RU2836005C1 - Bioinformatics - Google Patents

Abstract

FIELD: biotechnology.

SUBSTANCE: described is a method for identifying a tumour-specific antigen using the following device, comprising: microfluidic device comprising at least one flow channel containing a plurality of micropillars arranged in a matrix, to which at least one molecule is attached or at least one complex associated with at least one antibody against major histocompatibility complex or at least one antibody against human pan-leukocyte antigen (HLA), whereby peptide: major histocompatibility complex (pMHC) in a sample passing through said channel, can be extracted from said sample using said at least one antibody; and processor adapted to identify said peptide bound to said major histocompatibility complex, and to compare said peptide of said bound pMHC with a library of peptide antigens of pathogens to determine whether said peptide, which is or has been associated with said major histocompatibility complex, homology/affinity with at least one pathogen antigen or part thereof, and where there is more than 60% homology/affinity; determining said peptide, which is or has been linked to said major histocompatibility complex, as a tumour-specific antigen for use in anticancer therapy. Described is a method for patient stratification with respect to cancer treatment with a checkpoint inhibitor. Also disclosed is a method for patient stratification with respect to the treatment of adenoviral cancer. Disclosed is a method of treating cancer.

EFFECT: invention extends the range of anti-cancer agents.

21 cl, 13 dwg, 6 tbl

Description

Field of invention

The present invention relates to a device for identifying a tumor antigen and a method for identifying a tumor antigen; a tumor antigen identified in accordance with the use of said device and/or method; a pharmaceutical composition containing said tumor antigen; a method for treating cancer using said device and/or said method; a method for stratifying patients for cancer treatment using said device and/or said method; a treatment regimen comprising stratifying patients for cancer treatment using said device and/or method and then administering an anticancer therapeutic agent; and a tumor antigen identified using said device and/or said method for use as an anticancer vaccine or immunogenic agent or anticancer therapy.

Prior art

CD8 ⁺ T cells play a key role in detecting and eliminating cells that present abnormal peptides on their surface as a result of a pathogenic infection, such as a viral infection or malignant transformation. T cell receptor (TCR) cross-reactivity means that T cells can recognize an incredibly wide variety of different target peptides. This phenomenon allows a relatively small number of T cells to recognize many pMHC (peptide:major histocompatibility complex) molecules that are typical of abnormal cells and therefore potentially life-threatening.

However, an undesirable side effect of this mechanism is that the immune response directed against the pathogen may overcome the tolerance threshold for a highly homologous self-antigen, causing an off-target deleterious effect mediated by T-cell cross-reactivity, a process known as molecular mimicry. In the field of autoimmunity, the potentially deleterious consequences of such homology between self and pathogen peptides are well known, however, they have not been investigated in cancer.

Indeed, it has been thought that the best prognostic markers for the successful outcome of immunotherapeutic cancer treatment are a high tumor antigen mutation rate and abundant T cell infiltration. This judgment is based on the fact that a tumor that has a high number of mutations will have a higher chance of being recognized and eliminated by T cell infiltration.

However, some studies have demonstrated that the qualitative properties of tumor antigens may be more important than their quantity. In addition, antiviral T cells have been found to populate the tumor microenvironment [28], but it is still unclear whether their role is active or merely passive.

Oncoimmunology and immunotherapy have completely changed the way cancer is treated over the past decade, particularly with the use of checkpoint inhibitors (ICIs), which have shown remarkable clinical results, but unfortunately only in a small number of patients. It is becoming clear that immunotherapy with ICIs will only work fully when targeting specific tumor antigens. However, there is currently no easy and fast way to identify these tumor antigens.

Here, the inventors hypothesized that tumors may present peptides that have a high degree of homology or affinity score with pathogen-derived peptides, in particular viral peptides, and this could allow pathogenesis-derived cross-reactive T cells to recognize and destroy tumor cells.

To this end, the inventors developed a bioinformatics device called HEX (Homology Xenopeptide Evaluation) for the automatic and very simple identification of tumor antigens that are highly similar to viral peptides. Using this device, the inventors found that antiviral T-cell immunity mediated by a peptide with high homology or affinity score to a cancer antigen can also actively control tumor growth in both prophylactic and therapeutic settings. This observation demonstrates the cross-reactivity of activated T cells against both homologous viral peptides and tumor peptides.

The inventors then also discovered that the antibody response to cytomegalovirus (CMV) is able to stratify the response in melanoma patients to checkpoint inhibitor therapy (anti-PD1 antibody). Indeed, CMV- and melanoma-homing peptides identified using the HEX device were found to promote the release of Inf-g from peripheral blood mononuclear cells (PBMC) of CMV-seropositive melanoma patients.

Statement of the invention

According to a first aspect of the invention, a device for identifying a tumor antigen is proposed, comprising:

at least one flow channel comprising a plurality of substrates to which at least one molecule or at least one complex associated with at least one anti-MHC (major histocompatibility complex) antibody or at least one anti-human leukocyte antigen (HLA) antibody is attached, whereby pMHC (peptide:major histocompatibility complex) in a sample passing through said channel can be extracted from said sample using said at least one antibody.

Reference herein to an anti-pan-HLA antibody is to an antibody that recognizes or has specificity for any of the various HLAs present in mammals, particularly humans.

More ideally, said antibody recognizes or has specificity for a particular HLA type, such as MHC-I, and selected from at least one of the following group: MHC class I A, B and C.

Additionally or alternatively, said antibody recognizes or has specificity for a particular HLA type, such as MHC-II selected from at least one of the following group: MHC class II DP, DM, DO, DQ and DR.

Even more preferably, said antibody is an anti-human antibody.

In a preferred embodiment of the invention, said molecule or complex is a complex of thiol and alkyl (“ene”) functional groups, ideally spanning a stoichiometric ratio of 1.5 to 1.0 in the range of 0.15:0.1-1500:1000 (tetrathiol:triallyl).

More ideally, the design of the device is based on a UV-triggered photoreaction between a thiol and an alkyl ("ene") functional group. In a further preferred embodiment of the invention, the device is made by UV photopolymerization with biotin-PEG4-alkyne (biotin-polyethyleneglycol 4-alkyne) (Sigma, 764213). This is followed by a reaction with avidin:streptavidin. However, it is also within the scope of the invention to attach avidin to the biological entity prior to attaching the biological entity to the base element and vice versa.

In another preferred embodiment, said device is functionalized by reacting said antibody with said streptavidin. Ideally, more than one antibody reacts with said streptavidin, wherein each streptavidin-functionalized complex carries a plurality, such as 2 or 3, of said antibodies, such as a plurality of anti-pan-HLA antibodies.

In another preferred embodiment of the invention, the micropillar array is conditioned with a protein such as bovine serum albumin (BSA) (ideally 100 μg/ml in 15 mM PBS (phosphate buffered saline), incubated for 10 min), after functionalization with streptavidin, but prior to binding to the antibody.

In a more preferred aspect of the invention, said supports comprise a plurality of micropillars, such as those provided in a microfluidic device, and preferably said pillars are arranged in a matrix in said device. Ideally, said micropillars are made of a composition that is the same or similar to a composition used in conventional microfluidic devices, for example, to carry out reactions with an immobilized enzyme.

According to a further aspect of the invention, a method for identifying a tumor antigen is provided, comprising:

1) dissolving or suspending a tumor sample in a liquid;

2) passing said liquid through a device for identifying a tumor antigen, comprising: at least one flow channel containing a plurality of substrates to which at least one molecule or at least one complex are attached, associated with at least one antibody against MHC (major histocompatibility complex) or at least one antibody against human pan-leukocyte antigen (HLA), whereby pMHC (peptide:major histocompatibility complex) in a sample passing through said channel can be extracted from said sample using said at least one antibody;

3) binding with said at least one antibody to at least one pMHC (peptide:major histocompatibility complex) in said sample;

4) optionally extracting said at least one bound pMHC (peptide:major histocompatibility complex) according to part (3) from said device;

5) comparing said peptide of said linked pMHC (peptide:major histocompatibility complex) with a library of pathogen antigens to determine whether said peptide exhibits homology or affinity (molecular mimicry) with at least one pathogen antigen or portion thereof, and where there is greater than 60% homology/affinity;

6) identification of the said peptide as a tumor antigen for use in anticancer therapy.

In a preferred embodiment of any aspect of the invention, said homology/affinity may be any of the following percentages: 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 and 100%.

Reference herein to homology/affinity includes reference to a comparison of sequence structures in part (5) above using one of: homology/affinity as defined herein; or identity, which is determined by the number of identical residues over a specified length in a given alignment; or similarity, which is determined by the number of identical residues or conservative substitutions having similar physicochemical properties over a specified length in a given alignment.

In a preferred method according to the invention, said library is antigens of human pathogens and it comprises a screened library of known pathogen antigens, ideally derived from pathogenic viruses, or more suitably their proteomes, where the viruses infect mammals, ideally humans, such as any one or more than one, or indeed any combination of the following viruses:

Abyssoviridae; Ackermannviridae; Actantavirinae; Adenoviridae; Agantavirinae; Aglimvirinae; Alloherpesviridae; Alphaflexiviridae; Alphaherpesvirinae; Alphairidovirinae; Alphasatellitidae; Alphatetraviridae; Alvernaviridae; Amalgaviridae; Amnoonviridae; Ampullaviridae; Anelloviridae; arenaviridae; Arquatrovirinae; Arteriviridae; Artoviridae; Ascoviridae; Asfarviridae; Aspiviridae; Astroviridae; Autographivirinae; Avsunviroidae; Avulavirinae; Bacilladnaviridae; Baculoviridae; Barnaviridae; Bastillevirinae; Bclasvirinae; Belpaoviridae; Benyviridae; Betaflexiviridae; Betaherpesvirinae; Betairidovirinae; Bicaudaviridae; Bidnaviridae; Birnaviridae; bornaviridae; Botourmiaviridae; Brockvirinae; Bromoviridae; Bullavirinae; Caliciviridae; Calvusvirinae; Carmotetraviridae; Caulimoviridae; Ceronivirinae; Chebruvirinae; Chordopoxvirinae; Chrysoviridae; Chuviridae; Circoviridae; Clavaviridae; Closteroviridae; Comovirinae; Coronaviridae; Corticoviridae; Crocarterivirinae; Cruliviridae; Crustonivirinae; Cvivirinae; Cystoviridae; Dclasvirinae; Deltaflexiviridae; Densovirinae; Dicistroviridae; Endoraviridae; Entomopoxvirinae; Equarterivirinae; Eucampyvirinae; Euroniviridae;

Filoviridae; Fimoviridae; First papillomavirinae; Flaviviridae; Fuselloviridae; Gammaflexiviridae; Gammaherpesvirinae; Geminialphasatelitinae; Geminiviridae; Genomoviridae; Globuloviridae; Gokushovirinae; Guernseyvirinae; Guttaviridae; Hantaviridae; Hepadnaviridae; Hepeviridae; Herelleviridae; Heroarterivirinae; Herpesviridae; Hexponivirinae; Hypoviridae; Hytrosaviridae; Iflaviridae; Inoviridae; Iridoviridae; Jasinkavirinae; Kitaviridae; Lavidaviridae; Leishbuviridae; Letovirinae; Leviviridae; Lipothrixviridae; Lispiviridae; Luteoviridae; Malacoherpesviridae; Mammantavirinae; Marnaviridae; Marseilleviridae; Matonaviridae; McCleskeyvirinae;

Mclasvirinae; Medioniviridae; Medionivirinae; Megabirnaviridae; Mesoniviridae; Metaparamyxovirinae; Metaviridae; Microviridae; Mimiviridae; Mononiviridae; Mononivirinae; Mymonaviridae; Myoviridae; Mypoviridae; Nairoviridae; Nanoalphasatelitinae; Nanoviridae; Narnaviridae; Nclasvirinae; Nimaviridae; Nodaviridae; Nudiviridae; Nyamiviridae; Nymbaxtervirinae; Okanivirinae; Orthocoronavirinae; Orthomyxoviridae; Orthoparamyxovirinae; Orthoretrovirinae;

Ounavirinae; Ovaliviridae; Papillomaviridae; Paramyxoviridae; Partitiviridae; Parvoviridae; Parvovirinae; Pclasvirinae; Peduovirinae; Peribunyaviridae; Permutotetraviridae; Phasmaviridae; Phenuiviridae; Phycodnaviridae; Picobirnaviridae; Picornaviridae; Picovirinae; Piscanivirinae; Plasmaviridae; Pleolipoviridae; Pneumoviridae; Podoviridae; Polycipiviridae; Polydnaviridae; Polyomaviridae; Portogloboviridae; Pospiviroidae; Potyviridae; Poxviridae; Procedovirinae; Pseudoviridae; Qinviridae; Quadriviridae; Quinvirinae; Regressovirinae; Remotovirinae; Reoviridae; Repantavirinae; Retroviridae; Rhabdoviridae; Roniviridae; Rubulavirinae; Rudiviridae; Sarthroviridae; Second papillomavirinae; Secoviridae; Sedoreovirinae; Sepvirinae; Serpentovirinae; Simarterivirinae; Siphoviridae; Smacoviridae; Solemoviridae; Solinviviridae; Sphaerolipoviridae; Spinareovirinae; Spiraviridae; Spounavirinae; Spumaretrovirinae; Sunviridae; Tectiviridae; Tevenvirinae; Tiamatvirinae; Tobaniviridae; Togaviridae; Tolecusatellitidae; Tombusviridae; Torovirinae; Tospoviridae; Totiviridae; Tristromaviridae; Trivirinae; Tunavirinae; Tunicanivirinae; Turriviridae; Twortvirinae; Tymoviridae; Variarterivirinae; Vequintavirinae; Virgaviridae; Wupedeviridae; Xinmoviridae; Yueviridae; and Zealarterivirinae.

