Patent Description:
CD8+ T-cells have a key role in the detection and elimination of cells that present abnormal peptides on their surface as a result of pathogenic infection, such as viral infection, or malignant transformation. The cross-reactivity of the T-cell receptor (TCR) means T-cells can recognize an incredibly large variety of different target peptides. This phenomenon allows a relatively small number of T-cells to recognize multiple pMHC (peptide:Major Histocompatibility Complex) molecules that are representative of abnormal cells and so potentially health or life threatening.

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

Indeed, it has been thought that the best prognostic markers for successful outcome in cancer immunotherapeutic treatment is a high tumor antigen mutational rate and abundant T-cell infiltration. This thinking is based upon the fact that a tumor that has a high number of mutations will have a higher chance of being recognized and eliminated by infiltrating T-cells.

Nevertheless, some studies have shown that the qualitative properties of tumor antigens might be more important than their quantity. Furthermore, it has been observed that anti-viral T-cells populate the tumor microenvironment [<NUM>], however whether their role is active or just bystander is still unclear. Additionally, <NPL>, teach a cancer vaccine based on the similarity of a tumour peptide with a viral peptide, using an in silico platform, to engage a wide range of of lymphocyties. and HLA-affinity and sequence homology are evaluated.

Onco-immunology and immunotherapy have completely changed the way cancer is being treated over the last decade, particularly when using checkpoint inhibitors (ICIs) where outstanding clinical results have been seen but, unfortunately, only in a small number of patients. It is becoming clear that immunotherapy with ICIs will only fully work when targeting specific tumor antigens. However, at the moment there is not an easy and rapid method to identify these tumor antigens.

Herein, we hypothesized that tumors might present peptides that share a high degree of homology scoring with pathogen-derived peptides, in particular, viral peptides and this might enable pathogenically-generated, cross-reactive T-cells to recognize and kill tumor cells.

To this end, we have developed a bioinformatic device called HEX (Homology Evaluation of Xenopeptides) to automatically and very simply identify tumor antigens that are highly similar to viral peptides. Using this device, we have observed that anti-viral T-cell immunity, mediated by a peptide with high homology scoring with a cancer antigen, can also actively control tumor growth in both a prophylactic and a therapeutic setting. This observation demonstrates the cross-reactivity of activated T-cells against both homologous viral-derived peptides and tumor-derived peptides.

Subsequently, we have also found that a humoral response to cytomegalovirus (CMV) was able to stratify the response of melanoma patients' to checkpoint inhibitor therapy (anti-PD1). Indeed, peptides homologous with CMV and melanoma, identified through use of the device HEX, were found to foster Inf-g release in Pheripheral Blood Mononuclear Cells (PBMCs) from CMV seropositive melanoma patients.

The invention protected herein is as described in the accompanying claims and reproduced below.

According to a first aspect of the invention there is provided a device for tumour-specific antigen identification comprising:.

Reference herein to a anti-pan-HLA antibody is to an antibody that recognises or has specificity for any of the different HLAs present in mammals, in particular humans.

More ideally still, said antibody recognises 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 recognises 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.

Yet more preferably still said antibody is anti-human.

In a preferred embodiment of the invention said molecule or complex is a complex of thiol and alkyl ("ene") functional groups, ideally, involving the stoichiometric ratio of <NUM> to <NUM> within the range of <NUM>:<NUM>-<NUM>:<NUM> (tetrathiol: triallyl).

More ideally still, the fabrication of the device is based on UV-initiated photoreaction between the thiol and alkyl ("ene") functional groups. In yet a further preferred embodiment of the invention the device is made by UV photopolymerization with biotin-PEG4-alkyne (Sigma, <NUM>). This is followed by reaction with avidin: streptavidin. Although, it is within the scope of the invention to attach the avidin to the biotic before attaching the biotic to the pillar and vice versa.

In still yet a further preferred embodiment said device is functionalised by reacting said antibody with said streptavidin. Ideally, more than one antibody is reacted with said streptavidin whereby each complex functionalised with streptavidin carries a plurality, such <NUM> or <NUM>, of said antibodies such as a plurality of anti-pan-HLA antibodies.

Typically, although not claimed, the micropillar array was preconditioned with protein, such as bovine serum albumin (BSA) (ideally <NUM>µg/mL in <NUM> PBS, <NUM> incubation) after streptavidin functionalization and before antibody binding.

Typically, although not claimed, said micropillars are made of a same or similar composition to that which is used in conventional microfluidic devices e.g. for implementing immobilized enzyme reactions.

According to a further aspect of the invention there is provided a method for tumour-specific antigen identification comprising:.

In a preferred embodiment of any aspect of the invention said homology may be any one of the following percentages: <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM><NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM> and <NUM>%.

Reference herein to homology includes reference to a comparison of sequence structures in part v) above using one of: homology as defined herein; or identity which is determined by the number of identical residues over a defined 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 defined length in a given alignment.

In a preferred method of the invention said library of human pathogen-derived antigens comprises a curated library of known pathogenic antigens, ideally derived from pathogenic viruses, or more suitably the proteomes thereof, which viruses infect mammals, ideally humans, such as any one or more, or ideed 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; Endornaviridae; Entomopoxvirinae; Equarterivirinae; Eucampyvirinae; Euroniviridae; Filoviridae; Fimoviridae; Firstpapillomavirinae; Flaviviridae; Fuselloviridae; Gammaflexiviridae; Gammaherpesvirinae; Geminialphasatellitinae; 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; Nanoalphasatellitinae; 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; Secondpapillomavirinae; 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 antigen is a cytomegalovirus (CMV) antigen or an Epstein-Barr virus (EBV) antigen or, preferably, a Herpesvirus antigen, a Poxvirus antigen, a Hepadnavirus antigen, an Influenza virus antigen, a Coronavirus antigen, a Hepatitis virus antigen, a HIV antigen, or a Bunyavirus antigen.

Most preferably said virus is non-oncolytic i.e. its replication is not specifically restricted to cancer cells.

In an alternative embodiment, said virus is oncolytic i.e. capable of infecting and killing cancer cells by selective replication in tumour versus normal cells.

In a preferred method said comparing said peptide of said bound pMHC (peptide:Major Histocompatibility Complex) with a library of pathogenic antigens involves a plurality of scorings (peptide scoring and alignment scoring and similarity scoring and MHC-binding affinity scoring) to determine said homology or identity or similarity. Most preferably, said scoring is an homology scoring as described below.

In one embodiment, a matrix of rows (or columns) representing the amino acid position in the tumour peptide and columns (or rows) representing each of the <NUM> standard amino acids is generated and the amino acid positions of the tumour peptide are assigned the same high score and other positions are assigned the same low score as the pathogen-derived antigen/peptide. Tumour peptides with the highest degree of homology with a pathogen-derived antigen/peptide in terms of (in descending order of preference):.

