TW202528550A - Compositions and methods for non-invasive rapid test for dna, rna, and protein markers present in nasopharyngeal carcinoma - Google Patents

Abstract

Compositions, kits and methods of determining the presence of nasopharyngeal carcinoma (NPC) in a subject or relapse prediction of NPC in the subject are provided, with high specificity, sensitivity, and accuracy. Exemplary mutations include mutations in EMP2, IL32, EEF2KMT, CSTA, SOCS1, TESMIN, and IGFBP7 can be used to detect NPC. Mutations in IL32, DHX57, HMGN2P3, DNAJC11, EIF2AK1, FAM234A, PARPBP, ARL5A, ATPAF1, HDAC2, TACSRD2, and/or LMO4 used to achieve NPC relapse prediction. The analyte to be detected can be either RNA or protein, peptide, or fragment thereof etc. can detect NPC. High level of LY6D-positive neoplastic cells, neoplastic SPB cells reflected by RNA/ protein expression of KRT16, CEBPD, CDKN1A, PGM2, and LY6D, neoplastic SPB1, SPB2 cells neoplastic SPB5 cells HDAC-mutated neoplastic SPB1 cells ATPAF1-mutated neoplastic SPB1, and low level of non-neoplastic SPB cells, non-neoplastic SPB1, and high neoplasticity of SPB cells can be used for NPC relapse prediction.

Description

Compositions and methods for non-invasive rapid detection of DNA, RNA and protein markers in nasopharyngeal carcinoma

The present invention generally relates to compositions, methods, and kits for diagnosing nasopharyngeal carcinoma and predicting recurrence of nasopharyngeal carcinoma.

Nasopharyngeal carcinoma (NPC) is a malignant tumor highly associated with the Epstein-Barr virus (EBV). The gold standard for NPC diagnosis (NPCD) is a nasopharyngeal biopsy combined with in situ hybridization (ISH) to detect Epstein-Barr encoded ribonucleic acid (EBER) to confirm the presence of undifferentiated or non-keratinizing carcinoma. This method also requires endoscopic sampling, making it an invasive procedure. Although plasma EBV DNA is considered relatively sensitive for NPC screening, diagnosis, and treatment response assessment, up to 15% of NPC cases may be missed if used as the sole screening or diagnostic tool, particularly in early, mild NPC and those with defects in plasma EBV DNA production. The positive predictive value (PPV) of plasma EBV DNA in NPCD is only approximately 11%. Furthermore, plasma EBV DNA is currently used for post-treatment disease monitoring and remains unable to accurately predict relapse at initial diagnosis before treatment. Approximately 50% of local relapses and up to 20% of metastatic disease are negative for plasma EBV DNA. The PPV of plasma EBV DNA in NPC relapse prediction (NPCR) is only approximately 6% to 30%. Currently, despite intensive treatment, approximately 30% of patients will relapse within 5 years.

RNA and protein (expression) biomarkers for NPCD or NPCR have also been studied. The most commonly mutated genes in NPC are TP53 and PIK3CA . TP53 is a tumor suppressor gene involved in cell cycle regulation and apoptosis. However, studies have found that only approximately 6% of NPC patients and 15% of relapse cases carry TP53 mutations. In terms of relapse prediction, detectable EBV DNA during post-treatment follow-up is associated with tumor recurrence. However, a cutoff value of 0 copies/mL of EBV DNA during post-treatment follow-up has a sensitivity of approximately 50% to 80% and a positive predictive value of approximately 6% to 30%. There is currently no consensus or recognized gold standard for the use of any DNA mutation for non-invasive NPCD or NPCR.

The viral oncoprotein, Espinoza-Barr virus latent membrane protein 1 (LMP1), is upregulated in NPC. However, immunohistochemistry has shown that LMP1 is expressed in nasopharyngeal tumors in only 63% of NPC cases.

Currently, there is no consensus or gold standard for noninvasive NPCD or NPCR using RNA and protein biomarkers before treatment.

The present invention aims to provide compositions, methods and kits for non-invasive diagnosis of nasopharyngeal carcinoma (NPCD) in an individual.

The present invention aims to provide compositions, methods, and kits for non-invasive nasopharyngeal carcinoma recurrence prediction (NPCR) before treatment and for follow-up of individuals to provide recommendations.

Any discussion in this specification of documents, acts, materials, devices, articles, or the like is not to be construed as an admission that such matters form part of the prior art or are common general knowledge in the art relevant to the present disclosure, even if such matters existed before the priority date of the respective claims of the present invention.