More preferably, the pathogenic virus is cytomegalovirus (CMV) or Epstein-Barr virus (EBV) or, preferably, a herpes virus, poxvirus, hepadnavirus, influenza virus, coronavirus, hepatitis virus, HIV (human immunodeficiency virus) or bunyavirus.

Most preferably, the virus in question is non-oncolytic, meaning its replication is not specifically restricted to cancer cells.

In an alternative embodiment, said virus is oncolytic, meaning it is capable of infecting and killing cancer cells by selectively replicating in tumor cells rather than normal cells.

In a preferred method, said comparison of said peptide of said linked pMHC (peptide:major histocompatibility complex) with a library of pathogen antigens comprises a plurality of evaluations (peptide affinity evaluation and alignment evaluation and similarity evaluation and MHC binding affinity evaluation) to determine said homology/affinity or identity or similarity. Most preferably, said evaluation is a homology/affinity evaluation as described below.

In one embodiment, a matrix is obtained with rows (or columns) showing the amino acid position in the tumor peptide and columns (or rows) showing each of the 20 standard amino acids, and amino acid positions in the tumor peptide are assigned a high score, and other positions are assigned a low score for similarity to the pathogen antigen/peptide. Tumor peptides with the highest degree of homology/affinity to the pathogen antigen/peptide in terms of (in descending order of preference):

a) the structure of the full-length sequence (obviously, the tumor peptide with 100% identity to the pathogen peptide receives the highest score);

b) the identity of key amino acids at “hot spots” or key binding sites; and

c) the highest number of key amino acids in “hot spots” or key binding sites receive the highest scores.

Alignments are calculated pairwise between peptides, in the query set (pathogen-derived) versus the tumor set, or vice versa. For a given pair of peptides, their alignment is calculated by summing the distance scores between pairs of amino acids at the same position. The score is weighted to prioritize the similarity between more central amino acids in the peptide.

Additionally, MHC class I binding affinity predictions are made. Methods for achieving this are known in the art, such as using NetMHC (NetMHC4.0 or NetMHCpan4.1.) via the IEDB application programming interface (http://tools.iedb.org/main/tools-api/) and then analyzing and comparing in the tool.

In an alternative embodiment of the invention, said comparison involves feeding the software lists of tumor peptides of 8-12 amino acids in length. First, the BLAST tool is used to find "hits" (similar sequences) in the pathogen antigen library. The PAM30 tool is typically used for this task, however, BLOSUM and PAM substitution matrices across several evolutionary distances are supported. Then, a pairwise optimization alignment using at least BLOSUM62 and ideally also the substitution matrix developed by Kim et al (described in BMC Bioinformatics. 2009;10:394. Published 2009 Nov 30. doi:10.1186/1471-2105-10-394 Derivation of an amino acid similarity matrix for peptide: MHC binding and its application as a Bayesian prior. Kim Y, Sidney J, Pinilla C, Sette A, Peters B.) is performed to optimize the alignment in a position-weighted manner. A pair of peptides with high similarity in the TCR (T cell receptor) interaction region (the central region of the peptides) receives a higher similarity score. Finally, MHC binding affinity prediction is performed for both tumor and viral cognate peptides using NetMHC4.0 or NetMHCpan4.1 as a standalone command line tool. The results are ultimately automatically analyzed and compared within the tool, and a final score is generated that summarizes the above analysis.

According to yet another additional aspect of the invention, there is provided an anti-cancer therapeutic agent or immunogenic agent, or an anti-cancer vaccine, comprising a tumor antigen identified using the above device and/or method, or both.

According to a further aspect of the invention, there is provided a tumor antigen for use in anti-cancer therapy, identified using the above device and/or method, or both.

According to a further aspect of the invention, there is provided a tumor antigen for use in the manufacture of a medicament for the treatment of cancer, wherein said antigen is identified using the above device and/or method, or both.

Most preferably, said anti-cancer therapeutic agent or immunogenic agent or anti-cancer vaccine or tumor antigen is one or more of those listed in Tables 1-6.

According to yet another additional aspect of the invention, there is provided a method of treating patients suffering from cancer, comprising:

identifying a tumor antigen using said device and/or method according to the invention and then administering said tumor antigen to an individual or using said tumor antigen to increase the population of T cells active against said tumor antigen and then administering said T cells to the patient.

In a preferred embodiment, said T cells are ex vivo T cells and/or cultured T cells, either allogeneic or autologous T cells.

According to a further additional aspect of the invention, there is provided a pharmaceutical composition or immunogenic agent or vaccine comprising a tumor antigen according to the invention and a pharmaceutically acceptable carrier, adjuvant, diluent or excipient.

Suitable pharmaceutical excipients are well known to those skilled in the art. The pharmaceutical compositions can be prepared for administration by any suitable route, such as intratumoral, intramuscular, intraarterial, intravenous, intrapleural, intravesicular, intracavitary or intraperitoneal injection, buccal, nasal or bronchial (inhalation), transdermal or parenteral routes, and can be prepared by any methods well known in the art of pharmacy.

This composition can be prepared by combining the above-described tumor antigen with a carrier. Typically, the compositions are prepared by uniformly and intimately combining the tumor antigen with liquid carriers or finely divided solid carriers, or both, and then, if necessary, shaping the product. The present invention extends to methods for preparing a pharmaceutical composition comprising bringing the tumor antigen defined herein into combination or association with a pharmaceutically or veterinarily acceptable carrier or diluent.

According to another aspect of the invention, there is provided a combination therapeutic agent for the treatment of cancer, comprising: a tumor antigen identified using the said device and/or method according to the invention, and at least one additional anti-cancer therapeutic agent.

Preferably, said additional anticancer therapeutic agent most suitably comprises cyclophosphamide, since it down-regulates T-regulatory cells. However, as will be understood by those skilled in the art, said additional therapeutic agent may be any anticancer agent known in the art.

Preferably, the additional anticancer therapeutic agent comprises a checkpoint inhibitor (ICI).

The best characterized pathways for checkpoint inhibition are the cytotoxic T lymphocyte protein 4 (CTLA-4) pathway and the programmed cell death protein 1 (PD-1/PD-L1 (PD-1 ligand) pathway. Thus, the present invention can be used in combination with at least one checkpoint modulator, such as anti-CTLA-4 antibody molecules, anti-PD1 antibodies, or anti-PD-L1 antibodies, to neutralize the immunosuppressive tumor environment and induce a strong anti-immune response.

According to a further aspect of the invention, there is provided a method of stratifying patients for treatment of cancer with a checkpoint inhibitor, comprising:

1) taking a tumor sample from the patient;

2) dissolving or suspending the said sample in a liquid;

3) passing said liquid through a device for identifying a tumor antigen, comprising: at least one flow channel containing a plurality of substrates to which at least one molecule or at least one complex are attached, associated with at least one antibody against MHC (major histocompatibility complex) or at least one antibody against human pan-leukocyte antigen (HLA), whereby pMHC (peptide:major histocompatibility complex) in a sample passing through said channel can be extracted from said sample using said at least one antibody;

4) binding with said at least one antibody to at least one pMHC (peptide:major histocompatibility complex) in said sample;

5) optionally extracting said at least one bound pMHC (peptide:major histocompatibility complex) according to part (4) from said device;

6) comparing said peptide of said linked pMHC (peptide:major histocompatibility complex) with a library of human pathogen antigens to determine whether said peptide exhibits sequence homology/affinity with at least one human pathogen antigen or portion thereof, and where there is greater than 60% homology/affinity;

7) identification of said peptide as a tumor antigen; and

8) if said peptide is detected, administering to said patient an effective amount of at least a checkpoint inhibitor (ICI).

According to yet another further aspect or embodiment of the invention, there is provided a method of stratifying patients for treatment of cancer with a checkpoint inhibitor, comprising:

determining whether the patient is CMV seropositive and, if so, selecting the patient for treatment with at least an effective amount of a checkpoint inhibitor (ICI).

According to yet another additional aspect of the invention, there is provided a method of treating cancer comprising:

1) taking a tumor sample from the patient;

2) dissolving or suspending the said sample in a liquid;

3) passing said liquid through a tumor antigen identification device comprising at least one flow channel containing a plurality of substrates to which at least one molecule or at least one complex associated with at least one anti-MHC (major histocompatibility complex) antibody or at least one anti-human leukocyte antigen (HLA) antibody is attached, whereby pMHC (peptide:major histocompatibility complex) in a sample passing through said channel can be extracted from said sample using said at least one antibody;

4) binding with said at least one antibody to at least one pMHC (peptide:major histocompatibility complex) in said sample;

5) optionally extracting said at least one bound pMHC (peptide:major histocompatibility complex) according to part (4) from said device;

6) comparing said peptide of said linked pMHC (peptide:major histocompatibility complex) with a library of human pathogen antigens to determine whether said peptide exhibits sequence homology/affinity with at least one human pathogen antigen or portion thereof, and where there is greater than 60% homology/affinity;

7) identifying said peptide as a tumor antigen; and administering an effective amount of said peptide to said patient to stimulate or activate T cells against the tumor antigen and thus the cancer from which the sample was taken, or using said tumor antigen to increase the population of T cells active against said tumor antigen, and then administering said T cells to the patient.

According to yet another further aspect or embodiment of the invention, there is provided a method of treating cancer comprising:

determining whether the patient is CMV seropositive and, if so, administering to said patient an effective amount of at least a checkpoint inhibitor (ICI).

According to yet another additional aspect of the invention, there is provided a method for stratifying patients for treatment of adenoviral cancer, comprising:

1) taking a tumor sample from the patient;

2) dissolving or suspending the said sample in a liquid;

3) passing said liquid through a device for identifying a tumor antigen, comprising: at least one flow channel containing a plurality of substrates to which at least one molecule or at least one complex are attached, associated with at least one antibody against MHC (major histocompatibility complex) or at least one antibody against human pan-leukocyte antigen (HLA), whereby pMHC (peptide:major histocompatibility complex) in a sample passing through said channel can be extracted from said sample using said at least one antibody;

4) binding with said at least one antibody to at least one pMHC (peptide:major histocompatibility complex) in said sample;

5) optionally extracting said at least one bound pMHC (peptide:major histocompatibility complex) according to part (4) from said device;

6) comparing said peptide of said linked pMHC (peptide:major histocompatibility complex) with a library of human pathogen antigens to determine whether said peptide exhibits homology/affinity with at least one human pathogen antigen or portion thereof, and where there is greater than 60% homology/affinity;

7) identification of said peptide as a tumor antigen; and

8) if said peptide is detected, attaching said peptide to the capsid of an adenoviral vector and administering an effective amount of said adenoviral vector to said patient.

In a preferred embodiment, said peptide is attached to said adenoviral vector using known technology, such as the technology described in WO 2015/177098 and/or contained herein, see PeptiCRAd preparation.

Reference here to “effective amount” refers to an amount that is sufficient to achieve a desired biological effect, such as cancer cell death.

It is understood that the effective dose will depend on the age, sex, health and weight of the recipient, the type of concomitant treatment, if any, the frequency of treatment and the nature of the desired effect. Generally, the effective amount is determined by the person administering the treatment.