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

Additionally, MHC class I binding affinity predictions are made. Methods for achieving same are known in the art such as using NetMHC (NetMHC4. <NUM> or NetMHCpan4. ) via the Application programming interface of IEDB (http://tools. org/main/tools-api/) and are then parsed and collated within the tool.

In an alternative embodiment of the invention, said comparing involves inputting lists of tumor peptides of <NUM> to <NUM> amino acides in length into the software. Firstly, the tool BLAST is employed to find Hits (similar sequences) in the library of pathogen-derived antigens. For this task the PAM30 tool is generally used, however both BLOSUM and PAM substitution matrices across several evolutionary distances are supported. Next, a pairwise refinement alignment using at least BLOSUM62 and, ideally also, a substitution matrix developed by <NPL> <NPL>. ) is performed in order to refine the alignment in a position-weighted fashon. A pair of peptides with high similarity in the TCR-interacting area (the central section of the peptides) receive a higher similarity score. Finally, a MHC-binding affinity prediction can be produced for both the tumor and the viral cognate peptide using NetMHC4. <NUM> or NetMHCpan4. <NUM> as standalone command line tools. The results are ultimately automatically parsed and collated within the tool and a final score which summarize the above mentioned analysis is created.

The invention also concerns a checkpoint inhibitor (ICI).

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

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

Reference herein to an "effective amount", is one that is sufficient to achieve a desired biological effect, such as, cancer cell death.

It is understood that the effective dosage will be dependent upon the age, sex, health, and weight of the recipient, kind of concurrent treatment, if any, frequency of treatment, and the nature of the effect desired. Typically, the effective amount is determined by those administering the treatment.

Most preferably the cancer referred to herein includes any one or more of the following cancers: nasopharyngeal cancer, synovial cancer, hepatocellular cancer, renal cancer, cancer of connective tissues, 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, ureter cancer, oligodendroglioma, neuroblastoma, meningioma, spinal cord tumor, osteochondroma, chondrosarcoma, Ewing's sarcoma, cancer of unknown primary site, carcinoid, carcinoid of gastrointestinal tract, fibrosarcoma, breast cancer, Paget's disease, cervical cancer, esophagus cancer, gall bladder 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, endocrine pancreatic cancer, glucagonoma, parathyroid cancer, penis cancer, pituitary cancer, soft tissue sarcoma, retinoblastoma, small intestine cancer, stomach cancer, thymus cancer, thyroid cancer, trophoblastic cancer, hydatidiform mole, uterine cancer, endometrial cancer, vagina cancer, vulva cancer, acoustic neuroma, mycosis fungoides, insulinoma, carcinoid syndrome, somatostatinoma, gum cancer, heart cancer, lip cancer, meninges cancer, mouth cancer, nerve cancer, palate cancer, parotid gland cancer, peritoneum cancer, pharynx cancer, pleural cancer, salivary gland cancer, tongue cancer and tonsil cancer.

In the claims which follow and in the preceding description of the invention, except where the context requires otherwise due to express language or necessary implication, the word "comprise", or variations such as "comprises" or "comprising" is used in an inclusive sense i.e. to specify the presence of the stated features but not to preclude the presence or addition of further features in various embodiments of the invention.

No admission is made that any reference constitutes prior art. Further, no admission is made that any of the prior art constitutes part of the common general knowledge in the art.

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

Other features of the present invention will become apparent from the following examples. Generally speaking, the invention extends to any novel one, or any novel combination, of the features disclosed in this specification (including the accompanying claims and drawings). Thus, features, integers, characteristics, compounds or chemical moieties described in conjunction with a particular aspect, embodiment or example of the invention are to be understood to be applicable to any other aspect, embodiment or example described herein, unless incompatible therewith.

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

An embodiment of the present invention will now be described by way of example only with reference to the following wherein:.

The device of the invention (known as PeptiCHIP) is a flow-through construction containing thousands of pillars that are coated with a linker to which is attached the Biotin. Biotin is, in turn, connected to streptavidin which is bound with a HLA-specific capturing antibody, specifically, three molecules of HLA-specific capturing antibody.

Using conventional microfluidic manufacturing techniques, the fabrication of the PeptiCHIP is based on UV-initiated photoreaction between thiol and allyl ("ene") functional groups in according to the following off-stoichiometric ratio of <NUM>:<NUM> (tetrathiol: triallyl).

Immediately after the afore fabrication, the PeptiCHIP is derivatized with the biotin-PEG4-alkyne (Sigma, <NUM>) by UV photopolymerization followed by reaction with avidin. This is undertaken as follows.

Step <NUM>: We prepared a <NUM> biotin-PEG4-alkyne solution (stock of biotin-PEG4-alkyne in <NUM> in ethyleglycol). Wev tooke a small aliquot and added <NUM>% (m/v) photoinitiator (Igracure® TPO-L, BASF), using <NUM>% photoinitiator stock in methanol, thus, the final solution is <NUM> biotin + <NUM>% Lucirin in EG-MeOH <NUM>:<NUM>*
We filled the chip with one volume of the solution of biotin-PEG4-alkyne and <NUM>% Lucirin in EG:MeOH <NUM>:<NUM> (v/v), expose to UV for <NUM> (using LED UV lamp λ=<NUM>, l=<NUM> mW/cm<NUM>).

We rinsed thoroughly first with methanol, then with milli-Q water, we flushed <NUM>-<NUM> of each solvent through the channel and stored it dry (if necessary).

STEP <NUM>: After the derivation with biotin, the chip is functionalized with streptavidin. We prepared a stock of streptavidin of <NUM>/ml in PBS and added it to the CHIP for <NUM>' in the dark at room temperature. We washed <NUM> times with 200ul of PBS.

We added BSA 100ug/ml in <NUM> in PBS for <NUM>' at room temperature.

STEP <NUM>: Reaction with anti-pan-HLA antibody. We added <NUM> ul of biotin anti-human HLA-A,B,C <NUM> ug/ul (Biolegend CAT. Number <NUM>) for <NUM>' at room temperature. We then washed three times with <NUM> ul of PBS and then the CHIP is ready for the immunoprecipitation of the MHC complex.

Tumor sample preparation: we detached the cells with EDTA <NUM> and washed once with PBS. Add 25ul of Igepal <NUM>% in PBS + protease inhibitors. Centrfuge 500xg <NUM>' +<NUM>. Cnetrifuge 20000xg <NUM>' +<NUM>.

Immune-purification steps were carried out within a single microfluidic chip by adding a biotinylated pan-HLA antibody to a streptavidin-prefunctionalized solid support structure (i.e., the micropillar array) and then immobilizing the HLA onto the pan-HLA antibody coated solid surface.