Throughout this specification, the term "comprise" or variations such as "comprises" or "comprising" will be understood to imply the inclusion of stated elements, components, or steps, or groups of elements, components, or steps, but not the exclusion of any other elements, components, or steps, or groups of elements, components, or steps.

These methods, compositions, and kits are based on the discovery of genetic mutations, RNA and/or protein expression biomarkers, and epithelial cell types as reflected by DNA/RNA/protein biomarkers. The presence of these biomarkers allows for the diagnosis of NPC in an individual or the prediction of future NPC recurrence with high sensitivity, specificity, and accuracy. Thus, these methods detect the presence of NPCD or NPCR indicators, which can be: (i) genetic mutations, (ii) biomarker expression (RNA and/or protein expression), and/or (iii) cytological indicators (i.e., cell type) as reflected by the expression of cell-type-specific DNA/RNA/protein biomarkers.

The methods include the use of: (a) genetic marker detection, which is performed by determining the presence of one or more genetic mutations as disclosed herein in a sample obtained from an individual, the presence of which indicates in some forms that the individual is likely to experience a recurrence of NPC in the future (i.e., NPCR), or in other forms that the individual already has NPC, thereby generating a diagnosis (i.e., NPCD); (b) biomarker detection, which is performed by measuring at least one biomarker in a sample obtained from an individual. (c) cytological marker detection by detecting the presence of cell types (as disclosed herein) in a sample obtained from an individual. The presence of these cell types may indicate in some forms that the individual is likely to experience a relapse of NPC in the future (i.e., NPCR) or in other forms that the individual already has NPC, thereby generating a diagnostic result (i.e., NPCD).

A) Methods for detecting the presence of NPC recurrence (i.e., "NPCR") in a biological sample obtained from an individual, including the use of genetic markers, biomarkers, and/or cytological marker detection. Cytological marker detection includes detection of genetic mutations and/or biomarker expression in cytological markers, as discussed further below.

Genetic markers used to detect NPCR include detecting gene mutations in cells in biological samples obtained from individuals. In these formats, the method detects: single nucleotide variants (SNVs) and/or insertions/deletions (InDels), including but not limited to SNVs in (i) HMGN2P3 (high mobility group nucleosome binding domain 2 pseudogene 3), (ii) ARL5A (ADP ribosylation factor-like GTPase 5A), DHX57 (DExH box helicase 57), (iii) IL32 (interleukin 32), and/or (iv) ATPAF1 (ATP synthase mitochondrial F1 complex assembly factor 1); and/or InDels in (i) DNAJC11 (DnaJ heat shock protein family (Hsp40) member C11), (ii) EIF2AK1 (eukaryotic translation initiation factor 2 alpha kinase 1), (iii) FAM234A (sequence similarity family 234 member A); PARPBP (PARP1 binding protein) and/or (iv) HDAC2 (histone deacetylase 2). The presence of one or more of these mutations may indicate relapse. In some formats, the presence of two or more identified mutations is assessed, for example, IL32 and DHX57.

In some forms, the method can detect relapse with a specificity of at least 80%. In some forms, the presence of IL32 (interleukin 32) (SNV) and DHX57 (DExH box helicase 57) (SNV) can be indicative of relapse with a sensitivity of 100%.

Cytological markers for detecting NPCR include gene mutation detection and/or biomarker expression in specific cell types as disclosed below, and in these forms, the methods detect:

(i) Cytological (cell type) mutations, such as: (1) HDAC2 (chr6:113,970,875) [CT＞C,CTT,CTTT,CTTTT,CTTTTT] InDel or ATPAF1 (such as ATPAF1 chr1:46668177 [A＞C]) mutant LY6D+ proliferative secretory-induced basal cluster 1 cells (SPB1). SPB1 is the major or predominant form of secretory basal cells (SPBs) that is located during epithelial development just prior to entry into the goblet or secretory state; (2) an in-frame deletion of chr 1:5857099 [CCAGC] at TACSRD2 present in LY6D ⁺ proliferative SPB1 cells present in the sample; and/or (3) a single nucleotide mutation in the 5'UTR of chr 1:87328973 (A to T) at LMO4 present in LY6D ⁺ proliferative SPB1 cells present in the sample; or