Most preferably, the cancer mentioned herein includes any one or more of the following cancers: nasopharyngeal cancer, synovial cancer, hepatocellular carcinoma, renal cancer, connective tissue cancer, melanoma, lung cancer, bowel cancer, colon cancer, rectal cancer, colorectal cancer, brain cancer, throat cancer, oral cancer, liver cancer, bone cancer, pancreatic cancer, choriocarcinoma, gastrinoma, pheochromocytoma, prolactinoma, T-cell leukemia/lymphoma, neuroma, von Hippel-Lindau disease, Zollinger-Ellison syndrome, adrenal cancer, anal cancer, bile duct cancer, bladder cancer, ureteral cancer, oligodendroglioma, neuroblastoma, meningioma, spinal cord tumor, osteochondroma, chondrosarcoma, Ewing's sarcoma, non-cancerous cancer identified primary lesion, carcinoid, gastrointestinal carcinoid, fibrosarcoma, breast cancer, Paget's disease, cervical cancer, esophageal cancer, gallbladder cancer, head cancer, eye cancer, neck cancer, kidney cancer, Wilms' tumor, liver cancer, Kaposi's sarcoma, prostate cancer, testicular cancer, Hodgkin's disease, non-Hodgkin's lymphoma, skin cancer, mesothelioma, multiple myeloma, ovarian cancer, pancreatic endocrine cancer, glucagonoma, parathyroid cancer, penile cancer, pituitary cancer, soft tissue sarcoma, retinoblastoma, small bowel cancer, gastric cancer, thymus cancer, thyroid cancer, trophoblastic carcinoma, chorionic adenoma, uterine cancer, endometrial cancer, vaginal cancer, vulvar cancer, acoustic neuroma, fungating mycosis, insulinoma, carcinoid syndrome, somatostatinoma, gum cancer, heart cancer, lip cancer, meningeal cancer, oral cancer, nerve cancer, palate cancer, parotid cancer, peritoneal cancer, pharyngeal cancer, pleural cancer, salivary gland cancer, tongue cancer, and tonsil cancer.

In a particularly preferred embodiment of the invention, said sample is taken from a melanoma and said tumor antigen is homologous to a CMV antigen or peptide.

In the following claims and in the preceding description of the invention, except where the context otherwise requires by special language or necessary implication, the phrase “comprise” or variations such as “comprises” or “comprising” are used in an inclusive sense, that is, to indicate the presence of the claimed properties, but without preventing the presence or addition of additional properties in various embodiments of the invention.

All references, including any patent or patent application cited in this specification, are incorporated herein by reference. No admission has been made that any reference constitutes prior art. Additionally, no admission has been made that any prior art forms part of the general knowledge in the art.

Preferred features of each aspect of the invention may be the same as those described in connection with any other aspects.

Other features of the present invention will be apparent from the following examples. In general, the present invention extends to any new feature or any new combination of features disclosed in the present specification (including the appended claims and drawings). Thus, it is understood that features, integers, characteristics, compounds or chemical moieties described in connection with a particular aspect, embodiment or example of the invention are applicable to any other aspect, embodiment or example described herein, unless inconsistent therewith.

Furthermore, unless otherwise indicated, any feature disclosed herein may be replaced by an alternative feature serving the same or a similar purpose.

In the description and claims, the singular form includes the plural, unless the context requires otherwise. In particular, when an indefinite article is used, the description should be understood as including the plural form as well as the singular form, unless the context requires otherwise.

The embodiment of the present invention will now be described solely by way of example with reference to the following, where:

Fig. 1. [A] Inside the PeptiCHIP. The PeptiCHIP interior consists of thousands of pillars coated with a linker to which biotin is attached. The biotin is then reacted with streptavidin, which can then react with three molecules of an HLA-specific capture antibody. [B]. Microarray technology as a novel immunopurification platform for rapid antigen detection.

Schematic overview describing the developed new microarray technology. Thiol microarrays comprising free surface thiols were derivatized with biotin-PEG4-alkynethiolene (step 1) and functionalized with a streptavidin layer (step 2), after which biotinylated pan-HLA antibody was immobilized on the surface of the micropillars (step 3) and cell lysate was applied to the microarray (step 4). After appropriate incubation times and washing steps, HLA molecules were eluted by adding 7% acetic acid (step 5).

Fig. 2. Characteristic of the selectivity of microchip functionalization with biotinylated anti-pan-HLA antibody.

A) Binding efficiency of AlexaFluor 488-streptavidin on thiol micropillars pre-coated with biotin-PEG4-alkyne at two different incubation times with streptavidin (15 min and 1 h). B) Effect of streptavidin concentration (non-fluorescent) on the amount of immobilized biotinylated anti-pan-HLA antibody quantified by AlexaFluor 488-labeled secondary antibody. C) Effect of incubation with BSA on the amount of immobilized biotinylated anti-pan-HLA antibody quantified by AlexaFluor 488-labeled secondary antibody. The efficiency of BSA to block non-specific binding sites was assessed by pre-conditioning the micropillar array with BSA before (BSA-panHLA) or after (panHLA-BSA) immobilization of biotinylated anti-pan-HLA antibody. D) Total amount of biotinylated anti-pan-HLA antibody bound on a single chip as a function of loading cycles. A fresh batch of the same (constant) concentration of anti-pan-HLA antibody was used for each cycle. Significance was assessed by two-tailed unpaired Student's t-test, *p < 0.05.

Fig. 3. Properties of the HLA-I peptidomic dataset obtained for the JY cell line. A) Number of unique peptides eluted from 50x10 ⁶ , 10x10 ⁶ and 1x10 ⁶ JY cells. B) Overall peptide length distribution for HLA peptides in the three datasets obtained for the JY cell line. CE) The HLA peptide length distribution is plotted as the number of unique peptides (left y-axis) and percentage frequency (right y-axis) for 50x10 ⁶ (C), 10x10 ⁶ (D) and 1x10 ⁶ (E).

Fig. 4. Fine analysis of HLA ligands isolated from the JY cell line. A) Eluted 9-mers were analyzed for their binding affinity to HLA-A*02:01 and HLA-B*07:02. Binders (green dots) and non-binders (black dots) were determined using NetMHCpan 4.0 server (2% applied rank). B) HLA-I consensus binding motifs. Gibbs cluster analysis was performed to determine consensus binding motifs among the eluted 9-mer peptides. The reference motif is shown in the upper right corner. Clusters with the best fit (higher KLD values, orange star) are shown and the logo sequence is shown with the HLA-I number for each cluster.

Fig. 5A. Flow chart of the HEX algorithm. A matrix was constructed based on the amino acid composition of a tumor peptide (reference peptide). This matrix was then used to scan the viral database and the resulting viral peptides were ranked in order of log recognition probability. Each viral peptide was assigned an alignment score and a MHC-I binding prediction score. Candidate viral peptides were ranked based on the following criteria: MHC-I binding prediction score > alignment score > B-score, and the peptides with the highest scores were analyzed experimentally.

Fig. 5B. Flow chart of the application of the existing software. Lists of tumor peptides of 8-12 amino acids in length can be used as input to the software. First, the BLAST tool is used to find “hits” (similar sequences) in the pathogen antigen library. PAM30 is typically used for this task, however, BLOSUM and PAM substitution matrices across several evolutionary distances are supported. Then, a pairwise optimization alignment using at least BLOSUM62 and ideally also the substitution matrix developed by Kim et al (described in BMC Bioinformatics. 2009;10:394. Published 2009 Nov 30. doi:10.1186/1471-2105-10-394 Derivation of an amino acid similarity matrix for peptide: MHC binding and its application as a Bayesian prior. Kim Y, Sidney J, Pinilla C, Sette A, Peters B.) is performed to optimize the alignment in a position-weighted manner. A pair of peptides with high similarity in the TCR interaction region (the central region of the peptides) receives a higher similarity score. Finally, MHC binding affinity prediction is performed for both tumor and viral cognate peptides using NetMHC4.0 or NetMHCpan4.1 as a standalone command line tool. The results are ultimately automatically analyzed and compared within the tool, and a final score is generated that summarizes the above analysis.

Fig. 6: Immunization with viral peptides homologous to tumor antigens slows tumor growth.

A: Animal experiment design: To evaluate whether tumor peptide-like viral peptides could affect tumor growth, four groups of C57BL6 mice were formed. A group of untreated mice served as a model, and each of the other three groups was immunized with different pools of viral peptides. Mice were immunized at two time points, namely, 14 and 7 days before tumor transplantation.

B: Two weeks after the first immunization, mice were injected subcutaneously with 3x105 ^B16 -OVA murine melanoma cells. After transplantation, tumor growth was monitored by measuring with a digital caliper every other day for nineteen days. P-value was calculated using two-way ANOVA with multiple comparisons with Tukey's correction.

C: Mice were sacrificed after reaching the endpoint. Splenocytes from mice in each group were collected and pooled for ELISpot analysis. Each pool was then challenged with the corresponding viral peptides (TYR1-homologous viral peptides, TRP2-homologous viral peptides, GP100-homologous viral peptides) to assess the response to treatment. The dotted line shows the background level generated by the negative control.

Fig. 7: Viral peptides with higher affinity for MHC are more immunogenic and can induce a stronger cross-reactive response.

A: To assess the response to the corresponding baseline tumor epitope, splenocytes from each immunization group were pooled together and exposed to the corresponding tumor peptide.

B: Comparison between responses elicited by splenocyte exposure to pooled viral peptides and the corresponding parent tumor peptide.

C: Predicted affinity of the parent tumor and the corresponding similar pooled viral peptide pool for mouse MHC class I. 50 nM was considered as the cutoff value for defining “high-” and “intermediate/low affinity” peptides according to the IEDB (Immune Epitope Database) guidelines.

D: Correlation between IFN-γ (interferon gamma) response data and predicted affinity.

E: Stratification of peptides based on their affinity and ability to stimulate IFN-γ production. High-affinity peptides (IC ₅₀ < 50 nM) induced significantly higher IFN-γ production compared to intermediate/low affinity peptides (IC ₅₀ > 50 nM). P-value was calculated using the t-test with Mann-Whitney correction. The range of p-values is indicated by asterisks according to the following criteria: > 0.05 (not significant), ≤ 0.05 (*), ≤ 0.01 (**), ≤ 0.001 (***), ≤ 0.0001 (****).

Fig. 8. Viral peptides homologous to tumor antigens can reduce tumor growth in already formed tumors.

A: Animal experimental design: Four groups of C57BL mice were formed for each tumor cell line tested. Mice were injected subcutaneously with B16-OVA or B16F10 cells on day 0. After the tumor became palpable, mice were treated with saline (model group), bare adenovirus (bare virus group), adenovirus coated with a pool of viral peptides homologous to TRP2 (Viral PeptiCRAd, VPC), or adenovirus coated with TRP2 peptide (TRP2 PeptiCRAd, TPC).

B: Individual tumor growth B16 OVA. The threshold level that determines the success of therapy is identified by the median for all tumor volumes over the past 24 hours.

C: B16 Tumor volume OVA at endpoint. The median tumor volume, shown as a dotted line, defines the threshold for treatment success.

D: The B16 OVA probability plot shows the number of those who react in each treatment group.

E: Individual tumor growth B16 F10. The threshold level that determines the success of therapy is identified by the median for all tumor volumes over the past 24 hours

F: B16 F10 tumor volume at the endpoint. The median tumor volume, shown as a dotted line, defines the threshold level of treatment success.

G: The B16 OVA probability plot shows the number of those who react in each treatment group.

(C-F) P-value was calculated using one-way ANOVA with Tukey correction. The range of p-values was indicated by asterisks according to the following criteria: greater than 0.05 (not significant), less than 0.05 (*), less than 0.01 (**), less than 0.001 (***), less than 0.0001 (****).

(D-G) P-value was calculated using the chi-square test (and Fisher's exact test) of the odds ratio. The range of p-values was indicated by asterisks according to the following criteria: greater than 0.05 (not significant), less than 0.05 (*), less than 0.01 (**), less than 0.001 (***), less than 0.0001 (****).

Fig. 9. T cell cross-reactivity between viral and tumor antigens bearing homology/affinity degree. PBMCs obtained from a patient with HLA-A*02:01 and high serum levels of anti-CMV Ab were exposed to the peptides shown in Table 2. The level of IFN-γ secreted by activated CD8+ T cells was detected by ELISpot assay. The dotted line indicates the noise level resulting from non-specific CTL activation in the negative control (DMSO (dimethyl sulfoxide)) (A). PBMCs obtained from a patient with HLA-A*02:01 and high serum anti-CMV Ab levels and from a healthy donor (HS) who was positive for CMV response were tested for anti-CMV response by ELISpot assay using CMV-specific HLA-A*02:01 truncated peptide NLVPMVATV. P-value was calculated using t-test with Mann-Whitney correction (B).

