc.), a notification (e.g., a visual notification, an audible notification, an email notification, etc.), an email output, or a combination thereof. In some embodiments, final output 128 may be sent to remote system 130 for processing. Remote system 130 may include, for example, a computer system, a server, a processor, a cloud computing platform, cloud storage, a laptop, a tablet, a smartphone, some other type of mobile computing device, or a combination thereof.

[0134] In other embodiments, workflow 100 may optionally exclude one or more of the operations described herein and / or may optionally include one or more other steps or operations other than those described herein (e.g., in addition to and / or instead of those described herein). Accordingly, workflow 100 may be implemented in any of a number of different ways for use in the research, diagnosis, and / or treatment of a disease state.IV. Detection and Quantification of Peptide Structures[0135J Figure 2A and Figure 2B are schematic diagrams of a workflow for sample preparation and processing 106 in accordance with one or more embodiments. Figure 2A and Figure 2B are described with continuing reference to Figure 1. Sample preparation and processing 106 may include, for example, preparation workflow 200 shown in Figure 2A and data acquisition 124 shown in Figure 2B.IV. A. Sample Preparation and Processing

[0136] Figure 2A is a schematic diagram of preparation workflow 200 in accordance with one or more embodiments. Preparation workflow 200 may be used to prepare a sample, such as a sample of set of samples 120 in Figure 1, for analysis via data acquisition 124. For example, this analysis may be performed via mass spectrometry (e.g., LC-MS). In various embodiments, preparation workflow 200 may include denaturation and reduction 202, alkylation 204, and digestion 206. Areas of the preparation workflow can cause inconsistency between different samples and different experiments, necessitating the improved normalization systems and methods described herein and throughout. 0137] In general, polymers, such as proteins, in their native form, can fold to include secondary, tertiary, and / or other higher order structures. Such higher order structures may functionalize proteins to complete tasks (e.g., enable enzymatic activity) in a subject.Further, such higher order structures of polymers may be maintained via various interactions between side chains of amino acids within the polymers. Such interactions can include ionicbonding, hydrophobic interactions, hydrogen bonding, and disulfide linkages between cysteine residues. However, when using analytic systems and methods, including mass spectrometry, unfolding such polymers (e.g., peptide / protein molecules) may be desired to obtain sequence information. In some embodiments, unfolding a polymer may include denaturing the polymer, which may include, for example, linearizing the polymer.[0138 J In one or more embodiments, denaturation and reduction 202 can be used to disrupt higher order structures (e.g., secondary, tertiary, quaternary, etc. of one or more proteins (e.g., polypeptides and peptides) in a sample (e.g., one of set of samples 120 in Figure 1). Denaturation and reduction 202 includes, for example, a denaturation procedure and a reduction procedure. In some embodiments, the denaturation procedure may be performed using, for example, thermal denaturation, where heat is used as a denaturing agent. The thermal denaturation can disrupt ionic bonding, hydrophobic interactions, and / or hydrogen bonding.[0139 In various embodiments, the denaturation procedure may include using one or more denaturing agents. In one or more embodiments, the denaturation procedure may include using temperature. In one or more embodiments, the denaturation procedure may include using one or more denaturing agents in combination with heat. These one or more denaturing agents may include, for example, but are not limited to, any number of chaotropic salts (e.g., urea, guanidine), surfactants (e.g., sodium dodecyl sulfate (SDS), beta octyl glucoside, Triton X-100), or combination thereof. In some cases, such denaturing agents may be used in combination with heat when sample preparation workflow further includes a cleanup procedure.

[0140] The resulting one or more denatured (e.g., unfolded, linearized) proteins may then undergo further processing in preparation of analysis. For example, a reduction procedure may be performed in which one or more reducing agents are applied. In various embodiments, a reducing agent can produce an alkaline pH. A reducing agent may take the form of, for example, without limitation, dithiothreitol (DTT), tris(2-carboxyethyl)phosphine (TCEP), or some other reducing agent. The reducing agent may reduce (e.g., cleave) the disulfide linkages between cysteine residues of the one or more denatured proteins to form one or more reduced proteins.10141 ] In various embodiments, the one or more reduced proteins resulting from denaturation and reduction 202 may undergo a process to prevent the reformation of disulfide linkages between, for example, the cysteine residues of the one or more reduced proteins. This process may be implemented using alkylation 204 to form one or more alkylatedproteins. For example, alkylation 204 may be used to add an acetamide group to a sulfur on each cysteine residue to prevent disulfide linkages from reforming. In various embodiments, an acetamide group can be added by reacting one or more alkylating agents with a reduced protein. The one or more alkylating agents may include, for example, one or more acetamide salts. An alkylating agent may take the form of, for example, iodoacetamide (IAA), 2- chloroacetamide, some other type of acetamide salt, or some other type of alkylating agent.

[0142] In some embodiments, alkylation 204 may include a quenching procedure. The quenching procedure may be performed using one or more reducing agents (e.g., one or more of the reducing agents described above).[01431 In various embodiments, the one or more alkylated proteins formed via alkylation 204 can then undergo digestion 206 in preparation for analysis (e.g., mass spectrometry analysis). Digestion 206 of a protein may include cleaving the protein at or around one or more cleavage sites (e.g., site 205 which may be one or more amino acid residues). For example, without limitation, an alkylated protein may be cleaved at the carboxyl side of the lysine or arginine residues. This type of cleavage may break the protein into various segments, which include one or more peptide structures (e.g., glycosylated or aglycosylated).

[0144] In various embodiments, digestion 206 is performed using one or more proteolysis catalysts. For example, an enzyme can be used in digestion 206. In some embodiments, the enzyme takes the form of trypsin. In other embodiments, one or more other types of enzymes (e.g., proteases) may be used in addition to or in place of trypsin. These one or more other enzymes include, but are not limited to, LysC, LysN, AspN, GluC, and ArgC. In some embodiments, digestion 206 may be performed using tosyl phenylalanyl chloromethyl ketone (TPCK)-treated trypsin, one or more engineered forms of trypsin, one or more other formulations of trypsin, or a combination thereof. In some embodiments, digestion 206 may be performed in multiple steps, with each involving the use of one or more digestion agents. For example, a secondary digestion, tertiary digestion, etc. may be performed. In one or more embodiments, trypsin is used to digest serum samples. In one or more embodiments, trypsin / LysC cocktails are used to digest plasma samples.

[0015] In some embodiments, digestion 206 further includes a quenching procedure. The quenching procedure may be performed by acidifying the sample (e.g., to a pH <3). In some embodiments, formic acid may be used to perform this acidification.

[0146] In various embodiments, preparation workflow 200 further includes post-digestion procedure 207. Post-digestion procedure 207 may include, for example, a cleanup procedure. The cleanup procedure may include, for example, the removal of unwanted components inthe sample that results from digestion 206. For example, unwanted components may include, but are not limited to, inorganic ions, surfactants, etc. In some embodiments, post-digestion procedure 207 further includes a procedure for the addition of heavy-labeled peptide internal standards.

[0147] Although preparation workflow 200 has been described with respect to a sample created or taken from biological sample 112 that is blood-based (e.g., a whole blood sample, a plasma sample, a serum sample, etc.), sample preparation workflow 200 may be similarly implemented for other types of samples (e.g., tears, urine, tissue, interstitial fluids, sputum, etc.) to produce set of peptides structures 122.IV.B . Peptide Structure Identification and Quantitation

[0148] Figure 2B is a schematic diagram of data acquisition 124 in accordance with one or more embodiments. In various embodiments, data acquisition 124 can commence following sample preparation 200 described in Figure 2A. In various embodiments, data acquisition 124 can comprise quantification 208, quality control 210, and peak integration and normalization 212.