(ii) Cytological (cell type) biomarker expression in biological samples obtained from the individual, particularly markers specific for the LY6D-positive (+/- ATPAF1 mutant) proliferative SPB1/SPB subpopulation, to detect NPC relapse in the individual. Exemplary biomarkers (predictive of relapse) are cytological (cell type) biomarkers, particularly markers specific for the LY6D ⁺ proliferative SPB1/SPB epithelial subpopulation. Such biomarkers include mRNAs, proteins, peptides, or fragments thereof encoded by one or more of the following genes, the upregulation or downregulation of which can be used to detect relapse, as described herein: (A) LY6D ; KRT16 (keratin 16); CEBPD (CCAAT enhancer binding protein delta); CDKN1A (cyclin-dependent kinase inhibitor 1A); PGM2 (phosphoglucomutase 2); MEG3 (maternally expressed 3); CTNNBIP1 (catenin beta interacting protein 1); IGF2BP3 (insulin-like growth factor 2 mRNA binding protein 3); CLDND1 (claudin domain-containing 1); DUSP11 (bispecific phosphatase 11); FAF1 (Fas-associated factor 1); BAG4 (BAG co-chaperone 4); SIPA1L2 (signal-induced proliferation-associated 1-like 2); AP3M2 (adaptor-associated protein complex 3 subunit μ2); SERPINB12 (serine protease inhibitor family B member 12); CALML3 (calcineurin-like 3); CLCA4 (chloride channel agonist 4); GPX2 (glutathione peroxidase 2); and LSP1 (lymphocyte-specific protein 1) for detecting the presence of NPC relapse in an individual; the presence/high expression of one or more of these biomarkers in a biological sample obtained from the individual indicates the likelihood of future NPC relapse; and (B) for non-metaplastic LYD ⁺ cells, CLCA4 (chloride channel agonist 4); SYT8 (synaptic binding protein 8); FGFR3 (fibroblast growth factor receptor 3); SUSD4 (Sushi domain-containing 4); TNNT3 (tyrosin T3, fast skeletal type); NSG1 (neuronal vesicle transport-related 1), for detecting an individual experiencing an impending NPC relapse; the presence/low expression of one or more of these biomarkers in a biological sample obtained from the individual indicates that the individual is likely to experience a future NPC relapse.

Biomarker detection methods for detecting NPCR include, in some forms, detecting the expression of one or more of the following genes in a biological sample obtained from an individual: NEDD8 ; CALML3 ; NDUFA13 ; BEX3 ; HNRNPA0 ; SLIRP ; ADH5 ; GNG5 ; UBE2D2 ; PSMA4 ; SLC2A1 ; SNX3; LY6D ; PSMA3 ; PSMB1 ; YBX1 ; PSMB5 ; PSMA6; DHTKD1 , PRMT9 , SEH1L , MROH1 , MED25 , NCAN , GLS , DENND5B , COX8A , AUP1 , GAN . Expression can be determined by measuring mRNA, protein, peptide, or fragment thereof encoded by these genes. In some forms, prediction of NPC relapse is performed by detecting a combination of at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, or at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, or at least seventeen of the aforementioned genes. In some forms, mutations in eighteen of the aforementioned genes, e.g., NEDD8 ; CALML3 ; NDUFA13 ; BEX3 ; HNRNPA0 ; SLIRP ; ADH5 ; GNG5 ; UBE2D2 ; PSMA4 ; SLC2A1 ; SNX3; LY6D ; PSMA3 ; PSMB1 ; YBX1 ; PSMB5 ; and PSMA6, are used to detect NPC relapse. In some forms, detecting elevated RNA or protein levels of a relapse predictive biomarker selected from NEDD8, CALML3, NDUFA13, BEX3, HNRNPA0, SLIRP, ADH5, GNG5, UBE2D2, PSMA4, SLC2A1, SNX3, LY6D, PSMA3, PSMB1, YBX1, PSMB5, PSMA6, DHTKD1, PRMT9, SEH1L, MROH1, MED25, NCAN, GLS, DENND5B, COX8A, AUP1, and GAN is predictive of NPC relapse.

In some forms, increased expression of GAN, CYB561D2, DLST, PRMT9, OGA, PFDN4, CNDP1, PPP6R1, DPP3, ESD, CDH1, DAG1, AUP1, PGAM5, DAD1, FCN1, FGL1, SRGN, PGA3, PGA4, PGA5, FMOD can be used to predict local relapse.

In some forms, increased expression of GAS2L1, HDAC1, MARCHF2, TRIM28, TRAPPC11, ADAMTS7, HLA-DRB3, PKM, CXCL6, PRMT9, MROH1, MED25, NCAN, AMDHD1, PCSK1, ARFGAP1, ZNHIT1, ZNF326, CBX1, LRP5, SYNPO2, HIVEP1, DEFA3, HLA-DPB1, DUSP7, STOML3, GLS, DENND5B, AUP1, SLC17A5, SLC2A2 is predictive of distant relapse.