Fig. 10. Microarray-based platform reveals immunopeptidomic profile in scant tumor biopsies. A) Pre-processed sample mass, total counts, and unique peptides and enrichment in 7-13-mer patterns are summarized. B) Peptide length distributions in absolute counts and percentages are presented as a bar graph.

Fig. 11. Immunopeptidomic analysis of patient-derived organoids (PDO) with ccRCC (clear cell renal cell carcinoma) and bladder cancer. A) Multiple unique peptides detected in PDOs for ccRCC and bladder cancer. B) Peptide length distribution is presented as total number of unique peptides (left y-axis) and percentage of detection (right y-axis) for each PDO (ccRCC upper panel, bladder lower panel).

Fig. 12. Peptide testing. Balb/c mice were injected subcutaneously with the syngeneic CT26 tumor model in the left and right flank (day 0, Fig. 12A). After tumor formation (day 7, Fig. 12A), Valo-mD901 was coated with a pair of each polylysine-modified peptide in our list (PeptiCRAd1, PeptiCRAd2, PeptiCRAd3, Table) and injected intratumorally only in the right tumor. PeptiCRAd4, composed of Valo-mD901, was coated with gp70423-431 (AH1-5), which is a known immunodominant antigen of CT26 derived from an autoantigen encoded in the genome. The model and Valo-mD901 groups were also used as controls. PeptiCRAd 1 and PeptiCRAd2 improved tumor growth control as well as Valo-mD901 in injected lesions (Fig. 12B, right panel), as also shown in single tumor growth for each mouse for each treatment group. In fact, only PeptiCRAd1 significantly improved antitumor activity in untreated tumor, while Valo-mD901 had no effect (Fig. 12B, left panel).

The peptides in PeptiCRAd1 are derived from HEX analysis. PeptiCRAd4 consists of Valo-mD9 coated with gp70 423-431 (AH1-5).

Fig. 13. A schematic representation of the invention is shown. The device according to the invention is shown in the form of a diagram, it was previously functionalized with a plurality of anti-MHC (major histocompatibility complex) antibodies or anti-human pan-leukocyte antigen (HLA) antibodies that are capable of capturing pMHC (peptide:major histocompatibility complex). Tumor lysate is passed through the device and the antibodies capture pMHC. pMHC is eluted and peptides from the pMHC complex are analyzed, for example, by liquid chromatography with mass spectrometry (LC-MS/MS), resulting in a list of tumor peptides. Each tumor peptide is compared with a library of pathogen proteins (including antigens) to determine the level of homology/affinity between each tumor peptide identified using the device and the pathogen proteins (including pathogen antigens/pathogen peptides) in the library. The latter ensures the formation of a list of candidates ideally prioritized in terms of homology/affinity for use in anti-cancer therapy.

Table 1. Peptides used for animal experiments. Known melanoma tumor peptides (TRP2180-188, hGP10025-33, and TYR208-216) were analyzed using HEX. The best viral candidate peptides suggested by the software were selected, and a pool of the best 4 xenopeptides for each original tumor epitope was tested in vivo immunodominantly.

Table 2. Peptides used for ELISpot against patient PBMCs. Known melanoma-associated antigens were analyzed by HEX. The best human CMV-derived candidate peptide for each antigen was selected for in vitro testing.

Table 3. Comparative analysis between microarray-based immunoprecipitation technology and the standard method. The table shows the total amount of antibody deposited by the microarray IP technology and by the standard procedure.

Table 4. Shows the yield after HEX for the identified 13 tumor-restricted MHC peptides with their corresponding pathogen peptides.

Table 5. Table of peptides tested in the ELISPOT assay.

Table 6. Table of selected proteins for the PeptiCRAd in vivo assays shown in Fig. 12.

Materials and methods

Manufacturing of the device

The device according to the invention (known as PeptiCHIP) is a flow-through construct containing thousands of pillars that are coated with a linker to which biotin is attached. The biotin is in turn linked to streptavidin, which is linked to an HLA-specific capture antibody, specifically three molecules of an HLA-specific capture antibody.

Using conventional microfluidic manufacturing methods, PeptiCHIP fabrication is based on a UV-initiated photoreaction between thiol and allyl (“ene”) functional groups according to the following non-stoichiometric ratio of 150:100 (tetrathiol:triallyl).

Immediately after fabrication, PeptiCHIP is derivatized with biotin-PEG4-alkyne (Sigma, 764213) by UV photopolymerization followed by reaction with avidin. This is done as follows.

Step 1: A 1 mM biotin-PEG4-alkyne solution (a stock solution of biotin-PEG4-alkyne in 10 mM ethylene glycol) was prepared. A small aliquot was taken and 1% (w/v) photoinitiator (Igracure ^® TPO-L, BASF) was added using 10% photoinitiator stock in methanol, so the final solution was 1 mM biotin + 1% Lucirin in EG-MeOH 9:1*

The chip was filled with one volume of biotin-PEG4-alkyne solution and 1% Lucirin in EG:MeOH 9:1 (v/v), exposed to UV for 1 min (using LED UV lamp λ=365 nm, l=15 mW/ ^cm2 ).

Rinse thoroughly first with methanol, then with milli-Q water, flush 1-2 ml of each solvent through the channel and store dry (if necessary).

Step 2: After biotin derivatization, the chip was functionalized with streptavidin. A concentrated solution of 0.01 mg/mL streptavidin in PBS was prepared and added to the chip for 15' in the dark at room temperature. Washed 3 times with 200 µL PBS.

BSA 100 μg/ml at 15 mM in PBS was added for 10' at room temperature.

Step 3: Reaction with anti-pan-HLA antibody. 25 µl of anti-human HLA-A,B,C antibody with 1.6 µg/µl biotin (Biolegend CAT. Cat. No. 311434) was added for 15' at room temperature. Then washed three times with 200 µl PBS and then the chip was prepared ready for immunoprecipitation of MHC complex.

Tumor sample preparation: cells were detached with EDTA (ethylenediaminetetraacetic acid) 4 mM and washed once with PBS. 25 µl Igepal 1% in PBS + protease inhibitors were added. Centrifugation was carried out at 500xg 10' +4°C. Centrifugation was carried out at 20000xg 10' +4°C.

Optimized microfluidic pillar arrays

The immunoprecipitation steps were performed in a single microfluidic chip by adding biotinylated pan-HLA antibody to a streptavidin-functionalized solid support structure (i.e., micropillar array) and then immobilizing HLA onto the pan-HLA antibody-coated solid surface.

Finally, non-stoichiometric thiolene (OSTE) polymer micropillar arrays were prepared using UV replica molding technique and biotinylated. Biotinylated micropillars were then functionalized with streptavidin and biotinylated pan-HLA antibody was added, after which the cell lysate was loaded directly onto the microfluidic chip for selective capture of HLA-I complexes. After proper washing, captured HLA-I complexes were eluted at room temperature using 7% acetic acid (Figure 1B). The protocol was then performed according to the standard immunopeptidomics workflow, including purification of eluted HLA peptides using SepPak® ^- C18 in acetonitrile and their evaporation to dryness using vacuum centrifugation.

The efficiency of streptavidin functionalization on micropillar arrays was examined for two different incubation times (15 min and 1 h) with fluorescent AlexaFluor488-streptavidin. It was found that the shorter incubation time was long enough to build the first streptavidin layer (Fig. 2A). Additionally, several concentrations of non-fluorescent streptavidin were tested in the presence of a fixed amount of biotinylated pan-HLA antibody to determine the effect of streptavidin concentration on the final amount of immobilized biotinylated pan-HLA antibody. In this case, biotinylated pan-HLA antibody was incubated for 15 min followed by three washing steps with PBS (200 μl for each step). To determine the amount of immobilized biotinylated pan-HLA antibody at each streptavidin concentration, a fluorescently labeled AlexaFluor488 secondary antibody was used to titrate the immobilized biotinylated pan-HLA antibody. Interestingly, even a 10-fold increase in streptavidin concentration had no effect on the amount of immobilized biotinylated pan-HLA antibody (Fig. 2B), likely resulting from steric hindrance limiting the number of available streptavidin binding sites. Based on this finding, no additional streptavidin concentrations were tested, but the highest streptavidin concentration tested (0.1 mg/mL) was used in all subsequent experiments to ensure maximal binding of the biotinylated pan-HLA antibody. However, to further investigate the selectivity of antibody binding to the streptavidin-functionalized micropillar surface, the effect of an additional bovine serum albumin (BSA) coating step on the amount of immobilized biotinylated pan-HLA antibody was investigated to eliminate non-specific interactions. For this purpose, the micropillar array was preconditioned with BSA (100 μg/mL in 15 mM PBS, incubated for 10 min) after streptavidin functionalization and the subsequent binding efficiency of biotinylated pan-HLA antibody was again determined using a fluorescently labeled secondary antibody. This procedure significantly reduced the amount of immobilized pan-HLA antibody compared to surfaces without preconditioning (Fig. 2C), suggesting that non-specific binding sites can be blocked by a simple BSA pre-incubation step. Therefore, the BSA incubation step was adopted for all further experiments.

Finally, we sought to characterize the maximum amount of immobilized biotinylated pan-HLA antibody that can be bound to a single chip using the optimized protocol. The latter was assessed by using multiple loading cycles of a batch of the new antibody at the same concentration (0.5 mg/mL) onto a single microfluidic chip. In this case, the amount of immobilized pan-HLA antibody was determined by comparing the amount of pan-HLA antibody in the feed solution with that in the output solution using an ELISA assay. It was found that the amount of immobilized antibody increased almost linearly with the number of loading cycles (Figure 2D), allowing for precise adjustment of the total amount of immobilized biotinylated pan-HLA antibody based on the number of loading cycles. After seven cycles, the amount of immobilized antibody reached approximately 45 μg, which is sufficient, at least theoretically, for immunopeptidomic study of a scarce amount of biological material, since 10 mg of pan-HLA (3.88x10 ¹⁶ antibody molecules) is required according to the prior art methods to study a cell count of 10 ⁹ [22 A] (Table 3).

Using a microchip setup, it is possible to immobilize 1.74x10 ¹⁴ antibody molecules and technically it is possible to study 4.5x10 ⁶ cells.

Microarray-based antigen enrichment, implemented in the immunopeptidomics workflow, enables the identification of naturally occurring HLA-I peptides

To evaluate whether the developed thiol-ene microarray can be used as an antigen detection platform, immunopurification of HLA peptides from the human lymphoblastoid B cell line JY was performed. The JY line has a high expression of HLA class I and is homozygous for three alleles common in the human population (HLA-A*02:01, HLA-B*07:02, and HLA-C*07:02), and it has been widely accepted for ligand analysis. Therefore, the JY cell line was considered as a suitable model to test the microarray-based immunoprecipitation antigen enrichment technology.

Therefore, HLA-I complexes were immunoaffinity purified using thiol-ene microarrays functionalized with pan-HLA antibody amount as described above. Furthermore, to determine the sensitivity of our proposed approach, the protocol was tested using low total cell numbers such as ^50x106 , ^10x106 and ^1x106 . Lysates were loaded onto microarrays and after adequate washing with PBS, peptides were eluted with 7% acetic acid and analyzed by tandem mass spectrometry. The entire workflow from streptavidin functionalization to tumor peptide elution took less than 24 hours on average. A stringent false discovery rate threshold of 1% for peptide and protein identification was used to obtain high confidence data. The authors of the invention managed to identify 5589, 2100 and 1804 unique peptides from 50x10 ⁶ , 10x10 ⁶ and 1x10 ⁶ cells, respectively (Fig. 3A).

Because we sought to thoroughly analyze the ability of the microarray technology to enrich for natural HLA-I binders and avoid potential co-eluting contaminants, we extensively characterized the eluting peptides. First, the eluting peptides from the JY cell line represented a typical distribution over the length of the ligandome dataset, with 9-mers representing the most enriched peptide species (Fig. 3B-E). Next, the predicted binding affinity was determined for the two HLA-I alleles (HLA-A*0201 and HLA-B*0702) expressed in JY cells. JY cells also have a low level of the HLA-C*0702 allele, but the binding motif overlaps with the HLA-A*0201 and HLA-B*0702 motifs; therefore, only these alleles were considered in the subsequent analysis.