In summary, off-stoichiometric thiol-enes (OSTEs) polymer based micropillar arrays were fabricated with a UV-replicamolding technique and biotinylated. Next, the biotinylated micropillars were functionalized with streptavidin and the biotinylated pan-HLA antibody was added, after which the cell lysate was loaded directly into the microfluidic chip to selectively trap the HLA-I complexes. After adequate washing, the trapped HLA-I complexes were eluted at room temperature by applying <NUM>% acetic acid (<FIG>). Thereafter, the protocol proceeded according to the standard immunopeptidomics workflow, including purification of the eluted HLA peptides with SepPak®-C18 in acetonitrile and evaporating them to dryness by using vacuum centrifugation.

The efficiency of the streptavidin functionalization on micropillar arrays was examined with respect to two different incubation times (<NUM> and <NUM>), with the help of fluorescent AlexaFluor488-streptavidin. The shorter incubation time was found sufficiently long for building the first streptavidin layer (<FIG>). Moreover, to determine the effect of the streptavidin concentration on the final amount of immobilized biotinylated pan-HLA antibody, several concentrations of non-fluorescent streptavidin were tested in the presence of a fixed amount of the biotinylated pan-HLA antibody. In this case, the biotinylated pan-HLA antibody was incubated for <NUM> followed by three washing steps with PBS (<NUM>µl for each step). To quantitate the amount of the immobilized biotinylated pan-HLA antibody at each streptavidin concentration, a fluorescent-labelled AlexaFluor488 secondary antibody was used to titer the immobilized biotinylated pan-HLA antibody. Interestingly, even a <NUM>-fold increase in the streptavidin concentration did not much affect the amount of immobilized biotinylated pan-HLA antibody (<FIG>), which likely resulted from steric hindrances limiting the number of available streptavidin binding sites. On the basis of this finding, no further concentrations of streptavidin were explored, but the highest streptavidin concentration tested (<NUM>/mL) was used in all subsequent experiments to ensure maximal binding of the biotinylated pan-HLA antibody. However, to further investigate the selectivity of the antibody binding onto the streptavidin functionalized micropillar surface, the impact of an additional coating step with bovine serum albumin (BSA) on the amount of immobilized biotinylated pan-HLA antibody was examined with a view to eliminate nonspecific interactions. To this end, the micropillar array was preconditioned with BSA (<NUM>µg/mL in <NUM> PBS, <NUM> incubation) after streptavidin functionalization and the efficiency of subsequent binding of the biotinylated pan-HLA antibody was again determined with the help of the fluorescent-labelled secondary antibody. This procedure substantially reduced the amount of immobilized pan-HLA antibody in comparison to the non-preconditioned surfaces (<FIG>) suggesting that nonspecific binding sites could be blocked with a simple BSA preincubation step. Therefore, the BSA incubation step was adapted for all futher experiments.

Finally, we sought to characterize the maximum amount of immobilized biotinylated pan-HLA antibody that can be bound onto a single chip by using the optimized protocol. This was evaluated through the use of multiple loading cycles of a new antibody batch of the same concentration (<NUM>/mL) per a single microfluidic chip. In this case, the amount of the immobilized pan-HLA antibody was determined by comparing the pan-HLA antibody amount in the feed solution versus the output solution through an ELISA assay. It was observed that the amount of immobilized antibody increased almost linearly along with the number of loading cycles (<FIG>), allowing an accurate 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 the approximate amount of <NUM>µg, which suffices, at least theoretically, for the immunopeptidome investigation of scarce biological material as <NUM> of the pan-HLA (<NUM>. 88x10<NUM> molecules of antibody) is required by the state-of-the-art methodologies for the investigation of <NUM><NUM> number of cells [22A] (Table <NUM>).

With the microchip setup, <NUM>. 74x10<NUM> molecules of antibody could be immobilized and technically <NUM>. 5x10<NUM> cells could be investigated.

To assess whether the developed thiol-ene microchip could be exploited as a platform for antigen discovery, we immunopurified HLA peptides from the human B-cell lymphoblastoid cell line JY. The JY line has high expression of class I HLA and is homozygous for three alleles common in the human population (HLA-A*<NUM>:<NUM>, HLA-B*<NUM>:<NUM> and HLA-C*<NUM>:<NUM>), and it has been extensively adopted for ligandome analysis. Consequently, the JY cell line was considered a suitable model for benchmarking the microchip-based antigen enrichment Immuno Precipitation technology.

Hence, HLA-I complexes were immunoaffinity-purified using the thiol-ene microchips, functionalized with the amount of pan-HLA antibody as described above. Moreover, to determine the sensitivity of our approach, the protocol was challenged by using total cell numbers as low as 50x10<NUM>, 10x10<NUM> and 1x10<NUM>. The lysates were loaded into the microchips, and after adequate washing with PBS, the peptides were eluted with <NUM>% acetic acid and analysed by tandem mass spectrometry. The entire workflow took an average, from the streptavidin functionalization to the elution of the tumour peptides, of <<NUM> hours. A stringent false discovery rate threshold of <NUM>% for peptide and protein identification was applied to generate data with high confidence. We were able to identify <NUM>, <NUM> and <NUM> unique peptides from 50x10<NUM>, 10x10<NUM> and 1x10<NUM> cells, respectively (<FIG>).

As we sought to carefully analyse the ability of the microchip technology to enrich for natural HLA-I binders and to avoid potential co-eluting contaminants, we extensively characterized the eluted peptides. First, the eluted peptides from the JY cell line represented the typical length distribution of a ligandome data set, with 9mers as the most enriched peptide species (<FIG>). Next, the predicted binding affinity for the two HLA-I alleles (HLA-A*<NUM> and HLA-B*<NUM>) expressed in JY cells was determined. JY cells also have a low level of the allele HLA-C*<NUM>, but the binding motif overlaps with the motifs of HLA-A*<NUM> and HLA-B*<NUM>; hence, only these alleles were considered in the subsequent analysis.

Of the unique 9mers, <NUM>%, <NUM>% and <NUM>% were predicted to be binders (described as binders in NetMHCpan4. <NUM>, applied rank <NUM>% [24A-26A]) to either HLA-A*<NUM> or HLA-B*<NUM> alleles for 50x10<NUM>, 10x10<NUM> and 1x10<NUM> cells, respectively (<FIG>). Moreover, Gibbs analysis was performed to deconvolute the consensus binding motifs of respective HLA-I alleles from the eluted 9mer peptides; these clustered in two distinct groups, with a preference for reduced amino acid complexity for residues at positions P2 and Ω, matching remarkably well with the known ones for HLA-A*<NUM> and HLA-B*<NUM> (<FIG>).