[0149] In various embodiments, targeted quantification 208 of peptides and glycopeptides can incorporate use of liquid chromatography -mass spectrometry LC / MS instrumentation. For example, LC-MS / MS, or tandem MS may be used. In general, LC / MS (e.g., LC-MS / MS) can combine the physical separation capabilities of liquid chromatograph (LC) with the mass analysis capabilities of mass spectrometry (MS). According to some embodiments described herein, this technique allows for the separation of digested peptides to be fed from the LC column into the MS ion source through an interface.

[0150] In various embodiments, any LC / MS device can be incorporated into the workflow described herein. In various embodiments, an instrument or instrument system suited for identification and targeted quantification 208 may include, for example, a Triple Quadrupole LC / MS™. In various embodiments, targeted quantification 208 is performed using multiple reaction monitoring mass spectrometry (MRM-MS).

[0151] In various embodiments described herein, identification of a particular protein or peptide and an associated quantity can be assessed. In various embodiments described herein, identification of a particular glycan and an associated quantity can be assessed. In various embodiments described herein, particular glycans can be matched to a glycosylation site on a protein or peptide and the abundances measured.

[0152] In some cases, targeted quantification 208 includes using a specific collision energy associated for the appropriate fragmentation to consistently see an abundant product ion. Glycopeptide structures may have a lower collision energy than aglycosylated peptide structures. When analyzing a sample that includes glycopeptide structures, the source voltage and gas temperature may be lowered as compared to generic proteomic analysis. [0153 J In various embodiments, quality control 210 procedures can be put in place to optimize data quality. In various embodiments, measures can be put in place allowing only errors within acceptable ranges outside of an expected value. In various embodiments, employing statistical models (e.g., using Westgard rules) can assist in quality control 210. For example, quality control 210 may include, for example, assessing the retention time and abundance of representative peptide structures (e.g., glycosylated and / or aglycosylated) and spiked-in internal standards, in either every sample, or in each quality control sample (e.g., pooled serum digest).

[0154] Peak integration and normalization 212 may be performed to process the data that has been generated and transform the data into a format for analysis. For example, peak integration and normalization 212 may include converting abundance data for various product ions that were detected for a selected peptide structure into a single quantification metric (e.g., a relative quantity, an adjusted quantity, a normalized quantity, a relative concentration, an adjusted concentration, a normalized concentration, etc.) for that peptide structure. In some embodiments, peak integration and normalization 212 may be performed using one or more of the techniques described in U.S. Patent Publication No. 2020 / 0372973A1 and / or US Patent Publication No. 2020 / 0240996A1, the disclosures of which are incorporated by reference herein in their entireties.V. Peptide Structure Data AnalysisV. A. Exemplary System for Peptide Structure Data AnalysisV.A.l. Analysis System for Peptide Structure Data Analysis

[0155] Figure 3 is a block diagram of an analysis system 300 in accordance with one or more embodiments. Analysis system 300 can be used to both detect and analyze various peptide structures that have been associated to various disease states. Analysis system 300 is one example of an implementation for a system that may be used to perform data analysis 108 in Figure 1. Thus, analysis system 300 is described with continuing reference to workflow 100 as described in Figures 1, 2A, and / or 2B.

[0156] Analysis system 300 may include computing platform 302 and data store 304. In some embodiments, analysis system 300 also includes display system 306. Computing platform 302 may take various forms. In one or more embodiments, computing platform 302 includes a single computer (or computer system) or multiple computers in communication with each other. In other examples, computing platform 302 takes the form of a cloud computing platform.

[0157] Data store 304 and display system 306 may each be in communication with computing platform 302. In some examples, data store 304, display system 306, or both may be considered part of or otherwise integrated with computing platform 302. Thus, in some examples, computing platform 302, data store 304, and display system 306 may be separate components in communication with each other, but in other examples, some combination of these components may be integrated together. Communication between these different components may be implemented using any number of wired communications links, wireless communications links, optical communications links, or a combination thereof.

[0158] Analysis system 300 includes, for example, peptide structure analyzer 308, which may be implemented using hardware, software, firmware, or a combination thereof. In one or more embodiments, peptide structure analyzer 308 is implemented using computing platform 302.

[0159] Peptide structure analyzer 308 receives peptide structure data 310 for processing. Peptide structure data 310 may be, for example, the peptide structure data that is output from sample preparation and processing 106 in Figures 1, 2A, and 2B. Accordingly, peptide structure data 310 may correspond to set of peptide structures 122 identified for biological sample 112 and may thereby correspond to biological sample 112.

[0160] Peptide structure data 310 can be sent as input into peptide structure analyzer 308, retrieved from data store 304 or some other type of storage (e.g., cloud storage), accessed from cloud storage, or obtained in some other manner. In some cases, peptide structure data 310 may be retrieved from data store 304 in response to (e.g., directly or indirectly based on) receiving user input entered by a user via an input device.[016.1] Peptide structure analyzer 308 includes model 312 that is configured to receive peptide structure data 310 for processing. Model 312 may be implemented in any of a number of different ways. Model 312 may be implemented using any number of models, functions, equations, algorithms, and / or other mathematical techniques.

[0012] In one or more embodiments, model 312 includes machine learning system 314, which may itself be comprised of any number of machine learning models and / or algorithms.For example, machine learning system 314 may include, but is not limited to, at least one of a deep learning model, a neural network, a linear discriminant analysis model, a quadratic discriminant analysis model, a support vector machine, a random forest algorithm, a nearest neighbor algorithm (e.g., a k-Nearest Neighbors algorithm), a combined discriminant analysis model, a k-means clustering algorithm, an unsupervised model, a multivariable regression model, a penalized multivariable regression model, or another type of model. In various embodiments, model 312 includes a machine learning system 314 that comprises any number of or combination of the models or algorithms described above.

[0163] In various embodiments, model 312 analyzes peptide structure data 310 to generate disease indicator 316 that indicates whether the biological sample is positive for a colorectal cancer disease state based on set of peptide structures 318 identified as being associated with the colorectal cancer disease state. Peptide structure data 310 may include quantification data for the plurality of peptide structures. Quantification data for peptide structures can include at least one of an abundance, a relative abundance, a normalized abundance, a relative quantity, an adjusted quantity, a normalized quantity, a relative concentration, an adjusted concentration, or a normalized concentration. For example, peptide structure data 310 may include a set of quantification metrics for each peptide structure of a plurality of peptide structures. A quantification metric for a peptide structure may be selected as one of a relative quantity, an adjusted quantity, a normalized quantity, a relative abundance, an adjusted abundance, and a normalized abundance. In some cases, a quantification metric for a peptide structure is selected from one of a relative concentration, an adjusted concentration, and a normalized concentration. In one or more embodiments, the quantification metrics used are normalized abundances. In this manner, peptide structure data 310 may provide abundance information about the plurality of peptide structures with respect to biological sample 112.