Of the unique 9-mers, 78%, 83%, and 67% were predicted to bind (described as binding in NetMHCpan4.0, with a 2% [24A-26A] cutoff) to the HLA-A*0201 or HLA-B*0702 alleles for ^50x106 , ^10x106 , and ^1x106 cells, respectively (Fig. 4A). In addition, Gibbs analysis was performed to deconvolute the consensus motifs binding to the corresponding HLA-I alleles from the eluted 9-mer peptides; they clustered into two distinct groups with a preference for reduced amino acid complexity for residues at positions P2 and Ω, significantly overlapping with those known for HLA-A*0201 and HLA-B*0702 (Fig. 4B).

Next, in order to determine the role of the identified peptides, gene ontology (GO) enrichment analysis was performed on our derived list of 9-mer binding parent proteins. We found enrichment of nuclear and intracellular proteins, mainly those interacting with DNA, RNA or involved in catabolic activity. Finally, we developed an in vitro killing assay to further demonstrate the feasibility of the microarray technology for isolating peptides in complex with HLA-I. For this, a set of three peptides was selected from our derived JY dataset to stimulate HLA-matched PBMCs; CD8+ T cells were purified from PBMCs and taken up as effector cells in co-culture with JY cells. To account for non-specific cytotoxicity mediated by the effector cells themselves, unstimulated PBMCs were used as a control. Cytolysis was then monitored in real time. Interestingly, CD8+ T cells exposed to peptides QLVDIIEKV (SEQ ID NO: 75; gene name PSME3) and KVLEYVIKV (SEQ ID NO: 76; gene name MAGEA1) showed approximately 10% specific cytolysis, whereas CD8+ T cells exposed to peptide ILDKKVEKV (SEQ ID NO: 77; gene name HSP90AB3P) showed 15% specific cytolysis, indicating specific lysis in the presence of certain peptides.

To assess the validity of our HLA-I peptide lists identified using microarray technology, we examined SysteMHC, a repository of immunopeptide data generated using mass spectrometry. Among the unique 9-mer binding proteins identified in our data, 69%, 77%, and 81% were also found in a previously published ligandome dataset generated for the JY cell line (PRIDE ID PXD000394) [3A] for 50x10 ⁶ , 10x10 ⁶ , and 1x10 ⁶ cells, respectively (Fig. 6A). Furthermore, a positive correlation between source protein abundance and HLA representation was confirmed, with the most abundant proteins being the major source of HLA peptides.

Therefore, these results demonstrated that the chip-based protocol can be used as a reliable immunoprecipitation platform within the immunopeptidomic workflow.

Using the device

Application of sample to chip and elution of fractions for subsequent analysis

The sample was applied for several cycles and incubated for 5 minutes (at 4°C). Washed 3 times with 200 μl PBS and aspirated the last wash to empty the chip. A solution of 50% MeOH and 50% MilliQ was prepared. Using the previous solution, a 7% acetic acid solution was prepared. This solution was applied to the chip and the fraction was collected. The elution time was 5 minutes. The collected fractions were purified on the same day. A SepPak cartridge for each tissue sample was prepared for HLA-I peptide sampling and labeled. Using a syringe and a dedicated adapter, the cartridge was first washed with 1 ml of 80% ACN (acetonitrile) in 0.1% TFA (trifluoroacetic acid), then twice with 1 ml of 0.1% TFA. Each of the biological samples was applied to the SepPak cartridge. They were passed slowly (rate of approximately 1 ml for 20 s). The cartridges were washed twice with 1 ml of 0.1% TFA. HLA-binding peptides were eluted into a collection tube with 300 µl of 30% ACN in 0.1% TFA.

HEX (Homology Evaluation of Xenopeptides)

Homology (/affinity) evaluation of xenopeptides (HEX) is a novel in silico platform that compares the similarity between tumor peptides (reference peptides) and pathogen-derived peptides, such as viral ones (peptides of interest). It uses multiple parameters to accelerate the selection of candidate peptides. This is done by incorporating both new methods (peptide scoring and alignment scoring algorithm) and integrating pre-existing methods (MHC-I binding prediction). HEX is accompanied by multiple pre-compiled databases of known proteins, such as proteins derived from viral pathogens and the human proteome (33).

An associated scoring matrix was generated ad hoc based on the amino acid composition of the reference peptide as opposed to the experimental one. Specifically, in the matrix rows representing the amino acid position in the peptide and the columns representing each of the 20 standard amino acids, the amino acid positions of the reference peptide were assigned the same high score, and the other positions were assigned the same low score.

Alignments are calculated pairwise between peptides in the query set compared to a reference set. For a given pair of peptides, their alignment is calculated by summing the distance scores between pairs of amino acids at the same position. The score is weighted to prioritize similarity between more central amino acids in the peptide. HEX supports BLOSUM and PAM substitution matrices at several evolutionary distances. In particular, BLOSUM 62 was the matrix of choice for this study.

MHC class I binding affinity predictions were made using NetMHC [27] via the IEDB application programming interface (http://tools.iedb.org/main/tools-api/) and then analyzed and compared within the tool. The user can specify the desired scoring method or return a set of recommended results. Predictions for multiple human and mouse MHC-I alleles are supported.

Users can select peptides based on their own criteria or select peptides according to a random forest model. The random forest was trained on experimental results for peptides selected by the inventors. Feature importance was determined by out-of-set (OOB) mean squared error (MSE) augmentation and cross-validated on an unseen sample of peptides. HEX was developed as a web application using the R Shiny package and is available at https://picpl.arcca.cf.ac.uk/hex/app/ without user registration. The source code is available at https://github.com/whalleyt/hex.

In an alternative embodiment of the invention, HEX supports both BLOSUM and PAM substitution matrices across multiple evolutionary distances. In particular, BLOSUM 62 was the matrix used in this study. HEX first performs a BLAST search using PAM30 to find "hits" (similar sequences) in the reference library. HEX then performs a pairwise refinement alignment using both BLOSUM62 and the substitution matrix developed by Kim et al.

Reference: Kim Y, Sidney J, Pinilla C, Sette A, Peters B. Derivation of an amino acid similarity matrix for peptide: MHC binding and its application as a Bayesian prior. BMC Bioinformatics. 2009;10:394. Published 2009 Nov 30. doi:10.1186/1471-2105-10-394.

MHC class I binding affinity predictions were generated using NetMHC4.0 (or NetMHCpan4.1) as a standalone command line tool and then analyzed and compared within the tool. The user can specify the desired scoring method or return multiple recommended results. Predictions for multiple human and mouse MHC-I alleles are supported.

Patients and samples

A total of 16 patients with stage 4 metastatic melanoma were treated with anti-PD1 monoclonal antibody at the Comprehensive Cancer Center, Helsinki University Central Hospital (HUCH). Patients were randomly assigned to receive nivolumab infusions (n = 7) every other week or pembrolizumab infusions (n = 9) every third week. The study was approved by the Ethics Committee of HUCH (Dnro 115/13/03/02/15). Written informed consent was obtained from all patients and the study was conducted in accordance with the Declaration of Helsinki.

Peripheral blood samples (3 ml EDTA blood, 50 ml heparinized blood) were collected at three time points; before treatment, after one and three months of treatment. From these, plasma was separated by centrifugation and then stored at -70°C. CMV and EBV IgG levels were measured in thawed EDTA plasma samples using VIDAS CMV IgG (BioMérieux, Marcy-l'Etoile, France) and Siemens Enzygnost Anti-EBV/IgG kits (Siemens Healthcare Diagnostics, Marburg, Germany). Immunoglobulins (IgA, IgM, IgG) in thawed heparinized plasma were measured at the Helsinki University Central Hospital Central Laboratory (HUSLAB).

Cell lines and human samples

The murine melanoma cell line B16-F10 was purchased from the American Type Culture Collection (ATCC; Manassas, VA, USA). Cells were grown in RPMI (Gibco, Thermo Fisher Scientific, US) with 10% fetal bovine serum (FBS) (Life Technologies), 1% glutamax (Gibco, Thermo Fisher Scientific, US), and 1% penicillin and streptomycin (Gibco, Thermo Fisher Scientific, US) at 37°C/5% CO ₂ .

The B16-OVA cell line, a murine melanoma cell line modified to constitutively express chicken ovalbumin (OVA), was kindly provided by Professor Richard Vile (Mayo Clinic, Rochester, MN, USA). The cells were grown in low-glucose RPMI (Gibco, Thermo Fisher Scientific, US) with 10% FBS (Gibco, Thermo Fisher Scientific, US), 1% Glutamax, 1% penicillin and streptomycin (Gibco, Thermo Fisher Scientific, US), and 1% Geneticin (Gibco, Thermo Fisher Scientific, US) at 37°C/5% CO ₂ .

All cells were tested for mycoplasma contamination using a commercially available detection kit (Lonza - Basel, Switzerland). Isolated human PBMCs were frozen in FBS supplemented with 10% DMSO and then maintained in liquid nitrogen until use. Cryopreserved PBMCs were thawed and maintained overnight at 37°C/5% _CO2 in complete RPMI medium supplemented with 10% FBS, 1% glutamax, 1% penicillin-streptomycin before being applied to the ELISPOT plate.

Peptides

All peptides used in this study were purchased from Zhejiang Ontores Biotechnologies Co. (Zhejiang, China) 5 mg, purity greater than 90%. The sequences of all peptides used in this study can be found in Tables 1 and 2.

Preparation of PeptiCRAd

All PeptiCRAd complexes described in this work were prepared by mixing adenoviruses and poly-K-tailed peptides according to the following protocol: ^1x109 v.p. (virus particles) was mixed with 20 μg poly-K-tailed peptides (resuspensioned in water); after vortexing, the mixture was incubated at room temperature for 15 min; PBS was added after incubation to the injection volume (50 μg/mouse), then the solution was vortexed again and used for assays or for injection into animals. For TRP2-PeptiCRAd, 1x109 v.p. (virus particles) was mixed with 20 μg poly-K-tailed peptides (resuspension in water); after vortexing, the mixture was incubated at ^room temperature for 15 min; PBS was added after incubation to the injection volume (50 μg/mouse), then the solution was vortexed again and used for assays or for injection into animals. were mixed with 20 μg of 6K-TRP2 _180-188 peptide, whereas Viral-PeptiCRAd was prepared using ^1x109 v.p. mixed with 5 μg of each viral 6K peptide homologous to TRP2 _180-188 .

New PeptiCRAds were prepared prior to each experiment using fresh reagents. All virus and peptide dilutions required prior to incubation for PeptiCRAd preparation were performed in sterile PBS or water. PeptiCRAds were then diluted in the required assay buffer.

Viruses were obtained, propagated and characterized as described in [20].

Animal Experiments and Ethical Approvals

All animal experiments were reviewed and approved by the Animal Experimentation Committee of the University of Helsinki and the local authorities of Southern Finland.

All experiments were performed using C57BL/6JOlaHsd mice obtained from Scanbur (Karlslunde, Denmark).

For the immunization experiment, immunocompetent female C57BL/6J mice aged 8-9 weeks were divided into 4 groups. N = 3 mice were used as the model group, n = 7 mice were used to form each of the three different treatment groups. Each treatment group was vaccinated with a different group of xenopeptides. Mice were vaccinated twice and injections were performed at an interval of one week (days 0 and 7) at the base of the tail with 40 μg of peptides and 40 μg of adjuvant (VacciGrade poly(I:C) - Invivogen) in a final injection volume of 100 μl. Naïve mice (injected with PBS) were used as the model group. On day 14, mice were injected with 3* ¹⁰⁵ B16-OVA cells in the right flank and tumor growth was monitored until the end point.

To treat established tumors, the authors tested two different tumor cell lines: B16-OVA cells and the more aggressive B16F10 cells. 3*10 ⁵ B16-OVA cells and 1*10 ⁵ B16-F10 cells were injected subcutaneously into the right flank of immunocompetent female C57BL/6J mice aged 8-9 weeks. These mice were then randomly divided into 4 groups of 7-8 mice for each tumor cell line. The model group was treated with PBS; the second group was treated with uncoated adenovirus; the third group was treated with adenovirus coated with TRP2 _180-188 (TRP2-PeptiCRAd); the last group was treated with adenovirus coated with viral peptides homologous to TRP2- (Viral-PeptiCRAd).