Next, in order to determine the role of the peptides identified, a gene ontology (GO) term enrichment analysis was performed on our list of 9mer binder source proteins. We observed an enrichment in nuclear and intracellular proteins, mainly those interacting with DNA, RNA or involved in catabolic activity. Finally, we set up an in vitro killing assay to further demonstrate the capacity of the microchip technology in isolating peptides in complex with HLA-I. To this end, a set of three peptides was selected from our JY data set to stimulate HLA-matched PBMCs; CD8+ T cells were purified from the PBMCs and adopted as effector cells in coculture with JY cells. To account for nonspecific cytotoxicity due to the effector cells per se, unstimulated PBMCs were used as a control. Real time cytolysis was then monitored. Interestingly, the CD8+ T cells pulsed with the peptides QLVDIIEKV (SEQ ID NO: <NUM>; gene name PSME3) and KVLEYVIKV (SEQ ID NO: <NUM>; gene name MAGEA1) showed about <NUM>% specific cytolysis, whereas the CD8+ T cells pulsed with the peptide ILDKKVEKV (SEQ ID NO: <NUM>; gene name HSP90AB3P) induced <NUM>% specific cytolysis, indicating specific lysis in the presence of defined peptides.

To evaluate the validity of our HLA-I peptide lists identified by the microchip technology, we interrogated SysteMHC, a repository of the immunopetidomics data set generated by mass spectrometry. Among the unique 9mer binders identified in our data, <NUM>%, <NUM>% and <NUM>% were also found in a previously published ligandome data set derived from the JY cell line (pride ID PXD000394) [3A] for 50x10<NUM>, 10x10<NUM> and 1x10<NUM> cells, respectively (<FIG>). In addition, a positive correlation between the abundance of the source protein and HLA presentation was confirmed, with the most abundant proteins being the main source of the HLA peptides.

Hence, these results demonstrated that the chip-based protocol can be exploited as a reliable Immuno Precipiation platform within the immunopeptidomic workflow.

We applied the sample through multiple cycles and incubated for <NUM> minutes (working at <NUM>). We washed <NUM> times with 200ul of PBS and aspirated the last washing to empty the chip. We prepared a solution of <NUM>%MeOH and <NUM>%MilliQ. With the previous solution we prepared a <NUM>% solution of Acetic Acid. We applied this solution to the CHIP and collected the fraction. The elution time was <NUM> minutes. On the same day, we purified the collected fractions. We prepared a SepPak cartridge for each tissue sample-for the HLA-I peptides sample and labelled them. Using a syringe and the dedicated adaptor we washed the cartridge first once with <NUM> of <NUM>%ACN in <NUM>%TFA, then twice with <NUM> of <NUM>% TFA. We load each of the biological samples on a SepPak cartridge. We passed them through slowly (speed of about <NUM> in <NUM>). We washed the cartridges twice with1 ml of <NUM>%TFA. We eluted the HLA binding peptides into a collection tube with 300ul of <NUM>% ACN in <NUM>%TFA.

Homology Evaluation of Xenopeptides (HEX) is a novel in silico platform that compares similarity between tumor peptides (reference peptides) and pathogen-derived, such as viral, peptides (query peptides). It utilizes several metrics in order to expediate candidate peptide selection. This is done by incorporating both novel methods (peptide scoring and alignment scoring algorithm) and integrated pre-existing methods (MHC-I binding prediction). HEX comes with a number of precompiled databases of known proteins, such as proteins derived from viral pathogens and the human proteome (<NUM>).

The associated scoring matrix is generated ad hoc based on the amino acid composition of the reference peptide, as opposed to experimentally. In particular, in the matrix rows representing the amino acid position in the peptide and columns representing each of the <NUM> standard amino acids, amino acid positions of the reference peptide were assigned the same high score and other positions were assigned the same low score.

Alignments are computed pairwise between peptides in the query set against the reference set. For a given pair of peptides, their alignment is calculated by the summing the distance scores between pairs of amino acids in the same position. Scoring is weighted to prioritize similarity between more central amino acids in the peptide. HEX supports both BLOSUM and PAM substitution matrices across several evolutionary distances. Specifically, BLOSUM <NUM> was the matrix chosen for this study.

MHC class I binding affinity predictions are made using NetMHC [<NUM>] via the Application programming interface of IEDB (http://tools. org/main/tools-api/) and are then parsed and collated within the tool. The user can specify their desired scoring method or return a number of recommended results. Predictions for a number of human and murine MHC-I alleles are supported.

Users are able to select peptides by their own criteria or allow peptides to be selected by a random forest model. The random forest was trained on experimental outcomes of peptides chosen by the authors. Feature importance was determined by out-of-bag (OOB) increase in mean squared error (MSE) and cross-validated on an unseen sample of the peptides. HEX was developed as a web application using the R package Shiny and is accessible at https://picpl. 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 several evolutionary distances. Specifically, BLOSUM <NUM> was the matrix used in this study. HEX, first performs a BLAST search using PAM30 to find the Hits (similar sequences) in the reference library. Next, HEX performs a pairwise refinement alignment using both BLOSUM62 and a substitution matrix developed by Kim et al.

MHC class I binding affinity predictions are made using NetMHC4. <NUM> (or NetMHCpan4. <NUM>) as a standalone command line tool and are then parsed and collated within the tool. The user can specify their desired scoring method or return a number of recommended results. Predictions for a number of human and murine MHC-I alleles are supported.

In total, <NUM> stage <NUM> metastatic melanoma patients were treated with anti-PD1 monoclonal antibody in the Helsinki University Central Hospital (HUCH) Comprehensive Cancer Center. Patients were randomly selected to receive either nivolumab (n=<NUM>) infusions every second week or pembrolizumab (n=<NUM>) infusions every third week. The study was approved by Helsinki University Central Hospital (HUCH) ethical committee (Dnro <NUM>/<NUM>/<NUM>/<NUM>/<NUM>). Written informed consent was received from all patients and the study was conducted in accordance with the Declaration of Helsinki.

Peripheral blood samples (<NUM> EDTA blood, <NUM> Heparin blood) were collected from three time-points; before initiation of treatment, after one and three months of treatment. From these the plasma was separated by centrifuging and then stored at - <NUM>. The CMV and EBV IgG levels were measured from thawed EDTA plasma samples using VIDAS CMV IgG (BioMérieux, Marcy-I'Etoile, France) and Siemens Enzygnost Anti-EBV/IgG kits (Siemens Healthcare Diagnostics, Marburg, Germany). The immunoglobulins (IgA, IgM, IgG) from thawed Heparin plasma were measured in the central laboratory of the Helsinki University Central Hospital (HUSLAB).

The murine melanoma cell line B16-F10 was purchased from the American Type Culture Collection (ATCC; Manassas, VA, USA). Cells were cultured in RPMI (Gibco, Thermo Fisher Scientific, US) with <NUM>% fetal bovine serum (FBS) (Life Technologies), <NUM>% Glutamax (Gibco, Thermo Fisher Scientific, US), and <NUM>% Penicillin and Streptomycin (Gibco, Thermo Fisher Scientific, US) at <NUM>/ <NUM>% CO<NUM>.