[0164] Disease indicator 316 may take various forms. In some examples, disease indicator 316 includes a classification that indicates whether or not the subject is positive for the lung cancer disease state. In various embodiments, disease indicator 316 can include a score 320. In some aspects, score 320 indicates whether the lung cancer disease state is present or not. For example, score 320 may be, a probability score that indicates how likely it is that the biological sample 112 evidences the presence of the lung cancer disease state. Alternatively or in addition, disease indicator 316 includes a classification that indicates whether or not the subject has immunotherapy responsive lung cancer (i.e., will respond to immunotherapy treatment, for example immune checkpoint inhibitors such as ipilimumab,nivolumab, and / or pembrolizumab) or immunotherapy nonresponsive lung cancer (i.e., will not respond to immunotherapy treatment, for example immune checkpoint inhibitors such as ipilimumab, nivolumab, and / or pembrolizumab). In such cases, score 320 may be, for example, a probability score that indicates how likely it is that the biological sample 112 evidences the presence of an immunotherapy responsive lung cancer.[0165 J In one or more embodiments, a peptide structure or set of peptide structures 318 comprises a glycosylated peptide structure, or glycopeptide structure, that is defined by a peptide sequence and a glycan structure attached to a linking site of the peptide sequence quantity. For example, the peptide structure may be a glycopeptide or a portion of a glycopeptide. In some embodiments, a peptide structure of set of peptide structures 318 comprises an aglycosylated peptide structure that is defined by a peptide sequence. For example, the peptide structure may be a peptide or a portion of a peptide and may be referred to as a quantification peptide.

[0166] Set of peptide structures 318, and / or monomer weight scores derived therefrom, may be identified as being those most predictive or relevant to the lung cancer disease state based on training of model 312.

[0167] In various embodiments, machine learning system 314 takes the form of binary classification model 322. Binary classification model 322 may include, for example, but is not limited to, a regression model. Binary classification model 322 may include, for example, a penalized multivariable regression model that is trained to identify set of peptide structures 318 from a plurality of (or panel of) peptide structures identified in various subjects. Binary classification model 322 may be trained to identify weight coefficients for peptide structures and those peptide structures having non-zero weights or weight coefficients above a selected threshold (e.g., absolute weight coefficient above 0.0, 0.01, 0.05, 0.1, 0.015, 0.2, etc.) may be selected for inclusion in set of peptide structures 318.

[0168] Peptide structure analyzer 308 may generate final output 128 based on disease indicator 316 output by model 312. In other embodiments, final output 128 may be an output generated by model 312.

[0169] In some embodiments, final output 128 comprises disease indicator 316. In one or more embodiments, final output 128 comprises diagnosis output 324, treatment output 326, or both. Diagnosis output 324 may include, for example, a diagnosis for the lung cancer state. The diagnosis can include a positive diagnosis or a negative diagnosis for the lung cancer disease state. Alternatively or in addition, the diagnosis can include a determination that the subject has immunotherapy responsive lung cancer or immunotherapy nonresponsivelung cancer. In some aspects, immunotherapy responsive lung cancer describes lung cancer responsive to treatment with immune checkpoint inhibitors. In some aspects, immunotherapy responsive lung cancer describes lung cancer responsive to combination treatment with ipilimumab and nivolumab. In some aspects, immunotherapy responsive lung cancer describes lung cancer responsive to combination treatment with pembrolizumab.

[0170] In one or more embodiments, when disease indicator 316 and / or diagnosis output 324 indicate a positive diagnosis for the lung cancer disease state, a biopsy may be recommended. For example, a biopsy of the subject may be performed in response to disease indicator 316 and / or diagnosis output 324 indicating a positive diagnosis for the lung cancer disease state. In some embodiments, peptide structure analyzer 308 (or another system implemented on computing platform 302) may generate a report recommending that a biopsy is to be performed for the subject in response to disease indicator 316 and / or diagnosis output 324 indicating a positive diagnosis for the lung cancer disease state. In other embodiments, peptide structure analyzer 308 may send diagnosis final output 128 to remote system 130 over one or more wireless, wired, and / or optical communications links and remote system 130 may generate a report recommending that a biopsy is to be performed for the subject in response to disease indicator 316 and / or diagnosis output 324 indicating a positive diagnosis for the lung cancer disease state. The biopsy may be used to confirm the diagnosis to determine whether or not to administer treatment, which treatment to administer, and / or how quickly to administer treatment. When disease indicator 316 and / or diagnosis output 324 indicate a negative diagnosis for the lung cancer disease state, the report that is generated by peptide structure analyzer 308, remote system 130, or some other system implemented on computing platform 142 may recommend a period of monitoring for the subject. For example, a negative diagnosis indication by disease indicator 316 and / or diagnosis output 324 may thus help prevent unnecessary treatment or overtreatment of the subject.

[0171] Treatment output 326 may include, for example, at least one of an identification of a treatment for the subject, a treatment plan for administering the treatment, or both. Treatment for lung cancer may include, for example, but is not limited to, at least one of surgery, radiation therapy, a targeted drug therapy (e.g., one or more targeted therapeutic agents), chemotherapy (e.g., one or more chemotherapeutic agents), immunotherapy (e.g., one or more immunotherapeutic agents such as immune checkpoint inhibitors cemiplimab and / or camrelizumab), hormone therapy, neoadjuvant therapy, or some other form of treatment. The treatment plan may include, for example, but is not limited to, a timeline orschedule for administering the treatment, dosing information, other treatment-related information, or a combination thereof.

[0172] Final output 128 may be sent to remote system 130 for processing in some examples. In other embodiments, final output 128 may be displayed on graphical user interface 330 in display system 306 for viewing by a human operator.V.A.2. Computer Implemented System

[0173] Figure 4 is a block diagram of a computer system in accordance with various embodiments. Computer system 400 may be an example of one implementation for computing platform 302 described above in Figure 3.

[0174] In one or more examples, computer system 400 can include a bus 402 or other communication mechanism for communicating information, and a processor 404 coupled with bus 402 for processing information. In various embodiments, computer system 400 can also include a memory, which can be a random-access memory (RAM) 406 or other dynamic storage device, coupled to bus 402 for determining instructions to be executed by processor 404. Memory also can be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 404. In various embodiments, computer system 400 can further include a read only memory (ROM) 408 or other static storage device coupled to bus 402 for storing static information and instructions for processor 404. A storage device 410, such as a magnetic disk or optical disk, can be provided and coupled to bus 402 for storing information and instructions.

[0175] In various embodiments, computer system 400 can be coupled via bus 402 to a display 412, such as a cathode ray tube (CRT), liquid crystal display (LCD), or light emitting diode (LED) for displaying information to a computer user. An input device 414, including alphanumeric and other keys, can be coupled to bus 402 for communicating information and command selections to processor 404. Another type of user input device is a cursor control 416, such as a mouse, a joystick, a trackball, a gesture input device, a gaze-based input device, or cursor direction keys for communicating direction information and command selections to processor 404 and for controlling cursor movement on display 412. This input device 414 typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane. However, it should be understood that input devices 414 allowing for three-dimensional (e.g., x, y, and z) cursor movement are also contemplated herein. In various embodiments, the system can include one or more processors.

[0176] Consistent with certain implementations of the present teachings, results can be provided by computer system 400 in response to processor 404 executing one or more sequences of one or more instructions contained in RAM 406. Such instructions can be read into RAM 406 from another computer-readable medium or computer-readable storage medium, such as storage device 410. Execution of the sequences of instructions contained in RAM 406 can cause processor 404 to perform the processes described herein. Alternatively, hard-wired circuitry can be used in place of or in combination with software instructions to implement the present teachings. Thus, implementations of the present teachings are not limited to any specific combination of hardware circuitry and software.

[0177] The term “computer-readable medium” (e.g., data store, data storage, storage device, data storage device, etc.) or “computer-readable storage medium” as used herein refers to any media that participates in providing instructions to processor 404 for execution. Such a medium can take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Examples of non-volatile media can include, but are not limited to, optical, solid state, magnetic disks, such as storage device 410. Examples of volatile media can include, but are not limited to, dynamic memory, such as RAM 406. Examples of transmission media can include, but are not limited to, coaxial cables, copper wire, and fiber optics, including the wires that comprise bus 402.