Mice were treated intratumorally twice and injections were given at two day intervals (days 10 and 12 after tumor transplantation) and tumor growth was monitored until the endpoint was reached. The median tumor volume measurement on the last day identifies the threshold level of therapeutic success, represented by the dotted line. Mice that showed tumor volume below the threshold level at the endpoint were considered responders, whereas mice showing tumor volume above the threshold level were considered nonresponders.

Tumor growth was monitored using a digital caliper measuring two dimensions of the tumor. The volume was then mathematically calculated according to the following formula:

((long side) × (short side) ² )/2

In all experiments, tumors were measured every second or third day until tumor size reached the acceptable maximum, and mice were then sacrificed and spleens were collected.

ELISpot analysis

To assess the number of active antigen-specific T cells, interferon-γ (IFN-γ) secretion was measured using the ELISPOT assay from IMMUNOSPOT (CTL, Ohio USA) for murine IFN-γ and MABTECH (Mabtech AB, Nacka Strand, Sweden) for human IFN-γ.

Fresh mouse splenocytes collected at the endpoint of the experiment were used. The method was performed according to the manufacturer's instructions. Briefly, for mouse IFN-γ, 3*10 ⁵ splenocytes/well were seeded into the plate on day 0. Cells were stimulated with 2 μg peptides/well. After 3 days of incubation at 37°C/5% CO _2, plates were developed according to the kit protocol.

For human IFN-γ ELISPOT, human PBMC were thawed and stored overnight at 37°C/5% _CO2 in complete medium. On the following day, ^3x105 PBMC/well were seeded and stimulated with 2 μg/well of peptides. After 48 h of incubation at 37°C/5% CO2 _, plates were developed according to the manufacturer’s protocol. Plates were sent to CTL-Europe GmbH for analysis.

Cell line and reagents

The EBV-transformed human lymphoblastoid B-cell line JY (ECACC HLA-type collection, Sigma Aldrich) was grown in RPMI 1640 (GIBCO, Invitrogen, Carlsbad, CA, USA) supplemented with 1% GlutaMAX (GIBCO, Invitrogen, Carlsbad, CA, USA) and 10% heat-inactivated fetal bovine serum (HI-FBS, GIBCO, Invitrogen, Carlsbad, CA, USA).

Streptavidin (Streptomyces avidinii, affinity purified, lyophilized from 10 mM potassium phosphate, at least 13 U/mg protein) was purchased from Sigma-Aldrich (Saint Louis, Missouri, USA).

Biotin-conjugated anti-HLA-A, B, C w6/32 clone was purchased from Biolegend (San Diego, CA, USA) for analysis.

The following peptides purchased from Ontores Biotechnologies Co., Ltd. were used in the study: KVLEYVIKV (SEQ ID NO: 75; gene name MAGE A1), ILDKKVEKV (SEQ ID NO: 76; gene name HSP90), and QLVDIIEKV (SEQ ID NO: 77; gene name PSME3).

In addition, the following peptides were purchased from Chempeptide (Shangai, China): VIMDALKSSY (SEQ ID NO: 78; gene name NNMT), FLAEGGGVR (SEQ ID NO: 79; gene name FGA), and EVAQPGPSNR (SEQ ID NO: 80; gene name HSPG2).

Ovarian tumor biopsy and ethical considerations

Ovarian tumor biopsies were collected from a patient suffering from metastatic ovarian tumor (high serous grade) who completed informed consent, in the course of studies approved by the Research Ethics Committee of the North Savo Hospital District under approval number 350/2020. The samples were cut into small pieces and treated with disruption buffer containing 1 mg/ml collagenase type D (Roche), 100 μg/ml hyaluronidase (Sigma Aldrich) and 1 mg/ml DNase I (Roche) for 1 h at 37°C. The cell suspension was then passed through 500 μm and 300 μm cell strainers (pluriSelect) to obtain single cells.

Renal cell carcinoma and bladder tumor samples and ethical considerations

Patient tissue samples for organoid cultures were obtained from the DEDUCER (Development of Diagnostics and Treatment of Urological Cancers) study at Helsinki University Central Hospital under approval number HUS/71/2017, 26.04.2017, ethics committee approval number 15.03.2017 Dnro 154/13/03/02/2016, and with patient consent. The kidney sample was obtained from nephrectomy of an adult male with clear cell renal cell carcinoma (ccRCC, pTNM stage pT3a G2). A benign tumor tissue sample was used for the experiments. Urothelial carcinoma (bladder cancer, high grade, stage III, 1x1 cm) was obtained from an adult female and the cancer tissue sample was used for organoid culture.

Organoid culture of clear cell renal cell carcinoma and bladder tumor

Cells were isolated from the original tissue immediately after surgery by dissociating the tissue into small pieces and treating them with collagenase (40 units/mL) for 2-4 h. Benign and cancerous kidney cells from patients suffering from clear cell renal cell carcinoma were grown as organoids in F medium [3:1 (v/v) F-12 (Ham) nutrient mixture - DMEM (Dulbecco's modified Eagle's medium) (Invitrogen), 5% FBS, 8.4 ng/mL cholera toxin (Sigma), 0.4 μg/mL hydrocortisone (Sigma), 10 ng/mL epidermal growth factor (Corning), 24 μg/mL adenine (Sigma), 5 μg/mL insulin (Sigma), 10 μM ROCK inhibitor (Y‐27632, Enzo Life Sciences, Lausen, Switzerland) and 1% penicillin-streptomycin with 10% Matrigel (Corning). Bladder tumor-derived organoids were grown in hepatocyte calcium medium (Corning)[15A] supplemented with 5% CSFBS (Thermo Fisher Scientific), 10 μM Y‐27632 RHO inhibitor (Sigma), 10 ng/ml epidermal growth factor (Corning), 1% GlutaMAX (Gibco), 1% penicillin-streptomycin and 10% Matrigel. 6x10 ⁶ cells were collected by centrifugation, washed in PBS to remove Matrigel and snap frozen before assays.

HLA typing

Clinical HLA typing of tumor samples (ccRCC and bladder) was performed in the European Federation of Immunogenetics (EFI)-accredited HLA laboratory at the Finnish Red Cross Blood Service. Allelic determination of the three classical HLA-I genes HLA-A, -B and -C was performed by targeted PCR-based next-generation sequencing (NGS) according to the protocol proposed by the manufacturer (NGSgo ^® Workflow, GenDx, Utrecht, The Netherlands).

Allele annotation at 4-field resolution was performed using NGSengine Version: 2,11,0,11444 (GenDx, Utrecht, The Netherlands) using the IPD IMGT/HLA database version 3.33.0, https://www.ebi.ac.uk/ipd/imgt/hla/.

Flow cytometry analysis

The following antibodies were used to analyze cell surface expression of HLA-A2 and HLA-A, B, and C: PE-conjugated anti-HLA-A2 (clone BB7.2, BioLegend 343306 San Diego, CA, USA), PE-conjugated anti-human HLA-A, B, and C (clone W6/32, BioLegend 311406, San Diego, CA, USA), and Human TruStain FcX block (BioLegend B247182, San Diego, CA, USA).

Data were acquired using a BDLSR Fortessa flow cytometer. Flow cytometric analysis of renal cell carcinoma and bladder tumor organoids was performed using a BD Accuri 6 plus (BD Biosciences) and analyzed using FlowJo software (Tree Star, Ashland, OR, USA).

Results

Development of a homology xenopeptide (HEX) evaluation tool for the identification of viral and tumor peptides with high molecular mimicry

To investigate whether molecular mimicry between viruses and tumors can affect tumor growth, we needed to identify peptides with high homology/affinity. However, there is no tool to facilitate the identification of the appropriate target in this region. Therefore, we developed HEX (Homology Xenopeptide Score), which is a device that compares input sequences with sequences from a pathogen antigen/peptide database or protein sequences and selects highly homologous candidate peptide pairs based on at least two of the following three criteria: 1) B-score, which corresponds to the probability of peptides being recognized by a given TCR; 2) position-weighted alignment score to prioritize for similarity in the TCR interaction region; 3) MHC class I binding affinity prediction (Fig. 5A). Alternatively, conventional software may be used as shown in Fig. 5B and as described above.

We started with three melanoma-associated antigens that have been successfully used in a number of vaccination studies: TRP2 _180-188 (tyrosinase-related protein 2), GP100 _25-33 (aka PMEL; premelanosome protein), and TYR1 _208-216 (tyrosinase 1). TYR1 _208-216 is not predicted to bind to mouse MHC and is thus considered an “off target.” Using HEX, we identified viral peptides that have high homology/affinity to the input tumor epitopes. Pools of 4 viral peptides per input tumor epitope (Table 1) were selected for additional in vivo evaluation.

Antiviral immunity controls tumor growth through molecular mimicry of tumor antigens

To assess whether viral peptides would affect tumor growth, we simulated viral infection by immunizing C57BL6 mice with selected pools of viral peptides followed by tumor transplantation (Fig. 6A). All immunized mice showed a reduction in tumor growth compared to the model group. In addition, significant differences were observed between the treatment groups. Tumor growth was most reduced in mice immunized with a pool of viral peptides homologous to TRP2 or gp100 (Fig. 6B).

To further investigate the contribution of selected viral peptides to tumor growth reduction, we collected splenocytes from mice at the endpoint for ELISpot analysis (Fig. 6C). Splenocytes from mice immunized with pools of viral peptides homologous to TRP2 or gp100 promoted a higher IFN-γ response compared to splenocytes from mice immunized with control epitopes homologous to TYR1, in correlation with tumor growth outcome.

We further examined the reactivity of these splenocytes from virus-pre-immunized mice to their respective cognate tumor antigen (Fig. 7A). In a pairwise comparison, only TRP2-homologous viral peptides demonstrated higher IFN-γ secretion compared to the cognate tumor peptide (Fig. 7B). We further retrospectively assessed the affinities of each tumor antigen and their homologous viral pools for C57BL/6J MHC class I molecules (Fig. 7C) and found a significant correlation with their respective ability to stimulate IFN-γ secretion (Fig. 7D). According to the Immune Epitope Database (IEDB) guidelines, we divided the peptides into high- and low-affinity peptides using a cutoff of 50 nM (http://tools.iedb.org/mhci/help/) and found that high-affinity MHC-I peptides promoted higher INF-γ production compared to low-affinity MHC-I peptides (Fig. 7E). Taken together, these results support the predictive power of the HEX tool to identify homologous peptides and highlight that molecular mimicry between viruses and tumors plays a role in the antitumor immune response via cross-reactive T cells.

Viral Epitopes Highly Similar to Tumor Epitopes Reduce Established Melanoma Growth in Vivo

We demonstrated that molecular mimicry between viral and tumor antigens can influence tumor growth in pre-immunized mice. We next wanted to assess whether a molecular mimicry-directed response could influence established tumors in naive mice. To do so, mice were implanted with B16OVA tumors or the more aggressive and immunosuppressive B16F10 tumors and then treated with the previously developed PeptiCRAd vaccine platform (20) to mimic viral infection (Fig. 8A). Briefly, PeptiCRAd is a vaccine technology consisting of adenoviruses coated with MHC-I restricted peptides bearing positively charged polyamino acid tail moieties. We continued to use the most effective peptide pool from the first in vivo experiment. Vaccines were prepared by coating adenoviruses with the original TRP2 epitope _180-188 (TRP2-PeptiCRAd) or a corresponding pool of TRP2-like viral peptides (viral PeptiCRAd). Intratumoral injections of PeptiCRAd significantly reduced tumor development compared with saline or uncoated virus treatment in both B16OVA (Fig. 8B-C) and B16F10 tumors (Fig. 8E-F). Mice treated with the original tumor antigen or viral homologs showed significantly higher numbers of responders in both tumor models (Fig. 8D, 8G). In both tumor models, the authors found a significant reduction in tumor growth when mice were treated with viral peptides similar to tumor peptides, suggesting again that molecular mimicry may play a fundamental role in antitumor immunity involving cross-reactive T cells.

Investigation into whether molecular mimicry between CMV and tumor antigens may explain improved prognosis

We selected a pool of melanoma-associated proteins [21] that were compared with the CMV proteome using HEX, generating a list of tumor peptides with high similarity to CMV (Table 2). The tumor peptides and their CMV analog were used in an ELISPOT assay, showing that in a responding patient, PBMCs always responded to both viral and their tumor analog antigens, suggesting that CMV infection expanded viral T cell clones that can attack and kill tumor cells, making seropositive patients more prone to respond to melanoma-specific CMV-like epitopes (Fig. 9A, B). Although this observation was enticing, it had the limitation of not directly showing whether viral and tumor peptides expanded the same T cell clones.