The cell line B16-OVA, a mouse melanoma cell line modified to constitutively express chicken Ovalbumin (OVA), was kindly provided by Prof. Richard Vile (Mayo Clinic, Rochester, MN, USA). These cells were cultured in RPMI Low glucose (Gibco, Thermo Fisher Scientific, US) with <NUM>% FBS (Gibco, Thermo Fisher Scientific, US), <NUM>% Glutamax, <NUM>% Penicillin and Streptomycin (Gibco, Thermo Fisher Scientific, US) and <NUM>% Geneticin (Gibco, Thermo Fisher Scientific, US) at <NUM>/ <NUM>% CO<NUM>.

All cells were tested for mycoplasma contamination with a commercial detection kit (Lonza - Basel, Switzerland). Isolated human PBMCs were frozen in FBS supplemented with <NUM>% DMSO and then maintained in liquid nitrogen until use. Cryopreserved PBMCs were thawed and rested overnight at <NUM>/ <NUM>% CO<NUM> in complete RPMI medium supplemented with <NUM>% FBS, <NUM>% Glutamax, <NUM>% penicillin-streptomycin over night before plating them for ELISPOT.

All the peptides used in this study were purchased from Zhejiang Ontores Biotechnologies Co. (Zhejiang, China) <NUM> ><NUM>% purity. The sequences of all the peptides used in this study are found in Table <NUM> and <NUM>.

All PeptiCRAd complexes described in this work were prepared by mixing Adenoviruses and polyK-tailed peptides according to the following protocol: 1x10<NUM>vp(viral particles) were mixed with 20ug poly-K tailed peptides (resuspended in water); after vortexing, the mixture was incubated at room temperature for <NUM>; PBS was added, after the incubation, up to the injection volume (50uL/mouse), successively, the solution was vortexed again and used for assays or animal injections. For the TRP2-PeptiCRAd, 1x10<NUM>vp were mixed with 20ug of <NUM>-TRP2<NUM>-<NUM> peptide, while the Viral-PeptiCRAd was prepared using 1x10<NUM>vp mixed with 5ug of each viral <NUM>-peptide homologous for TRP2<NUM>-<NUM>.

New PeptiCRAds were prepared before each experiment using fresh reagents. All dilutions of virus and peptides required before incubation for PeptiCRAd preparation, were performed in sterile PBS or water. The PeptiCRAds were then diluted in the buffer required by the assay.

Viruses were generated, propagated, and characterized as elsewhere described [<NUM>].

All animal experiments were reviewed and approved by the Experimental Animal Committee of the University of Helsinki and the Provincial Government of Southern Finland.

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

For the immunization experiment <NUM>- to <NUM>-week-old immune competent female C57BL/6J mice were divided in <NUM> groups. N = <NUM> mice were used as mock group, n = <NUM> 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 one-week interval (day <NUM> & <NUM>) at the base of the tail with 40ug of peptides and 40ug of adjuvant (VacciGrade poly(I:C) - Invivogen) in a final injectable volume of 100ul. Naïve mice (PBS injected) were used as Mock group. At day fourteen mice were injected with <NUM>*<NUM><NUM> B16-OVA cells on the right flank and tumor growth was followed until endpoint was reached.

For the treatment of established tumors, we tested <NUM> different tumor cell lines: B16-OVA cells and the more aggressive B16F10 cells. <NUM>*<NUM><NUM> B16-OVA cells and <NUM>*<NUM><NUM> B16-F10 were injected subcutaneously on the right flank of <NUM>- to <NUM>-week-old immune competent female C57BL/6J mice. Successively, these mice were randomly divided in <NUM> groups of <NUM>-<NUM> mice for each tumor cell line. A mock group was treated with PBS; a second group was treated with uncoated Adenovirus; a third group was treated with Adenovirus coated with TRP2<NUM>-<NUM> (TRP2-PeptiCRAd); the last group was treated with Adenovirus coated with TRP2-homologous viral peptides (Viral-PeptiCRAd).

Mice were intratumorally treated twice and injections were performed at two days interval (day <NUM> & <NUM> from tumor engraftment) and tumor growth was followed until endpoint was reached. The median of the tumor volume measurement of the last day identifies the therapeutic success threshold showed as a dotted line. Mice that at the end point showed a tumor volume below the threshold were considered responders, while mice above it were considered as non-responders.

Tumor growth was followed with a digital caliper measuring two dimensions of the tumor. Successively, the volume was mathematically calculated according to the following formula:<MAT>.

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

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

Fresh mice splenocytes collected at the end point of the experiment have been used. The procedure was carried out according to the manufacturer instructions. In brief, for murine IFN-γ, <NUM>*<NUM><NUM> of splenocytes/well were plated at day <NUM>. Cells were stimulated with 2ug/well of peptides. After <NUM> days of incubation at <NUM>/ <NUM>% CO2, plates were developed according to the kit's protocol.

For human IFN-γ ELISPOT, human PBMCs were thawed and let rest over-night at <NUM>/ <NUM>% CO<NUM> in complete medium. The following day, 3x10<NUM> PBMCs/well were plated and stimulated with 2ug/well of peptides. After <NUM> of incubation at <NUM>/ <NUM>% CO<NUM> the plates were developed according to the manufacturer's protocol. Plates were sent to CTL-Europe GmbH to be analyzed.

The EBV-transformed human lymphoblastoid B-cell line JY (ECACC HLA-type collection, Sigma Aldrich) was cultured in RPMI <NUM> (GIBCO, Invitrogen, Carlsbad, CA, USA) supplemented with <NUM>% GlutaMAX (GIBCO, Invitrogen, Carlsbad, CA, USA) and <NUM>% heat inactivated foetal bovine serum (HI-FBS, GIBCO, Invitrogen, Carlsbad, CA, USA).

Streptavidin (Streptomyces avidinii, affinity purified, lyophilized from <NUM> potassium phosphate, ≥<NUM> U/mg protein) was purchased from Sigma-Aldrich (Saint Louis, Missouri, USA).

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

The following peptides were purchased from Ontores Biotechnologies Co. , were used throughout the study: KVLEYVIKV (SEQ ID NO: <NUM>; gene name MAGE A1), ILDKKVEKV (SEQ ID NO: <NUM>; gene name HSP90), and QLVDIIEKV (SEQ ID NO: <NUM>; gene name PSME3).

Additionally, the following peptides were purchased from Chempeptide (Shangai, China):
VIMDALKSSY (SEQ ID NO: <NUM>; gene name NNMT), FLAEGGGVR (SEQ ID NO: <NUM>; gene name FGA) and EVAQPGPSNR (SEQ ID NO: <NUM>; gene name HSPG2).