[0178] Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, or any other tangible medium from which a computer can read.

[0179] In addition to computer readable medium, instructions or data can be provided as signals on transmission media included in a communications apparatus or system to provide sequences of one or more instructions to processor 404 of computer system 400 for execution. For example, a communication apparatus may include a transceiver having signals indicative of instructions and data. The instructions and data are configured to cause one or more processors to implement the functions outlined in the disclosure herein. Representative examples of data communications transmission connections can include, but are not limited to, telephone modem connections, wide area networks (WAN), local area networks (LAN), infrared data connections, NFC connections, optical communications connections, etc.(0180] It should be appreciated that the methodologies described herein, flow charts, diagrams, and accompanying disclosure can be implemented using computer system 400 as a standalone device or on a distributed network of shared computer processing resources such as a cloud computing network.

[0181] The methodologies described herein may be implemented by various means depending upon the application. For example, these methodologies may be implemented in hardware, firmware, software, or any combination thereof. For a hardware implementation, the processing unit may be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, electronic devices, other electronic units designed to perform the functions described herein, or a combination thereof.

[0182] In various embodiments, the methods of the present teachings may be implemented as firmware and / or a software program and applications written in conventional programming languages such as C, C++, Python, etc. If implemented as firmware and / or software, the embodiments described herein can be implemented on a non-transitory computer-readable medium in which a program is stored for causing a computer to perform the methods described above. It should be understood that the various engines described herein can be provided on a computer system, such as computer system 400, whereby processor 404 would execute the analyses and determinations provided by these engines, subject to instructions provided by any one of, or a combination of, the memory components RAM 406, ROM, 408, or storage device 410 and user input provided via input device 414.VI. Exemplary EmbodimentsMaterials and methodsReagents

[0183] 2-Fluoro-peracetyl-fucose (2FPF) was purchased from Cayman Chemicals (Ann Arbor, MI). The anti -human PD-1 antibodies camrelizumab, pembrolizumab, nivolumab and cemiplimab were from Selleck Chemicals (Houston, TX). Human IgG4 isotype was from Biolegend (San Diego, CA).Serum samples

[0184] Pre-treatment NSCLC serum samples were sourced from i Specimen (Lexington, MA). Samples were collected in accordance with relevant applicable guidelines and regulations for human subjects’ protection as outlined in the Declaration of Helsinki. All protocols were approved under respective Institutional Review Boards and Ethics Committees (20223899). All subjects utilized in this study provided a written informed consent prior to collection of samples. Collected clinical data included age at blood draw, body mass index, sex, race, and histopathological data and clinical staging, when applicable.Cell lines and culture(0185| CHO-K1 and CHO-K1 Fut8 KO cell lines (Creative Biogene, Shirley, NY) were cultured in RPMI-1640 media (Thermo Fisher Scientific, Walham, MA) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin. Jurkat-Lucia TCR-hPD-1 and Raji-APC-hPD-Ll cells were purchased from InvivoGen (San Diego, CA) and cultured in IMDM media (Thermo Fisher Scientific) supplemented with 10% fetal bovine serum (VWR, Radnor, PA), 1% penicillin-streptomycin (Thermo Fisher Scientific) and 100 ug / ml Normocin (Invivogen). Adherent cell lines were maintained in a humidified incubator at 37°C with 5% CO2. Expi293 cells (Thermo Fisher Scientific) were maintained in Expi293 expression media (Thermo Fisher Scientific) in a humidified incubator at 37 °C with 8% CO2 at 125 rpm.Recombinant protein production|0186 DNA encoding for residues 24-167 of the extracellular portion of human PD-1 (UniProt Q15116) with a C-terminus histidine tag or corresponding PD-1 N58Q mutant were cloned into pcDNA3.4 by GenScript (Piscataway, NJ). Expi293 cells were transiently transfected with plasmid DNA mixed with PEI (Polysciences, Warrington, PA) and incubated for 5 days at 37 °C. For expression of fucosylated PD-1, Expi293 cells were grown in presence of 0.6 mM 2FPF. PD-1 proteins were purified by affinity chromatography using Ni Sepharose Excel resin (Cytiva, Marlborough, MA). PD-10 columns (Cytiva) were used to exchange buffer to phosphate-buffered saline (PBS; Corning, Tweksbury, MA). Protein quality and purity was assessed by SDS-PAGE (BioRad, Hercules, CA) and size-exclusion chromatography using a Superdex 200 5 / 150 (Cytiva) with Acquity UPLC (Waters, Milford, MA). Protein concentration was determined using a NanoDrop (Wilmington, DE).Mass spectrometry analysis of recombinant PD-1 variants

[0187] For chymotrypsin digestion, 5 pg of each PD-1 variant were diluted with 50 mM ammonium bicarbonate buffer (Sigma Aldrich) and reduced with the addition of 5 mM dithiothreitol (DTT; Sigma Aldrich). Samples were incubated at 60 °C for 20 minutes and then alkylated with 10 mM iodoacetamide (Sigma Aldrich) in the dark at 25°C for one hour. Sequencing grade chymotrypsin (Promega, Madison, WI) was added at a protein: enzyme ratio of 40: 1 and the samples were incubated at 25°C for 8 hours. For trypsin digestion, 5 pg of each PD-1 variant were diluted in a 50 mM ammonium bicarbonate buffer (Sigma Aldrich) containing 5 mM DTT concentration. Samples were incubated at 60°C for 45 minutes and then alkylated with 10 mM iodoacetamide for 30 minutes at 25°C. MS-grade trypsin (Pierce) was added to the samples at a protein: enzyme ratio of 40: 1. The samples were then incubated at 37°C for 8 hours before LC-MS grade formic acid (LiChroPur) was added to a final concentration of 1% to quench the reaction.|0188 Digested samples were analyzed using an Orbitrap Exploris 480 Mass Spectrometer (i.e., ion trap mass analyzer, Thermo Scientific) after desalting with an Acclaim Pepmap 100 Cl 8 trap (Thermo Scientific) and liquid chromatography separation with an Ultimate 3000 RSLCnano System (Dionex). Samples eluting from the LC were ionized with a Nanospray Flex™ Ion Source at a spray voltage of 2800V. Each sample was acquired using a Top 20 data dependent acquisition (DDA) method. Raw data were analyzed with GlycoDecipher software suite with trypsin or chymotrypsin set as proteases, depending on the experiment, allowing up to 3 missed cleavages. Cysteine carbamidomethylation was set as a fixed modification, and methionine oxidation as variable. Spectrum expansion was enabled, and the other settings were left as default. GlycoTouCan was used as a glycan database. Identified spectra were manually validated for correct sequence and glycan assignment, quantitation was performed based on summed peak areas of the elution profiles of each glycopeptide at all the detected charge states.Antibody binding assays

[0189] Binding kinetics of PD-1 antibodies were determined using an Octet Red96 instrument (ForteBio), following previously described procedures. All proteins were diluted in Octet kinetics buffer (Sartorius, Gottingen, Germany). Antibodies were captured on antihuman Fc AHC2 tips (Sartorius). Data capture was performed using Octet Data Acquisition software version 9.0 (ForteBio). Data analysis was performed using Octet Data Analysis software version 9.0 (ForteBio) using a 1 : 1 binding model.