Novel chip-based microfluidic platform identifies immunopeptidomic profile in scant tumor biopsy tissue

We critically evaluated the platform for studying a scant tumor biopsy. Thus, a metastatic ovarian tumor (high serous grade) was collected from a patient and four pieces were obtained from the tumor border (S1, S2, S3, and S4); the central part of the tumor (S5) was also collected. The samples were then weighed and, as summarized in Fig. 10A, the average size was between 0.01 g and 0.06 g. After sample disruption, the resulting single cell suspensions were lysed and passed through a microarray. Using a strict false positive rate cutoff of 1% for protein and peptide identification, 916, 695, 172, 1128, and 256 unique peptides were identified in S1, S2, S3, S4, and S5, respectively (Fig. 10A). Consistent with the typical ligandome profile, overall enrichment (above 70%) was observed in 7-13-mer samples (Figure 10A). In terms of absolute quantity and percentage of amino acid length distribution, 9-mer samples were the most representative (Figure 10B), confirming our analysis and other previous immunopeptidomic analysis. We then further examined the parent proteins found in our data using gene ontology (GO) enrichment analysis. Consistent with the typical ligandome profile, metabolic pathways were enriched in all samples examined. Furthermore, the analysis revealed enrichment of skin developmental pathway proteins consistent with the epithelial nature of the serous ovarian tumor analyzed here. Overall, these results highlighted the feasibility of using the developed microfluidic chip platform to analyze scant tumor biopsy and generate personalized antigenic peptide for tumor treatment.

A microarray-based protocol reveals the immunopeptidomic landscape in patient-derived organoids.

The microarray technology was critically evaluated using only ^6x106 cells from patient-derived organoids (PDOs). The inventors selected two patients from an ongoing precision medicine study for urologic cancers, a nephrectomy specimen containing both benign and cancerous tissue from a patient suffering from clear cell renal cell carcinoma (ccRCC) and a 1x1 cm sample from a patient suffering from bladder cancer were further processed as 3D primary organoid cultures.

Using the developed microarray technology and a stringent false positive rate of 1% for peptide and protein identification, we were able to identify a total of 576 and 2089 unique peptides in ccRCC and bladder PDO, respectively (Fig. 11A). The amount of peptides recovered differed between the two samples, with bladder samples yielding more peptides than ccRCC samples. It is well known that HLA expression influences the amount of HLA-I peptides recovered [22A], and consistent with this, flow cytometry analysis revealed higher surface levels of HLA-A, HLA-B, and HLA-C in bladder than in ccRCC samples, explaining the different yields of peptides recovered between the samples. Peptide analysis demonstrated a preference for 9-12-mers (56.4% in ccRCC and 47.9% in bladder tumors) with an enrichment in the 9-mer population, consistent with the typical length distribution of ligandome assays [33] (Fig. 11B). To distinguish between HLA-I binders and contaminants, Gibbs clustering and NetMHC4.0 analysis were performed. First, 9-mer deconvolution demonstrated that 55% and 67% in bladder and ccRCC PDO, respectively, matched at least one of the patient HLA alleles. NetMHC4.0 was then applied to all 9-mers identified in our dataset. Among them, 46% and 69% were predicted to bind to patient-specific HLAs. The parent proteins present in both our datasets were then examined. For this purpose, gene ontology (GO) enrichment analysis was performed. In line with previous observations and our published data, both samples showed enrichment in intracellular and nuclear proteins that interact with RNA and are involved in catabolic/metabolic processes.

To demonstrate that the technology can be used to rapidly develop therapeutic cancer vaccines, we developed a killing assay. We focused on the analysis of ccRCC samples. We used transcriptome levels to select putative tumor antigens using PBMC and healthy kidney tissue as reference sets. PBMC from healthy volunteers were then exposed to the selected peptides, and CD8+ T cells isolated from these cells were used in the assay. T cells exposed to the peptide EVAQPGPSNR (HSPG2 gene name) showed approximately 10% specific cytolysis.

Finally, we sought to investigate the T cell response in a patient suffering from ccRCC. To do so, unfractionated PBMCs obtained from the patient were stimulated in vitro with the EVAQPGPSNR peptide, while unstimulated PBMCs were used as controls. The derived CD8+ T cells were then added to ccRCC PDO, showing approximately a 7% increase in killing activity compared to the control group.

Peptides from patient organoids have been shown to have therapeutic efficacy in in vivo studies

The list of peptides was analyzed by HEX. First, the software prioritized peptides that both had strong binding properties (IC ₅₀ cutoff range of 50 nM-500 nM according to NetMHC4.0) and demonstrated a higher weighted alignment score (0.8-1 cutoff of the normalized weighted alignment score). The latter focuses the peptide similarity on the interaction region that is most likely to interact with the TCR in CD8+ T cells to induce a mediated immune response; the resulting peptides were then further categorized based on their overall percent identity to pathogen antigens/peptides and IC ₅₀ binding affinity score. The final output consisted of thirteen peptides with similar pathogenic peptides (Table 4).

To determine the immunogenicity of the peptide, mice were pre-immunized with subcutaneous injections of each single peptide in the presence of poly(I:C) adjuvant; a group of mice were injected with poly(I:C) alone or also with saline as a control. Splenocytes from these mice were collected and tested for IFNγ production upon specific stimulation (Table 5) in an ELISpot assay.

We next sought to test whether the candidate peptides could be used as a cancer vaccine to treat established tumors. To do so, we used PeptiCRAd, a previously developed cancer vaccine platform that combines oncolytic adenoviruses with polylysine-modified peptides attached to the capsid (via a polylysine linker). The adenovirus used here is VALO-mD901, which has been genetically modified to express murine OX40L and CD40L, and has previously been shown to induce tumor control and a systemic antitumor response in a mouse melanoma model. Thus, Balb/c mice were injected subcutaneously with the syngeneic CT26 tumor model in the left and right flank (day 0, Fig. 12A). When tumors were formed (day 7, Fig. 12A), Valo-mD901 was coated with a pair of each polylysine-modified peptide from our list (PeptiCRAd1, PeptiCRAd2, PeptiCRAd3, Table 6) and intratumorally injected into the right tumor only. PeptiCRAd4 consisted of Valo-mD901 coated with gp70423-431 (AH1-5). The model group and Valo-mD901 group were also used as controls. PeptiCRAd1 and PeptiCRAd2 improved tumor growth control as well as Valo-mD901 in the injected lesions (Fig. 12B, right panel). Favorably, PeptiCRAd1 improved antitumor growth control in the untreated tumor, in contrast to Valo-mD901, which had no effect.

Conclusion

HEX is a bioinformatics tool that analyzes peptide sequences and compares them to a curated database of viral pathogen proteomes in search of sequences with high similarity.

Since the presented peptides are mainly involved in interaction with anchor residues in MHC [25], our software returns an alignment score that is positionally weighted to prioritize similarities occurring in the central part of the peptide, i.e., the position that is most involved in interaction with the TCR [26]. Furthermore, to increase the chances that our proposed target is the presented epitope, we ranked the obtained peptides according to binding affinity for the selected MHCs using in one example the IEDB NetMHC API prediction tool [27]; or NetMHC4.0 or NetMHCpan4.1b as standalone tools.

The authors used HEX to identify viral peptides homologous to three widely characterized tumor-associated antigens.

The inventors found that pre-immunization with inventor-selected pools of viral peptides mimicked the antiviral immune status prior to tumor formation and effectively slowed the growth of subcutaneously injected melanoma tumor cells in mice, indicating that pre-exposure to viral peptides can influence tumor growth.

The inventors then found that tumor-homologic viral peptides had a similar effect on established tumors when administered during tumor progression, indicating that viral infection occurring during tumor progression can still influence tumor growth.

Together, the authors' findings suggest that molecular mimicry between viral and tumor antigens may exert control over tumor growth without relying on prior exposure or pre-acquired immunity.

The new ICPIs significantly improved patient survival in several solid tumors, especially metastatic melanoma, compared with other commonly used therapies such as radiotherapy and chemotherapy. However, despite the improved survival and effective response rates, it is still unknown why some patients did not benefit from the ICPI therapies. Our results showed that patients with high titers of CMV-specific IgG levels had significantly longer progression-free survival.

The inventors found that PBMCs from patients with high titers of anti-CMV IgG reacted with melanoma antigens similar to CMV peptides. These data indicate that CMV seropositivity promotes melanoma-specific T cell immunity and thus provides a clinical benefit for these patients undergoing ICPI therapy.

Here, the inventors demonstrated that viral infections can influence tumor growth and clearance. In addition, the inventors demonstrated cross-reactivity of cytotoxic T cells against viral and homologous tumor antigens selected using HEX. Based on the results obtained by the inventors, the inventors concluded that the phenomenon of molecular mimicry can be used in the future to develop new treatment methods or, when combined with immunotherapy methods, potentially enhances the antitumor immune effect achieved by these treatment methods.

In addition, the development of effective tumor eradication and protection strategies requires reliable identification of tumor peptides that bind to HLA-I. Direct identification of peptides from the HLA-I complex still represents the best approach. However, the immunopeptidomics process is relatively complex and thus represents a major bottleneck in the antigen discovery process. Currently, the inability to analyze immunopeptidomes from small amounts of biological materials (e.g., tissue needle biopsy), sample processing, cost, and the affinity matrix (which is labor-intensive and expensive to manufacture) adopted in the conventional platform have been identified as the major technical challenges to be addressed.

In this work, the authors addressed several technical issues that hinder ligandome studies, focusing on the limited availability of assay material, the cost of consumables, and lengthy protocols.

By exploiting the well-characterized biotin-streptavidin interaction to immobilize biotinylated pan-HLA antibody on streptavidin-functionalized surfaces, we were able to replace the conventional technology based on affinity arrays generated by cross-linking reactions with a microchip platform. The limitations of material scarcity (e.g., needle biopsy) prompted the work to implement a microfluidic protocol. In this work, we used a custom microchip protocol encompassing a thiolene polymer-based micropillar array as a solid support for additional biofunctionalization, which allowed the entire IP procedure to be performed on a single microfluidic chip.

Testing of the identified peptides in an in vitro killing assay confirmed that the peptides identified in the present invention were indeed expressed on the surface of JY cells, as they were killed in a CD8+ T cell-specific manner. The peptide ILDKKVEKV (SEQ ID NO: 3) found in our data set resulted in a higher percentage of specific cytolysis.

Thus, the authors' approach offers an unexplored tool for immunoaffinity purification.

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Claims (57)