The ovarian tumour biopsy was collected from a patient with ovarian metastatic tumour (high grade serous), who signed an informed consent, under the studies approved by the Research Ethics Committee of the Northern Savo Hospital District with the approval number <NUM>/<NUM>. The samples were chopped in small pieces and treated with a digestion buffer containing collagenase type D (Roche) <NUM>/ml, Hyaluronidase (Sigma Aldrich) <NUM>µg/ml and DNase I (Roche) <NUM>/ml for <NUM> at <NUM>. The cell suspension was sequentially passed through a <NUM> and <NUM> cell strainer (pluriSelect) to obtain single cells.

Patient tissue samples for organoid cultures were obtained from the DEDUCER study (Development of Diagnostics and Treatment of Urological Cancers) at Helsinki University Central Hospital with approval number HUS/<NUM>/<NUM>, <NUM>. <NUM>, ethical committee approval number <NUM>. <NUM> Dnro <NUM>/<NUM>/<NUM>/<NUM>/<NUM>, and patient consent. The kidney sample was obtained from a nephrectomy of an adult male with a clear cell renal cell carcinoma (ccRCC, pTNM stage pT3a G2). The benign kidney tissue sample was used for the experiments. The carcinoma urothelial (bladder cancer, high grade, gradus III, 1x1 cm) was obtained from adult female, and the cancer tissue sample was used for the organoid culture.

Cells were isolated from the original tissue instantly after surgery by dissociating the tissue into small pieces and treating it with collagenase (<NUM> units/ml) for <NUM>-<NUM>. Benign and cancer cells of the kidney of a clear cell renal cell carcinoma patient cells were grown as organoids in F-medium [<NUM>:<NUM> (v/v) of F-<NUM> nutrient mixture (Ham) - DMEM (Invitrogen), <NUM>% FBS, <NUM> ng/mL cholera toxin (Sigma), <NUM>µg/mL hydrocortisone (Sigma), <NUM> ng/mL epidermal growth factor (Corning), <NUM>µg/mL adenine (Sigma), <NUM>µg/mL insulin (Sigma), <NUM> ROCK inhibitor (Y-<NUM>, Enzo Life Sciences, Lausen, Switzerland) and <NUM>% penicillin-streptomycin with <NUM>% Matrigel (Corning). The bladder tumour-derived organoids were grown in hepatocyte calcium medium (Corning)[15A] supplemented with <NUM>% CSFBS (Thermo Fisher Scientific), <NUM> Y-<NUM> RHO inhibitor (Sigma), <NUM> ng/mL epidermal growth factor (Corning), <NUM>% GlutaMAX (Gibco), <NUM>% penicillin-streptomycin and <NUM>% Matrigel. 6x10<NUM> cells were collected by centrifugation, washed in PBS to remove Matrigel and snap frozen before analyses.

The clinical HLA typing of tumour samples (ccRCC and bladder) was performed by the European Federation for Immunogenetics (EFI) -accredited HLA laboratory of the Finnish Red Cross Blood Service. Allele determination of three classical HLA-I genes HLA-A, -B and -C was performed by targeted PCR based next generation sequence (NGS) technique according to the protocol provided by the manufacturer (NGSgo® Workflow, GenDx, Utrecht, The Netherlands).

The allele assignment at <NUM>-field resolution level was implemented by NGSengine Version: <NUM>. <NUM> (GenDx, Utrecht, The Netherlands) using IPD IMGT/HLA database Release <NUM>. <NUM>, https://www. uk/ipd/imgt/hla/.

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

The data were acquired using a BDLSR Fortessa Flow Cytometer. Flow cytometric analysis of renal cell carcinoma and bladder tumour-derived organoids was performed using a BD Accuri <NUM> plus (BD Biosciences) and analysed with FlowJo software (Tree Star, Ashland, OR, USA).

In order to study whether molecular mimicry between viruses and tumor could impact on tumor growth we needed to identify peptides sharing a high degree of homology. However, a tool to facilitate the identification of relevant target, to this scope, is lacking. Therefore, we developed HEX (Homology Evaluation of Xenopeptides), a device that compares the input sequences to a database of pathogen-derived antigens/peptides or protein sequences and selects highly homologous candidate pairs of peptides based on at least two of the three following criteria: <NUM>) A B-score that corresponds to the likelihood of the peptides being recognized by a given TCR; <NUM>) A positional weighted alignment score to prioritize the similarity in the area of interaction with the TCR; <NUM>) the prediction of the MHC class I binding affinity (<FIG>). In the alternative the use of conventional software may be used as shown in <FIG> and the as described above.

We started from three melanoma-associated antigens, that have been applied successfully in a number of vaccination studies: TRP2<NUM>-<NUM> (tyrosinase-related protein <NUM>), GP100<NUM>-<NUM> (aka PMEL; premelanosome protein) and TYR1<NUM>-<NUM> (tyrosinase <NUM>).

TYR1<NUM>-<NUM> is not predicted to be a binder of the murine MHC, and thus, was considered as an "irrelevant target". With HEX we identified viral peptides that shared a high degree of homology with the input tumor epitopes. Pools of <NUM> viral-derived peptides per each original tumor epitope (Table <NUM>) were chosen to be further evaluated in vivo.

To assess whether the viral-derived peptides would influence the tumor growth, we decided to mimic the viral infection by immunizing C57BL6 mice with selected viral peptide-pools followed by the tumor engraftment (<FIG>). All immunized mice showed a reduction in tumor growth compared to mock. Additionally, significant differences between the treatment groups were observed. The tumor growth was most reduced in mice immunized with viral-pool of peptides homologous to TRP2 or gp100 (<FIG>).

To further investigate the contribution of the selected viral peptides in the reduction of tumor growth, we collected the splenocytes of the mice at the endpoint for ELISpot assay (<FIG>). The splenocytes from mice immunized with viral peptide-pools homologous to TRP2 or gp100 fostered higher IFN-γ response compared to the splenocytes from mice immunized with control TYR1-homologous epitopes, in correlation with the outcome of the tumor growth.

We further investigated the reactivity of these splenocytes from the viral pre-immunized mice towards their respective cognate tumor antigen (<FIG>). When compared side by side only the viral peptides homologous to TRP2 showed higher IFN-γ secretion compared to the cognate tumor peptide (<FIG>). In addition, we retrospectively assessed the affinities of each tumor antigen and their homologous viral pools to the C57BL/6J MHC class I molecules (<FIG>) and observed significant correlation with their respective ability to stimulate IFN-γ secretion (<FIG>). Following the guidelines of the Immune Epitope Database (IEDB) we divided the peptides as High and Low affinity using the threshold of <NUM> (http://tools. org/mhci/help/) and observed that peptides with high MHC-I affinity fostered the production of more INF-γ compared to peptides with low MHC-I affinity (<FIG>). Taken together these results validate the predictive efficiency of HEX tool to identify homologous peptides, and highlight that molecular mimicry between viruses and tumor plays a role in anti-tumor immune response through cross-reactive T-cells.