[0190] Antibody binding was also assayed by flow cytometry using CHO-K1 and CHO- K1 Fut8 KO cells transfected with a human PD-1 plasmid (GenScript) by lipofectamine (Thermo Fisher Scientific). Anti-PD-1 antibodies or human IgG4 isotype were added to the cells and incubated for 20 minutes on ice. Cells were washed and stained with an 0.1 mg / 1 anti-human IgG Fc-PE antibody for 20 minutes. Cells were analyzed using a CytoFlex instrument (Beckman Coulter, Brea, CA) and data were analyzed using Flow Jo software (FlowJo, Ashland, OR).10191] Additionally, antibody binding was assessed by flow cytometry using human naive CD8 T cells. 2 million CD8 T cells (Stemcell Technologies, Cambridge, MA) from three donors were cultured for 48 hours in AIM V Medium (Thermo Fisher Scientific) supplemented with 5% heat-inactivated fetal bovine serum and activated with Dynabeads Human T-Activator CD3 / CD28 (ThermoFisher Scientific) at a 1 : 1 bead:cell ratio according to the manufacturer’s instructions, in the presence or absence of 0.1 mM of 2FPF. At days 1 and 2 after stimulation, cells were stained with a Zombie Red viability dye kit (Biolegend), followed by fixation with fixation solution (eBioscience-Thermo). Core fucosylation was assessed by incubating cells with FITC-conjugated LCA lectin (Vector Labs) at a 1 :500 dilution. To confirm activation followed by bead stimulation, cells were stained with BV711- conjugated anti-CD25 antibody (Clone M-A251, Biolegend) at a 1 :50 dilution in PBS. Cells were incubated with pembrolizumab, camrelizumab or human IgG4 isotype control (at 1 nM concentration), followed by incubation with PE-conjugated anti-human IgG4 (Southern Biotechnologies) at a 1 :300 dilution in PBS. Cells were analyzed by a CytoFlex flow cytometer and data analysis was performed using FlowJo 10.9.0 software.Ligand blocking assays

[0192] To assess the binding of PD-L1 to PD-1, biotinylated human PD-L1 (ACROBioSystems, Newark, DE) and PE-conjugated streptavidin (Biolegend, San Diego, CA) were mixed at a 1 :2 molar ratio, added to PD-1 transfected CHO-K1 or CHO-K1 Fut8 KO cells and incubated for 20 minutes on ice. Cells were washed and analyzed by a CytoFlex flow cytometer. To investigate blocking of PD-L1 binding, PD-1 transfected CHO-K1 and CHO-K1 Fut8 KO cells were incubated with antibodies for 15 minutes on ice before addition of PD-L1 :SA-PE complexes. Cells were then washed and analyzed by a CytoFlex flow cytometer.

[0013] For plate-based ligand blocking assay, 96-well flat-bottom plates (Thermo Fisher Scientific) were coated with 5 ug / ml of purified recombinant PD-1, PD-1 NF or PD-1 N58Qin PBS overnight at room temperature. Plates were then washed with PBS containing 0.05% Tween 20 (PBST). After blocking with 5% BSA in PBS for one hour at room temperature, wells were washed with PBST, followed by incubation with anti-PD-1 antibodies diluted in PBS containing 0.5% BSA for one hour incubation at room temperature. After washing with PBST, 1 ug / ml PD-Ll-Fc-biotin in PBS containing 0.5% BSA were added to each well and incubated for two hours. After washing with PBST, the wells were incubated with 0.1 ug / ml horseradish peroxidase-conjugated streptavidin (ACROBiosystems) for 30 minutes. Wells were washed and incubated with 3,3',5,5'-Tetra-methyl benzidine substrate (Biolegend, San Diego, CA), followed by a stop solution (Biolegend). Absorbance at 450 nm was read with the Envision microplate reader (Perkin Elmer).PD-1:PD-L1 functional assay

[0194] Jurkat TCR-hPD-1 cells were pre-cultured with or without 300pM 2F-Peracetyl- Fucose for 3 days in IMDM medium supplemented with 10% FBS, 1% penicillinstreptomycin. Antibodies were diluted in PBS and added into 96 well flat-bottom plates. Jurkat-Lucia TCR-hPD-1 cells and Raji-APC-hPD-Ll were added to the wells and plates were incubated at 37°C in a CO2 incubator for 6 hours. Supernatants were transferred into a 96-well white plate (VWR) and assayed with QUANTI-Luc 4 reagent (Invivogen). Luminescence was read immediately with an Envision system.Serum PD-1 quantification

[0195] Serum and recombinant PD-1 was quantified by human PD-1 Quantikine ELISA and human PD-1 DuoSet ELISA (R&D Systems, Minneapolis, MN), following manufacturer’s protocol. In addition, serum and recombinant PD-1 were quantified by ELISA assay using wells coated overnight with 5 pg / ml camrelizumab or pembrolizumab in PBS, followed by incubation with 1% BSA in PBS and detection with human PD-1 DuoSet ELISA detection antibody. Wells were washed and incubated with 3,3',5,5'-Tetra-methyl benzidine substrate. Reaction was stopped with acid solution, and absorbance was read with the Envision microplate reader.Data analysis and visualization

[0196] GraphPad Prism (GraphPad Software, Boston, MA) was used to represent data and for statistical analysis.Protein structure analysis

[0197] Interactions and interfaces between antibodies or ligands and PD-1 were calculated with PDBePISA (https: / / www.ebi.ac.uk / pdbe / pisa / papers / pisa-web.pdf) and CoCoMaps https: / / www.molnac.unisa.it / BioTools / cocomaps / using PDB structures 7CU5, 7WVM, 7ZQK, 5WT9 and 5DK3). Structures were visualized by PyMol (Schroedinger, New York, NY).EXAMPLESI. Example 1: Glycosylation-dependent anti-PD-1 antibodies interact with the core fucose

[0198] A number of monoclonal antibodies have been approved as anti-cancer treatment in a broad range of indications (Table 1) with clinical efficacy and toxicity profiles that may be related to the intrinsic properties of the molecule. A subgroup of these antibodies, including camrelizumab and cemiplimab, have been recently identified for their dependency on the N58 glycosylation for binding. To elucidate the detailed binding characteristics of camrelizumab and cemiplimab to PD-1 and the PD-L1 mechanism, the available structures of antibody :PD-1 complexes were inspected. In line with what was previously described, it was observed that camrelizumab and cemiplimab bound to a similar region of PD-1 in proximity of the N58 glycosylation site, whereas pembrolizumab and nivolumab recognized different epitopes (Figure 9A and Figure 9B). In particular, the presence of the N58 glycan of PD-1 in the camrelizumab :PD-1 complex allowed the visualization of the interactions between the VH and the glycan. In addition to the previously described interactions between the HCDR1 and HCDR2 and the core glycan, we observed that residues S30 and S31 of the CRD1 and S52, G53 and G54 of the CRD2 engaged with the core alphal,6 fucose through hydrogen bonds (Figure 5A to Figure 5C). Although the PD-1 N58 glycan was not visible in the complex with cemiplimab, the antibody appeared to bind to PD-1 in a similar orientation as camrelizumab, suggesting the N58 glycan composition may influence binding and activity of both antibodies.Table 1: List of monoclonal antibodies that have been approved as anti-cancer treatment for a broad range of indications.II. Example 2: Core fucose of the PD-1 N58 glycan is a key determinant for camrelizumab and cemiplimab binding