1. A method for identifying a tumor-specific antigen, including: 1) dissolving or suspending a tumor sample in a liquid; 2) passing said liquid through a microfluidic device for identifying a tumor-specific antigen, comprising: at least one flow channel containing a plurality of micropillars arranged in a matrix, to which at least one molecule or at least one complex are attached, associated with at least one antibody against a major histocompatibility complex or at least one antibody against a human pan-leukocyte antigen, whereby a peptide:major histocompatibility complex (pMHC) in a sample passing through said channel can be extracted from said sample using said at least one antibody; 3) binding with said at least one antibody to at least one pMHC in said sample; 4) extracting said at least one bound pMHC according to part (3) from said device; 5) comparing said peptide of said linked pMHC with a library of pathogen antigens to determine whether said peptide exhibits homology/affinity with at least one pathogen antigen or portion thereof, and where there is greater than 60% homology/affinity; 6) identification of the said peptide as a tumor-specific antigen for use in anticancer therapy. 2. The method according to claim 1, wherein said library of pathogen antigens comprises a verified library of known pathogen antigens. 3. The method according to claim 1 or 2, wherein said pathogen antigens are antigens of human pathogens. 4. The method according to any one of paragraphs 1-3, wherein said pathogen antigens are viruses. 5. The method according to any one of claims 1-4, wherein said pathogen antigens are obtained from at least one virus selected from the group consisting of: Abyssoviridae; Ackermannviridae; Actantavirinae; Adenoviridae; Agantavirinae; Aglimvirinae; Alloherpesviridae; Alphaflexiviridae; Alphaherpesvirinae; Alphairidovirinae; Alphasatellitidae; Alphatetraviridae; Alvernaviridae; Amalgaviridae; Amnoonviridae; Ampullaviridae; Anelloviridae; arenaviridae; Arquatrovirinae; Arteriviridae; Artoviridae; Ascoviridae; Asfarviridae; Aspiviridae; Astroviridae; Autographivirinae; Avsunviroidae; Avulavirinae; Bacilladnaviridae; Baculoviridae; Barnaviridae; Bastillevirinae; Bclasvirinae; Belpaoviridae; Benyviridae; Betaflexiviridae; Betaherpesvirinae; Betairidovirinae; Bicaudaviridae; Bidnaviridae; Birnaviridae; bornaviridae; Botourmiaviridae; Brockvirinae; Bromoviridae; Bullavirinae; Caliciviridae; Calvusvirinae; Carmotetraviridae; Caulimoviridae; Ceronivirinae; Chebruvirinae; Chordopoxvirinae; Chrysoviridae; Chuviridae; Circoviridae; Clavaviridae; Closteroviridae; Comovirinae; Coronaviridae; Corticoviridae; Crocarterivirinae; Cruliviridae; Crustonivirinae; Cvivirinae; Cystoviridae; Dclasvirinae; Deltaflexiviridae; Densovirinae; Dicistroviridae; Endoraviridae; Entomopoxvirinae; Equarterivirinae; Eucampyvirinae; Euroniviridae; Filoviridae; Fimoviridae; First papillomavirinae; Flaviviridae; Fuselloviridae; Gammaflexiviridae; Gammaherpesvirinae; Geminialphasatelitinae; Geminiviridae; Genomoviridae; Globuloviridae; Gokushovirinae; Guernseyvirinae; Guttaviridae; Hantaviridae; Hepadnaviridae; Hepeviridae; Herelleviridae; Heroarterivirinae; Herpesviridae; Hexponivirinae; Hypoviridae; Hytrosaviridae; Iflaviridae; Inoviridae; Iridoviridae; Jasinkavirinae; Kitaviridae; Lavidaviridae; Leishbuviridae; Letovirinae; Leviviridae; Lipothrixviridae; Lispiviridae; Luteoviridae; Malacoherpesviridae; Mammantavirinae; Marnaviridae; Marseilleviridae; Matonaviridae; McCleskeyvirinae; Mclasvirinae; Medioniviridae; Medionivirinae; Megabirnaviridae; Mesoniviridae; Metaparamyxovirinae; Metaviridae; Microviridae; Mimiviridae; Mononiviridae; Mononivirinae; Mymonaviridae; Myoviridae; Mypoviridae; Nairoviridae; Nanoalphasatelitinae; Nanoviridae; Narnaviridae; Nclasvirinae; Nimaviridae; Nodaviridae; Nudiviridae; Nyamiviridae; Nymbaxtervirinae; Okanivirinae; Orthocoronavirinae; Orthomyxoviridae; Orthoparamyxovirinae; Orthoretrovirinae; Ounavirinae; Ovaliviridae; Papillomaviridae; Paramyxoviridae; Partitiviridae; Parvoviridae; Parvovirinae; Pclasvirinae; Peduovirinae; Peribunyaviridae; Permutotetraviridae; Phasmaviridae; Phenuiviridae; Phycodnaviridae; Picobirnaviridae; Picornaviridae; Picovirinae; Piscanivirinae; Plasmaviridae; Pleolipoviridae; Pneumoviridae; Podoviridae; Polycipiviridae; Polydnaviridae; Polyomaviridae; Portogloboviridae; Pospiviroidae; Potyviridae; Poxviridae; Procedovirinae; Pseudoviridae; Qinviridae; Quadriviridae; Quinvirinae; Regressovirinae; Remotovirinae; Reoviridae; Repantavirinae; Retroviridae; Rhabdoviridae; Roniviridae; Rubulavirinae; Rudiviridae; Sarthroviridae; Second papillomavirinae; Secoviridae; Sedoreovirinae; Sepvirinae; Serpentovirinae; Simarterivirinae; Siphoviridae; Smacoviridae; Solemoviridae; Solinviviridae; Sphaerolipoviridae; Spinareovirinae; Spiraviridae; Spounavirinae; Spumaretrovirinae; Sunviridae; Tectiviridae; Tevenvirinae; Tiamatvirinae; Tobaniviridae; Togaviridae; Tolecusatellitidae; Tombusviridae; Torovirinae; Tospoviridae; Totiviridae; Tristromaviridae; Trivirinae; Tunavirinae; Tunicanivirinae; Turriviridae; Twortvirinae; Tymoviridae; Variarterivirinae; Vequintavirinae; Virgaviridae; Wupedeviridae; Xinmoviridae; Yueviridae; and Zealarterivirinae, including any combination thereof. 6. The method according to any one of claims 1-5, wherein said pathogen antigen is a cytomegalovirus antigen, or an Epstein-Barr virus antigen, or a herpes virus antigen, or a poxvirus antigen, or a hepadnavirus antigen, or an influenza virus antigen, or a coronavirus antigen, or a hepatitis virus antigen, or an HIV (human immunodeficiency virus) antigen, or a bunyavirus antigen. 7. The method of any one of claims 1-6, wherein comparing said peptide of said linked pMHC with a library of pathogen antigens comprises evaluating the peptide and/or evaluating the alignment to determine said homology/affinity. 8. The method according to any one of claims 1-7, wherein comparing said peptide of said linked pMHC with a library of pathogen antigens comprises determining: a) similarity or identity of the structures of the full-length sequence of the said peptide and the said pathogen antigens; and/or b) similarity or identity of key amino acids in key binding sites of the said peptide and the said pathogen antigens; and/or c) the greatest number of similar or identical key amino acids in the key binding sites of the specified peptide and the specified pathogen antigens. 9. A device for identifying a tumor-specific antigen, comprising: a microfluidic device comprising at least one flow channel comprising a plurality of micropillars arranged in a matrix to which at least one molecule or at least one complex is attached, associated with at least one anti-major histocompatibility complex antibody or at least one anti-human leukocyte antigen (HLA) antibody, whereby a peptide:major histocompatibility complex (pMHC) in a sample passing through said channel can be extracted from said sample using said at least one antibody; and a processor adapted to identify said peptide bound to said major histocompatibility complex and compare said peptide of said bound pMHC with a library of peptide antigens of pathogens to determine whether said peptide that is bound or has been bound to said major histocompatibility complex exhibits homology/affinity with at least one pathogen antigen or part thereof, and where there is greater than 60% homology/affinity; determining said peptide that is bound or has been bound to said major histocompatibility complex as a tumor-specific antigen for use in anti-cancer therapy. 10. The device of claim 9, wherein said anti-pan-HLA antibody is selected from the group comprising: MHC class I A, B and C. 11. The device of claim 9, wherein said anti-pan-HLA antibody is selected from the group consisting of: MHC class II DP, DM, DO, DQ and DR. 12. The device according to any one of claims 9-11, wherein said antibody is anti-human. 13. A device according to any one of claims 9-12, wherein said molecule or complex is a complex of a thiol and an alkene functional group. 14. The device according to any one of claims 9-13, wherein said molecule or complex comprises biotin and streptavidin. 15. The device according to any one of claims 9-14, wherein said molecule or complex comprises more than one antibody bound to streptavidin. 16. The device of any one of claims 9-15, wherein said molecule or complex is conditioned by coating with a protein, such as bovine serum albumin, prior to binding to said antibody. 17. A device according to any one of paragraphs 9-16, wherein said device is adapted to implement the method according to any one of paragraphs 1-8. 18. A method for stratifying patients for cancer treatment with a checkpoint inhibitor, comprising: 1) taking a tumor sample from the patient; 2) dissolving or suspending the said sample in a liquid; 3) passing said liquid through a microfluidic device for identifying a tumor-specific antigen, comprising: at least one flow channel containing a plurality of micropillars arranged in a matrix, to which at least one molecule or at least one complex are attached, associated with at least one antibody against a major histocompatibility complex or at least one antibody against a human pan-leukocyte antigen, whereby a peptide:major histocompatibility complex (pMHC) in a sample passing through said channel can be extracted from said sample using said at least one antibody; 4) binding with said at least one antibody to at least one pMHC in said sample; 5) extracting said at least one bound pMHC according to part (4) from said device; 6) comparing said peptide of said linked pMHC with a library of human pathogen antigens to determine whether said peptide exhibits homology/affinity with at least one human pathogen antigen or portion thereof, and where there is greater than 60% homology/affinity; 7) identification of said peptide as a tumor-specific antigen; and 8) if said peptide is detected, administering to said patient an effective amount of at least one checkpoint inhibitor. 19. A method for stratifying patients for treatment of adenoviral cancer, including: 1) taking a tumor sample from the patient; 2) dissolving or suspending the said sample in a liquid; 3) passing said liquid through a microfluidic device for identifying a tumor-specific antigen, comprising: at least one flow channel containing a plurality of micropillars arranged in a matrix, to which at least one molecule or at least one complex are attached, associated with at least one antibody against a major histocompatibility complex or at least one antibody against a human pan-leukocyte antigen, whereby a peptide:major histocompatibility complex (pMHC) in a sample passing through said channel can be extracted from said sample using said at least one antibody; 4) binding with said at least one antibody to at least one pMHC in said sample; 5) extracting said at least one bound pMHC according to part (4) from said device; 6) comparing said peptide of said linked pMHC with a library of human pathogen antigens to determine whether said peptide exhibits homology/affinity with at least one human pathogen antigen or portion thereof, and where there is greater than 60% homology/affinity; 7) identification of said peptide as a tumor-specific antigen; and 8) if said peptide is detected, attaching said peptide to the capsid of an adenoviral vector and administering an effective amount of said adenoviral vector to said patient. 20. A method of treating cancer, comprising: 1) taking a tumor sample from the patient; 2) dissolving or suspending the said sample in a liquid; 3) passing said liquid through a microfluidic device for identifying a tumor-specific antigen, comprising: at least one flow channel containing a plurality of micropillars arranged in a matrix, to which at least one molecule or at least one complex are attached, associated with at least one antibody against a major histocompatibility complex or at least one antibody against a human pan-leukocyte antigen, whereby a peptide:major histocompatibility complex (pMHC) in a sample passing through said channel can be extracted from said sample using said at least one antibody; 4) binding with said at least one antibody to at least one pMHC in said sample; 5) extracting said at least one bound pMHC according to part (4) from said device; 6) comparing said peptide of said linked pMHC with a library of human pathogen antigens to determine whether said peptide exhibits homology/affinity with at least one human pathogen antigen or portion thereof, and where there is greater than 60% homology/affinity; 7) identification of said peptide as a tumor-specific antigen; and 8) administering an effective amount of said peptide to said patient to stimulate or activate T cells against a tumor-specific antigen and thus the cancer from which the sample was taken; or using said tumor-specific antigen to increase the population of T cells active against said tumor-specific antigen, and then administering said T cells to said patient. 21. The method according to any of paragraphs. 19, 20, wherein said cancer is selected from the group consisting of: nasopharyngeal cancer, synovial cancer, hepatocellular carcinoma, kidney cancer, connective tissue cancer, melanoma, lung cancer, bowel cancer, colon cancer, rectal cancer, colorectal cancer, brain cancer, throat cancer, oral cavity cancer, liver cancer, bone cancer, pancreatic cancer, choriocarcinoma, gastrinoma, pheochromocytoma, prolactinoma, T-cell leukemia/lymphoma, neuroma, von Hippel-Lindau disease, Zollinger-Ellison syndrome, adrenal cancer, anal cancer, bile duct cancer, bladder cancer, ureteral cancer, oligodendroglioma, neuroblastoma, meningioma, spinal cord tumor, osteochondroma, chondrosarcoma, Ewing's sarcoma, cancer of unknown primary, carcinoid, gastrointestinal carcinoid, fibrosarcoma, breast cancer, Paget's disease, cervical cancer, esophageal cancer, gallbladder cancer, head cancer, eye cancer, neck cancer, kidney cancer, Wilms' tumor, liver cancer, Kaposi's sarcoma, prostate cancer, testicular cancer, Hodgkin's disease, non-Hodgkin's lymphoma, skin cancer, mesothelioma, multiple myeloma, ovarian cancer, pancreatic endocrine cancer, glucagonoma, parathyroid cancer, penile cancer, pituitary cancer, soft tissue sarcoma, retinoblastoma, small bowel cancer, gastric cancer, thymus cancer, thyroid cancer, trophoblastic carcinoma, chorionadenoma, uterine cancer, endometrial cancer, vaginal cancer, vulvar cancer, acoustic neuroma, mycosis fungoides, insulinoma, carcinoid syndrome, somatostatinoma, gum cancer, heart cancer, lip cancer, meningeal cancer, oral cancer, nerve cancer, palate cancer, parotid cancer, peritoneal cancer, pharyngeal cancer, pleural cancer, salivary gland cancer, tongue cancer, and tonsil cancer.