We have shown that the molecular mimicry between viral and tumor antigens can affect the tumor growth in preimmunized mice. Next, we wanted to assess whether the molecular mimicry directed-response could have an impact on already established tumors in naïve mice. To this end, mice were implanted with B16OVA tumors, or more aggressive and immunosuppressive B16F10 tumors and, subsequently, treated with the previously developed vaccine platform PeptiCRAd (<NUM>) to mimic a viral infection (<FIG>). Briefly, PeptiCRAd is a vaccinal technology consisting of adenoviruses coated with MHC-I restricted peptides bearing positively charged poly-aminoacidic tales. In this case, we proceeded using the most effective pool of peptides from the first in vivo experiment. Vaccines were prepared by coating adenoviruses with the original TRP2<NUM>-<NUM> epitope (TRP2-PeptiCRAd) or the corresponding pool of viral-derived peptides similar to TRP2 (Viral PeptiCRAd). Intratumoral injections of PeptiCRAd significantly reduced the tumor progression compared to treatment with saline buffer or uncoated-virus both for B16OVA (<FIG>) and B16F10 tumors (<FIG>). Mice treated with the original tumor antigen or the viral homologous showed significantly higher number of responders in both of the tumor models (<FIG>). In both tumor models we observed a significant reduction in tumor growth when mice were treated with viral peptides similar to tumor peptides, suggesting once again that molecular mimicry might play a fundamental role in anti-tumor immunity engaging cross-reactive T cells.

We selected a pool of melanoma-associated proteins [<NUM>] that were compared to the CMV proteome using HEX generating a list of tumor peptides with high similarity to CMV (Table <NUM>). Tumor peptides and their CMV counterpart were used in ELISPOT assay showing that in a responder patient, PBMCs always responded to both viral and their counterpart tumor antigens, suggesting that the CMV infection had expanded viral T cell clones that could attack and kill tumor cells making seropositive patients more prone to react towards melanoma specific epitopes similar to CMV (<FIG>). Although tantalizing, this observation suffered the limitation that did not directly show if viral and tumor peptides had expanded the same T cell clones.

We challenged the platform for the investigation of a scarce tumour biopsy. Thus, an ovarian metastatic tumour (high grade serous) was collected from the patient and four pieces were derived from the tumoral border (S1, S2, S3 and S4); the central part of the tumour was collected as well (S5). Next, the samples were weighed and as summarized in <FIG>, the size averaged from a <NUM> to <NUM>. After the sample digestion, the obtained single cell suspensions were lysed and processed through the microchip. Applying a stringent false discovery rate threshold of <NUM>% for peptide and protein identification, <NUM>, <NUM>,<NUM>, <NUM> and <NUM> unique peptides were identified respectively in S1, S2, S3, S4 and S5 (<FIG>). In line with a typical ligandome profile, a general enrichment (above <NUM>%) was observed in <NUM>-13mers specimens (<FIG>). As regard absolute number and percentage, the aminoacidic length distribution showed that the 9mers specimens were the most representative (<FIG>), confirming our and other previous immunopeptidomic analysis. Next, we further investigated the source proteins found in our data, applying Gene Ontology (GO) enrichment analysis. Consistently with a typical ligandome profile, metabolic processes were enriched in all the samples examined. Additionally, the analysis revealed an increase in skin development pathway proteins in line with the epithelial nature of the ovarian serous tumour here analyzed. Altogether, the results highlighted the feasibility of exploiting the developed microfluidic-chip platform to analyse scarce tumour biopsy and obtain personalized antigenic peptidse for tumour treatment.

The microchip technology was challenged with as few as 6x10<NUM> cells from patient-derived organoids (PDOs). We selected two patients from an on-going precision medicine study for urological cancers, a nephrectomy sample containing both benign and cancer tissue from clear cell renal cell carcinoma (ccRCC) patient and 1x1 cm sample from bladder cancer patient, were further processed as 3D primary organoid cultures.

Applying the developed microchip technology and a stringent false discovery rate threshold of <NUM>% for peptide and protein identification, we were able to identify a total of <NUM> and <NUM> unique peptides in ccRCC and bladder PDOs respectively (<FIG>). The number of retrieved peptides differed between the two samples, with bladder samples resulting in more peptides than ccRCC samples. It is well known that HLA expression influences the amount of isolated HLA-I peptides [22A], and consistent with this, flow cytometry analysis revealed higher surface levels of HLA-A, HLA-B, and HLA-C in our bladder than in our ccRCC samples, explaining the different yields of retrieved peptides from our samples. Analysis of the peptides showed a preference for <NUM>- to 12mers (<NUM>% in ccRCC and <NUM>% in bladder tumours) with an enrichment in the 9mer population, in line with the typical length distribution of ligandome analysis [<NUM>] (<FIG>). To discriminate between HLA-I binders and contaminants, Gibbs clustering and NetMHC4. <NUM> analysis was performed. First, deconvolution of the 9mers showed that <NUM>% and <NUM>% in bladder and ccRCC PDOs, respectively, matched at least one of the patients' HLA alleles. Next, NetMHC4. <NUM> was applied to all 9mers identified in our data set. Among them, <NUM>% and <NUM>% were predicted to be binders of the specific HLA of the patients. Next, the source proteins present in both our data sets were investigated. To this end, Gene Ontology (GO) enrichment analysis was performed. Consistent with our previous observations and published data, both samples showed an enrichment in intracellular and nuclear proteins interacting with RNA and involved in catabolic/metabolic processes.

To demonstrate that technology can be exploited for the rapid development of therapeutic cancer vaccines, we set up a killing assay. We focused our analysis on ccRCC samples. We used transcriptomics levels to select putative tumour antigens using PBMC and healthy kidney tissue as reference sets. Next, PBMCs from healthy volunteers were pulsed with the selected peptides, and CD8+ T cells isolated from those cells were used in the assay. T cells pulsed with the peptide EVAQPGPSNR (gene name HSPG2) showed about <NUM>% specific cytolysis.

Finally, we sought to investigate the recall T cells response in the ccRCC patient. To this end, unfractionated PBMCs derived from the patient were in vitro stimulated with the peptide EVAQPGPSNR whereas unstimulated PBMCs were adopted as control. The derived CD8+T cells were then added to the ccRCC PDO showing about a <NUM>% increase in killing activity compared to the control group.

The list of peptides was analysed with HEX. First, the software prioritized the peptides that were concurrent strong binders (cut off IC50 range <NUM>-<NUM> according to NetMHC4. <NUM>) and that showed higher weighted alignment score (cut off <NUM>-<NUM> normalized weighted alignment score). This latter focuses the peptide's similarity in the area of interaction that most likely will engage the TCR of CD8+ T cells, in order to induce mediated immune response; the resultant peptides are then further categorized based on their overall percentage of identity to pathogen-derived antigens/peptides and IC<NUM> binding affinity score. The ultimate output consisted in thirteen peptides with their counterpart pathogenic peptides (Table <NUM>).