[0199] To directly test if the core fucose of the PD-1 N58 glycan was involved in antibody binding, both protein- and cell-based assays were used. First, three recombinant PD- 1 variants were generated by expressing the extracellular region of PD-1 in Expi293 cells in presence or absence of the fucose inhibitor 2-fluoro-peracetyl-fucose, and by producing a N58Q mutant protein (Figure 6A). Mass spectrometry analysis confirmed that the glycoforms at the N58 site of the PD-1 and PD-1 NF (expressed in presence of fucose inhibitor) variants were comparable and differed only for the presence or absence of fucose (Figure 6B and Figure 10 to Figure 12). Binding of camrelizumab and cemiplimab to thethree PD-1 variants was examined by a biolayer interferometry assay, along with nivolumab and pembrolizumab as controls. A 100-fold difference in affinity of camrelizumab to PD-1 and PD-1 N58Q was observed, in line with previous reports (Table 2). Strikingly, the difference in KD between PD-1 and the N58Q mutant was completely recapitulated by PD-1 NF, indicating that the contribution of the N58 glycan to the antibody binding is largely provided by the core fucose. Similarly, binding of cemiplimab to PD-1 was compromised by the absence of fucose, although not to the same extent as camrelizumab. As expected, binding of pembrolizumab or nivolumab to the PD-1 variants was not affected by changes in glycosylation.Table 2: Affinity of anti -PD-1 antibodies for PD-1 variants.

[0200] Next, to further test the hypothesis that core fucose was a key determinant for binding, antibody binding to full length PD-1 expressed in wild type or Fut8-knockout CHO- K1 cells was analyzed (Figure 6C). Absence of core fucose reduced binding of camrelizumab and cemiplimab, but did not alter binding of pembrolizumab, resulting in relative EC50 values of 0.9, 0.9 and 0.5 nM in CHO-K1 cells and 1.4, 1.15 and 0.3 nM in CHO-K1 Fut8-KO cells for camrelizumab, cemiplimab and pembrolizumab, respectively.

[0201] Lastly, PD-1 detection by camrelizumab was assessed in primary human CD8 T cells activated in the presence or absence of fucose inhibitor. As expected, naive CD8 T cells expressed zero or relatively low levels of PD-1 and CD25 (Figure 6E1 to Figure 6E4). CD3 / CD28 stimulation induced CD25 expression within 24 hours (Figure 6E4) and PD-1 expression within 48 hours (Figure 6E1 to Figure 6E2). Remarkably, activation in the presence of the fucose inhibitor (e.g., 2FPF) resulted in a substantial decrease of binding of the lectin LCA (Figure 6E3) and PD-1 detection by camrelizumab (Figure 6E2), but did not affect recognition by pembrolizumab (Figure 6E1) or the CD25 antibody (Figure 6E4). The term vehicle means adding buffer that does not contain 2FPF.

[0202] Altogether, these data indicate that non-fucosylated PD-1 can be expressed on the surface of T cells and that binding of camrelizumab and, to lesser extent, cemiplimab depends on core fucosylation of the N58 glycan of PD-1.III. Example 3: PD-1 fucosylation affects PD-L1 blocking efficacy of camrelizumab and cemiplimab

[0203] To understand if the glycosylation structure of PD-1 had an impact on the ability of anti-PD-1 antibodies to block interactions with PD-L1 (Figure 7A), the fucosylation status of PD-1 modulated interaction with its ligand was assessed, and no differences in PD-L1 binding to PD-1 in CH0-K1 cells was observed regardless of Fut8 expression (EC50 values were 1.0 nM for CHO-K1 cells and 1.7 nM CHO-K1 Fut8 KO cells) (Figure 14). However, whereas ligand blocking profiles were overlapping in CHO-K1 cells, camrelizumab and cemiplimab were less effective in preventing PD-L1 interactions in Fut8-deficient cells (calculated IC50 values were 5.6 nM camrelizumab, 2.6 nM for cemiplimab and 0.9 nM for pembrolizumab).

[0204] To further investigate the impact of fucosylation on the blocking efficacy of anti- PD-1 antibodies, their capacity to impair binding of PD-L1 to plate-coated PD-1 variants (Figure 7B) was monitored. Inhibition of PD-L1 binding to PD-1 was similar for cemiplimab, camrelizumab and pembrolizumab. However, blockade of PD-L1 binding was sensitive to both fucosylation and glycosylation at the N58 site, with calculated IC50 values 1.6 nM for camrelizumab, 0.6 nM for cemiplimab, and 0.5 nM for pembrolizumab for PD-1 NF and 22.3 nM for camrelizumab, 1.6 nM for cemiplimab, and 0.8 nM for pembrolizumab for PD-1 N58Q.

[0205] Lastly, to determine if the observed differences in ligand blockade had functional consequences in modulating PD-1 signaling, a reporter assay that uses suppression of luciferase expression as a readout of PD-1 :PD-L1 interaction (Figure 7C) was employed. Consistent with previous observations, differences in blocking ability of the anti-PD-1 antibodies in untreated cells was not detected. However, treatment of Jurkat hPD-1 cells with a fucose inhibitor (Figure 15A and Figure 15B) revealed differences in the potency of the antibodies, with calculated IC50 values of 10.7 nM for camrelizumab, 5.7 nM for cemiplimab, and 2.0 nM for pembrolizumab. The lower overall signals detected in Jurkat cells exposed to the fucose inhibitors might be related to the impact of fucose on other components of the immune synapse. Taken together, these data suggest that the blocking efficacy of the anti-PD-1 antibodies camrelizumab and cemiplimab is influenced by PD-1 fucosylation at the N58 site.IV. Example 4: Fucosylated PD-1 increases in serum of late-stage lung cancer patients

[0206] As camrelizumab and cemiplimab are selective for fucosylated PD-1, it is important to understand if there was variability in fucose content of soluble PD-1 in blood of cancer patients. In order to detect fucosylated PD-1, a plate-based assay was developed that employs plate-bound camrelizumab or pembrolizumab for capturing PD-1, followed by detection with polyclonal anti -PD-1 antibodies. The two assays were able to discriminate between fucosylated and non-fucosylated PD-1 (Figure 16C and Figure 16D). Surprisingly, similar binding patterns were observed with commercial kits (Figure 16A and Figure 16B). Using combinations of these assays, it was possible to measure fucosylated PD-1 in the serum of a cohort of NSCLC patients (Table 3). Whereas there were no differences in the concentration of total serum PD-1 in different disease stages (Figure 8A), the fraction of fucosylated PD-1 appeared to increase in late-stage lung cancer (Figure 8B). Altogether, these data indicate that there is variability in serum PD-1 fucosylation of cancer patients.Table 3: Study population characteristics. Counts are followed by the appropriate column-wise percentage, while continuous variables are summarized by medians and standard deviation.

[0207] Glycosylation impacts the structure of proteins, their activity and interactions with other molecules. The availability of increasingly sophisticated tools to characterize this protein modification has allowed the definition of structure-function relationships, bringing light to the complexity of the glycan structures, the fine regulation of the underlying biosynthetic mechanisms, and their detailed roles. For example, modulation of IgGl binding to FcgRIIIA by Fc fucosylation is a well known feature that has been exploited in therapeutics design to enhance antibody-dependent cytotoxicity and antibody-dependent phagocytosis. While currently underexplored, glycoprofiling of new or validated drug targets represents a valuable opportunity for development of next generation therapeutics that may achieve exquisite selectivity by binding to distinct glycosylation variants. Additionally, the discovery of fucosylated biomarkers in blood of melanoma patients that failed to achieve extended survival following treatment with immune checkpoint inhibitors point to a role of glycosylation not only in the tumor but also in the periphery. Therefore, glycosylation analysis of circulating glycoproteins may offer crucial information for both drug design and biomarker discovery.