To determine the peptide immunogenicity, mice were pre-immunized with subcutaneous injection of each single peptide in presence of an adjuvant Poly(I:C); a group of mice was injected either with Poly(I:C) alone or saline as control as well. The splenocytes from those mice were harvested and tested for IFNγ production upon specific stimuli (Table <NUM>) in an ELISpot assay.

We then sought to verify whether the candidate peptides could be exploited as cancer vaccine for treatment of established tumors. To this end, we adopted PeptiCRAd, our previously developed cancer vaccine platform, that combines oncolytic adenoviruses with capsid attached (via a poly-lysine linker) poly-lysine modified peptides. The adenovirus used here is VALO-mD901 i.e., genetically modified to express murine OX40L and CD40L and previously shown to elicit tumor growth control and systemic antitumor response in murine melanoma model. Therefore, Balb/c mice were subcutaneously injected with the syngeneic tumor model CT26 in the left and right flank. (Day <NUM>, <FIG>. When the tumors were established (Day <NUM>, <FIG>), Valo-mD901 was coated with a pair of each poly-lysine-modified peptide in our list (PeptiCRAd1, PeptiCRAd2, PeptiCRAd3, Table <NUM>)) and injected intratumorally only in the right tumor. PeptiCRAd4 consisted of Valo-mD901 coated with gp70423-<NUM> (AH1-<NUM>). Mock and Valo-mD901 groups were used as control as well. PeptiCRAd <NUM> and PeptiCRAd2 improved tumor growth control as well as Valo-mD901 in the injected lesions (<FIG>, right panel). Advatageously, PeptiCRAd1 improved anti-tumor groth control in the untreated tumor in contrast to Valo-mD901 which did not trigger effect.

HEX is a bioinformatic tool that analyzes peptide sequences and compares them to a curated database of pathogen-derived viral proteomes looking for sequences with high similarity.

Since presented peptides are mainly involved in the interaction with the anchor residues of the MHC [<NUM>], our software returns an alignment score that is positionally weighted in order to prioritize the similarities occurring in the central section of the peptide, that is the position which is most involved in the interaction with the TCR [<NUM>]. Furthermore, in order to increase the chances that our target is a presented epitope, we ranked the resulting peptides according to the binding affinity for the chosen MHCs using, in one example, the IEDB NetMHC prediction tool API [<NUM>]; or NetMHC4. <NUM> or NetMHCpan4.1b as stand alone tools.

We used HEX to identify viral-peptides homologous to three extensively characterized tumor associated antigens.

We found that preimmunization with our selected viral peptides pools, simulated an anti-viral immune status prior to the establishment of the tumor and efficiently slowed down the growth of subcutaneously injected melanoma tumor cells in mice indicating that the pre-exposure to viral-derived peptides can affect the tumor growth.

Next, we observed that the tumor-homologous viral-derived peptides have a similar effect on already established tumors when administrated during tumor progression, indicating that a viral infection occurring during the tumor progression could still impact on its growth.

Taken together our results suggest that the molecular mimicry between viral- and tumor-derived antigens is able to exert a control on the tumor growth even without relying on the pre-exposure or pre-acquired immunity.

Novel ICPIs have significantly improved patient survival in several solid tumors, especially in metastatic melanoma, when compared to the other commonly used therapies such as radiation and chemotherapies. However, despite the improved survival and efficient response rates, it is yet known why some patients do not benefit from ICPI-therapies. Our results indicated that patients with high titer of CMV-specific IgG levels had significantly longer progression free survival.

We observed that PBMCs, from patients with high anti-CMV-IgG titer, reacted with melanoma antigens similar to CMV peptides. This data indicates CMV-seropositivity contributes to a melanoma-specific T-cell immunity, and thus provide a clinical advantage for these patients undergoing ICPI therapy.

Here we show that viral infections could have an impact on the tumor growth and clearance. Additionally, we show the cross-reactivity of cytotoxic T-cells against viral- and homologous tumor-derived antigens selected using HEX. Based on our results we conclude that the molecular mimicry phenomenon could be exploited in the future for the development of novel therapies or, when accompanied with immunotherapies, potentially enhance the antitumor immune effect achieved by these therapies.

Moreover, designing effective tumour rejection and protection strategies requires the reliable identification of tumour peptides binding to HLA-I. The direct identification of peptides from the HLA-I complex still represents the best method. Nevertheless, the immunopeptidomics workflow is relatively complex and thus represents a major bottleneck in the antigen discovery process. Currently, the inability to analyse immunopeptidomes from a small amount of biological materials (e.g., tissue needle biopsy), the sample throughput, the cost and the affinity matrix adopted (which is both laborious and expensive to produce) in the conventional platform have been depicted as the main technical challenges to address.

In this work, we addressed several technical issues hindering the ligandome research, with main focus on the limited availability of material to analyse, on the cost of consumables and on prolonged 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 matrices prepared via crosslinking reactions with a microchip platform. The limitations posed by the paucity of the material (e.g., needle biopsy) inspired the work towards the implementation of a microfluidic protocol. In this work, we employed a custom microchip protocol involving a thiol-ene polymer-based micropillar array as the solid support for further biofunctionalization that enabled performing the entire IP procedure on a single microfluidic chip.

The validation of the identified peptides in an in vitro killing assay confirmed that the peptides identified when working the invention were actually presented on the JY cell surface, as they were killed in a specific CD8+ T cell-dependent fashion. The peptide ILDKKVEKV (SEQ ID NO: <NUM>) found in our data set elicited the higher percentage of specific cytolysis.

Our approach thus offers an unexplored tool for immune-affinity purification.

Claim 1:
A method for tumour-specific antigen identification comprising:
i) dissolving or suspending a sample of tumour in a fluid;
ii) passing said fluid through a microfluidic device for tumour-specific antigen identification comprising: at least one flow-through channel containing a plurality of micropillars arranged in an array to which there is attached at least one molecule, or at least one complex, that has bound thereto at least one anti-Major Histocompatibility Complex antibody or at least one anti-pan-Human Leukocyte Antigen antibody whereby a peptide:Major Histocompatibility Complex (pMHC) in a sample flowing through said channel can be extracted from said sample using said at least one antibody.
iii) binding to said at least one antibody at least one pMHC in said sample;
iv) removing said at least one bound pMHC of part iii) from said device;
v) comparing said peptide of said bound pMHC with a library of pathogen-derived antigens to determine if said peptide shows homology, by way of a comparison of sequence structures, with at least one pathogenic antigen, or part thereof, and where greater than <NUM>% homology exists;
vi) identifying said peptide as a tumour-specific antigen for use in cancer therapy.