[0208] It was observed that camrelizumab interacted with the core fucose of the N58 N- glycan of PD-1. The current study confirmed that camrelizumab and cemiplimab bound preferentially to PD-1 carrying glycans with core fucose. Camrelizumab exhibited a stronger dependance on this modification compared to cemiplimab, probably due to the contribution of serine S31 in the HCDR1 in binding to the fucose moiety. Using a combination of protein- and cell-based assays, it was shown that the ligand blocking activity of these two antibodies is sensitive to variations in fucose. Lastly, the study provided preliminary data indicating that fucosylation of soluble PD-1 in blood varies in different stages of lung cancer. Other antibodies that depend on the N58 glycan for binding, including MW11 -h317, mAB059c, and STM418, might be regulated in a similar fashion by the glycan structure.

[0209] Fucosylation can regulate PD-1 function in T cells. Whereas both fucosylated and non fucosylated forms of PD-1 can be found on T cells, the stability of PD-1 may be enhanced by fucosylation at sites N49 and N74. It is interesting that core fucosylation promotes the stability of the cell surface adhesin LCAM1, likely by blocking shedding caused by proteases.Similarly, MHC-II fucosylation accumulates this protein at the surface of tumor cells. Therefore, core fucosylation might exhibit a general role in supporting receptor stability at the cell surface. Interestingly, as fucosylation leads to PD-1 accumulation on the T cell surface and limits activation of murine T cells, it has been suggested that terminally exhausted cells display high levels of fucosylated PD-1. As there is debate around which subsets of exhausted T cells (precursor or terminally exhausted) can be reactivated by anti -PD-1 antibodies, gly coformspecific antibodies may help provide insights on this matter.

[0210] The analysis shows that fucosylation of PD-1 has an impact on binding of clinically used blocking antibodies. In addition to the fucosylation at the N58 site, under certain circumstances, it is possible that additional glycan binding sites may have an impact on antibody binding. Moreover, it was observed that a 100-fold difference in KD values between fucosylated and non-fucosylated PD-1 for camrelizumab, resulted in a 5 to 10-fold difference in PD-1 :PD-L1 blocking activity. Additional studies in primary cells with endogenous PD-1 expression are needed to provide a better understanding of the functional relevance of fucosylation. Thirdly, lack of data from clinical trials comparing camrelizumab or cemiplimab to glycosylation-independent anti -PD-1 antibodies does not allow a clear assessment of the additional benefit of targeting fucosylated PD-1. Preclinical and clinical evidence points to significant overall survival across multiple tumor indications was obtained with camrelizumab, even if pharmacodynamics data from human phase 1 trials of camrelizumab and nivolumab revealed comparable PD-1 binding affinity (KD equal to 3.31 nM and 3.65 nM, respectively) and receptor occupancy on circulating T lymphocytes (85% and 70%, respectively). Of note, whereas camrelizumab displayed similar rates of grade 3-4 treatment-related adverse events compared to nivolumab and pembrolizumab, ranging 22-25% of the cohorts studied, reactive cutaneous capillary endothelial proliferation was observed much more frequently with camrelizumab compared to nivolumab and pembrolizumab (67.0% vs. 2.4%). This has been attributed to the unexpected binding to vascular endothelial growth factor receptor 2 (VEGFR2), frizzled class receptor 5 and ULI 6 binding protein 2 (ULBP2) that may correlate with the side effects of capillary hemangiomas observed in clinical studies with camrelizumab. [02.1.1] In summary, the present disclosure demonstrates that two anti-PD-1 antibodies currently used in clinical routine have a preference for glycosylated PD-1 modified with a fucose, a frequent but variable element in glycoproteins. Future studies should determine PD- 1 glycosylation status both in T cells in different activation states and in the blood of patients scheduled to receive immune checkpoint inhibitor therapy. Methods with increased sensitivity, such as glycoproteomics, may prove particularly useful for such analyses. This researchsupports a framework for the development of antibody therapeutics that target distinct subsets of target proteins. If the glycosylation status of membrane-bound and soluble target is comparable, measuring glycosylation of circulating form provides a convenient strategy to obtain information about the tumor microenvironment. Conversely, if peripheral and tissue glycosylation differ, this feature can be engineered in the antibody specificity to enhance tumor targeting or avoid peripheral sink effects.Additional Considerations

[0212] Any headers and / or subheaders between sections and subsections of this document are included solely for the purpose of improving readability and do not imply that features cannot be combined across sections and subsection. Accordingly, sections and subsections do not describe separate embodiments.

[0213] While the present teachings are described in conjunction with various embodiments, it is not intended that the present teachings be limited to such embodiments. On the contrary, the present teachings encompass various alternatives, modifications, and equivalents, as will be appreciated by those of skill in the art. The present description provides preferred exemplary embodiments, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the present description of the preferred exemplary embodiments will provide those skilled in the art with an enabling description for implementing various embodiments.

[0214] It is understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope as set forth in the appended claims. Thus, such modifications and variations are considered to be within the scope set forth in the appended claims. Further, the terms and expressions which have been employed are used as terms of description and not of limitation, and there is no intention in the use of such terms and expressions of excluding any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the invention claimed.

[0215] In describing the various embodiments, the specification may have presented a method and / or process as a particular sequence of steps. However, to the extent that the method or process does not rely on the particular order of steps set forth herein, the method or process should not be limited to the particular sequence of steps described, and one skilled in the art can readily appreciate that the sequences may be varied and still remain within the spirit and scope of the various embodiments.

[0216] Some embodiments of the present disclosure include a system including one or more data processors. In some embodiments, the system includes a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform part or all of one or more methods and / or part or all of one or more processes disclosed herein. Some embodiments of the present disclosure include a computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform part or all of one or more methods and / or part or all of one or more processes disclosed herein.

[0217] Specific details are given in the present description to provide an understanding of the embodiments. However, it is understood that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.

Claims

CLAIMSWhat is claimed:

1. A method of classifying a biological sample from a subject with respect to a responsiveness to immune checkpoint inhibitor therapy, the method comprising: analyzing an amount or absence of a fucosylated PD-1 variant from the biological sample from the subject; and generating a diagnosis output based on the amount or absence of the fucosylated PD-1 variant that classifies whether the subject is likely to benefit or not likely to benefit from the immune checkpoint inhibitor therapy.

2. The method of claim 1, wherein the analyzing an amount or absence of the fucosylated PD-1 variant comprises: receiving an abundance value from a mass spectrometer.

3. The method of claim 2, wherein the mass spectrometer is configured to perform multiple reaction monitoring mass spectrometry (MRM-MS).

4. The method of claim 2, wherein the mass spectrometer is configured to perform a shotgun analysis.

5. The method of claim 1, wherein the analyzing an amount or absence of the fucosylated PD-1 variant comprises: receiving an optical signal value from a plate reader or a flow cytometer.

6. The method of claim 1, wherein the analyzing an amount or absence of the fucosylated PD-1 variant comprises: incubating a coated surface with the biological sample from the subject, wherein the coated surface, before the incubation, comprises a layer of an immune checkpoint inhibitor for the immune checkpoint inhibitor therapy, wherein the immune checkpoint inhibitor is configured to bind to a fucosylated PD-1 variant; wherein when the biological sample contains the fucosylated PD-1 variant, it binds to the coated surface to form a bound fucosylated PD-1 variant; and measuring an optical signal corresponding to the amount or absence of the fucosylated PD-1 variant in the sample.

7. The method of claim 6, wherein after the fucosylated PD-1 variant is bound to the coated surface and before measuring the optical signal, the method further comprises: adding a PD-1 detection antibody to the coated surface, wherein the PD-1 detection antibody is configured to bind to the bound fucosylated PD-1 variant, and wherein the PD-1 detection antibody is coupled to a peroxidase; binding the PD-1 detection antibody to the bound fucosylated PD-1 variant to form a bound fucosylated PD-1 variant / PD-1 detection antibody complex; adding a peroxidase substrate and a dye onto the coated surface; oxidizing the dye to form an oxidized dye, wherein the amount of the oxidized dye is proportional to the amount or absence of the fucosylated PD-1 variant; and measuring the amount of the oxidized dye with an optical signal.

8. The method of any one of claims 1-7, wherein the immune checkpoint inhibitor therapy comprises an immune checkpoint inhibitor selected from the group consisting of camrelizumab, cemiplimab, and a combination thereof.

9. The method of any one of claims 1-8, wherein the fucosylated PD-1 variant comprises a fucosylated glycan at position N58 of a PD-1 protein.

10. The method of any one of claims 1-9, wherein the fucosylated PD-1 variant comprises one or more glycan compositions selected from the group consisting of H4N4S1F1, H3N4F1, H3N5F1, H4N5S1F1, H5N4S1F1, H4N4F1, and combinations thereof.

11. The method of any one of claims 1-10, wherein the fucosylated glycan comprises a core fucose.

12. The method of any one of claims 1-11, wherein the immune checkpoint inhibitor is configured to bind to the fucosylated PD-1 variant with a higher affinity than a non- fucosylated PD-1 variant.

13. A method of treating a subject comprising: analyzing an amount or absence of a fucosylated PD-1 variant from a biological sample from a subject;generating a diagnosis output based on the amount or absence of the fucosylated PD-1 variant that classifies whether the subject is likely to benefit or not likely to benefit from the immune checkpoint inhibitor therapy; and administering to the subject a therapeutically effective amount of an immune checkpoint inhibitor, wherein the immune checkpoint inhibitor is configured to bind to the fucosylated PD-1 variant with a higher affinity than a non-fucosylated PD-1 variant.

14. The method of claim 13, wherein the analyzing an amount or absence of the fucosylated PD-1 variant comprises: receiving an abundance value from a mass spectrometer.

15. The method of claim 14, wherein the mass spectrometer is configured to perform multiple reaction monitoring mass spectrometry (MRM-MS).

16. The method of claim 14, wherein the mass spectrometer is configured to perform a shotgun analysis.

17. The method of claim 13, wherein the analyzing an amount or absence of the fucosylated PD-1 variant comprises: receiving an optical signal value from a plate reader or a flow cytometer.

18. The method of claim 13, wherein the analyzing the amount or absence of the fucosylated PD-1 variant comprises: incubating a coated surface with the biological sample from the subject, wherein the coated surface, before the incubation, comprises a layer of an immune checkpoint inhibitor for the immune checkpoint inhibitor therapy, wherein the immune checkpoint inhibitor is configured to bind a fucosylated PD-1 variant; wherein when the biological sample contains the fucosylated PD-1 variant, it binds to the coated surface to form a bound fucosylated PD-1 variant; and measuring an optical signal corresponding to the amount or absence of the fucosylated PD-1 variant in the sample.

19. The method of claim 18, wherein after the fucosylated PD-1 variant is bound to the coated surface and before measuring the optical signal, the method further comprises:adding a PD-1 detection antibody to the coated surface, wherein the PD-1 detection antibody is configured to bind to the bound fucosylated PD-1 variant, and wherein the PD-1 detection antibody is coupled to a peroxidase; binding the PD-1 detection antibody to the bound fucosylated PD-1 variant to form a bound fucosylated PD-1 variant / PD-1 detection antibody complex; adding a peroxidase substrate and a dye onto the coated surface oxidizing the dye to form an oxidized dye, wherein an amount of the oxidized dye is proportional to the amount or absence of the fucosylated PD-1 variant; and measuring the amount of the oxidized dye with an optical signal.

20. The method of any one of claims 13-19, wherein the immune checkpoint inhibitor therapy comprises an immune checkpoint inhibitor selected from the group consisting of camrelizumab, cemiplimab, and a combination thereof.

21. The method of any one of claims 13-20, wherein the fucosylated PD-1 variant comprises a fucosylated glycan at position N58 of a PD-1 protein.

22. The method of any one of claims 13-21, wherein the fucosylated PD-1 variant comprises one or more glycan compositions selected from the group consisting of H4N4S1F1, H3N4F1, H3N5F1, H4N5S1F1, H5N4S1F1, H4N4F1, and combinations thereof.

23. The method of any one of claims 13-22, wherein the fucosylated glycan comprises a core fucose.

24. A method of screening a drug candidate for potential use in a subject, the method comprising: incubating a coated surface with a biological sample from the subject, wherein the coated surface, before the incubation, comprises an adsorbed layer of the drug candidate; wherein when the biological sample contains a fucosylated PD-1 variant, it binds to the coated surface; measuring an optical signal corresponding to the amount or absence of the fucosylated PD-1 variant in the sample; and classifying whether the subject is likely to benefit or not likely to benefit from the drug candidate based on the optical signal.

25. The method of claim 24, wherein after the fucosylated PD-1 variant binds to the coated surface and before measuring the optical signal, the method further comprises: adding a PD-1 detection antibody to the coated surface, wherein the PD-1 detection antibody is configured to bind to the bound fucosylated PD-1 variant, and wherein the PD-1 detection antibody is coupled to a peroxidase; binding the PD-1 detection antibody to the bound fucosylated PD-1 variant to form a bound fucosylated PD-1 variant / PD-1 detection antibody complex; adding a peroxidase substrate and a dye onto the coated surface; oxidizing the dye to form an oxidized dye, wherein an amount of the oxidized dye is proportional to the amount or absence of the fucosylated PD-1 variant; and measuring the amount of the oxidized dye with the optical signal.

26. The method of any one of claims 24-25, wherein the drug candidate comprises an anti- PD-1 antibody.

27. The method of any one of claims 24-26, wherein the fucosylated PD-1 variant comprises a fucosylated glycan at position N58 of a PD-1 protein.

28. The method of any one of claims 24-27, wherein the fucosylated PD-1 variant comprises one or more glycan compositions selected from the group consisting of H4N4S1F1, H3N4F1, H3N5F1, H4N5S1F1, H5N4S1F1, H4N4F1, and combinations thereof.

29. The method of any one of claims 24-28, wherein the fucosylated glycan comprises a core fucose.

30. The method of any one of claims 24-29, wherein the drug candidate is configured to bind to the fucosylated PD-1 variant with a higher affinity than a non -fucosylated PD-1 variant.

31. The method of any one of claims 1-30, wherein the subject has lung cancer.

32. The method of any one of claims 4 or 16, wherein the mass spectrometer is an ion trap mass analyzer or a qTOF mass spectrometer,33. The method of claim 32, wherein the ion trap mass analyzer comprises an outer barrellike electrode and a coaxial inner spindle-like electrode; wherein the ion trap mass analyzer is configured to trap ions in an orbital motion around the spindle.

34. The method of any one of claims 1-33, wherein the fucosylated PD-1 variant is coupled to a surface of a cell.

35. The method of any one of claims 1-33, wherein the fucosylated PD-1 variant is added to plasma or serum portion of a sample.

36. The method of claim 35, wherein the fucosylated PD-1 variant is not attached to a cell.

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