Increased expression of NID2, APP, XYLT2, GNPAT, MRPL17, CONDMKN, CTSA, TNFSF12, SRGN, PROZ, CALU, C1S, FGL1, and SOD3 can predict neck recurrence.

In some forms, prognosis of NPC recurrence can be determined when the RNA or protein level of any one or more of the aforementioned genes in an individual is about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 35%, about 40%, about 45%, about 50%, about 55%, about 60%, about 65%, about 70%, about 75%, about 80%, about 85%, about 90%, about 92%, about 94%, about 96%, about 98%, about 100% or more compared to a control individual or a predetermined control value. Decreased expression of HMGB2 , SNRPF , SRP14 , RANBP3L , SAMHD1 , SART3 , IFI16 , CROCC , NCF1B , SLIT1 , SH3BGR , IGHV3-53 , ANTXR1 , EPB42 , and/or SLC4A1 predicts relapse.

Reduced expression of ZNF428 , HDGFL2 , GALE , HNRNPL , LUC7L2 , HNRNPA1 , THYN1 , ANK3 , LAMTOR5 , MFGE8 , CD69 , REEP5 , CHST7 , ATP6V0A1 , RDX , SLIT1 , HLA-A , SH3BGR , SYNPO2 , CD2BP2 , SET , SELENOH , CCL19 , RHAG , CLEC3B , EZR , SLC4A1 , SLC29A1 , FRZB , and/or SEPTIN11 predicts local relapse.

Decreased expression of GASK1A , ANKRD30A , SMIM15 , RIPOR1 , B4GALT5 , ZNF846 , PRF1 , MINPP1 , FBL , RALYL , EPB41 , TMEM132D , NCF1B , JAG1 , CLEC14A , GALC , SNRPB2 , SLFN5 , CALU , SLC12A4 , CROCC , DSCAML1 , IGHV3-53 , TTN , IGHV4-39 , CYP2C9 , HSD11B1 , COL2A1 , and/or STAU2 predicted distant relapse.

Reduced expression of AP1B1 , CNBP , EXOC3L4 , LSM6 , ABCB6 , ANTXR2 , CLEC3B , LTF , F11 , C1QTNF3 , ANTXR1 , and/or PIP4K2A predicts neck recurrence.

In some forms, the presence of EBV DNA, RNA, or protein of RPMS1, LMP-1, or LMP-2B can detect NPC. High levels of EBV DNA, RNA, or protein of RPMS1, BALF4, BALF5, or BALF0 can indicate a prognosis for NPC recurrence.

B) Methods for detecting the presence of NPC markers ("NPCD") in a biological sample obtained from an individual using genetic markers and/or biomarker detection. Therefore, methods for detecting the presence of NPC markers (i.e., "NPCD") in a biological sample obtained from an individual include:

Genetic markers used to detect NPCD include detecting one or more genetic mutations in cells from a biological sample obtained from an individual. In these formats, the methods detect SNVs and/or indels, including but not limited to SNVs in EEF2KMT , SOCS1 , TESMIN , and IGFBP7 , and/or indels in EMP2 , IL32 , and/or CSTA , optionally in combination with EBV biomarkers. In these formats, the disclosed methods diagnose NPC in an individual with a sensitivity of at least about 80%, preferably at least 85%, 90%, or up to 95%.

Biomarkers for detecting NPCD include detecting expression (and therefore, increased/decreased expression) in a biological sample obtained from an individual. Gene expression can be determined by measuring mRNA or protein, peptide, or fragment thereof encoded by the gene.

In some forms, expression of one or more of the following genes is increased for detecting the presence of NPCs in an individual: CKAP4 , SYNGR2 , MARCKSL1 , CFL1 , PDCD5 , IL32 , RAN , NME1 , VCAM1 , GAPDH , TUBB , HSPD1 , LGALS1 , TNFAIP3 , ITGAV , RPLP0 , STMN1 , PPIA , CCL20 , FSCN1 , LGALS9 , RPS19 , YBX1 , TPI1 , ICAM1 , ENO1 , C1QBP , CXCL3 , MIF , TAGLN2 , CXCL10 , UBD , CSTA , NFKBIA , SOCS1 , MYBPC1 , NTRK2 , FKBP1A , SOX4 , TUBA1C , PYCARD , TCIM , and RPMS1 . The presence/increased levels of one or more of these biomarkers in a biological sample obtained from an individual indicates the presence of NPCs. In some forms, the disclosed methods diagnose NPCs in an individual with a sensitivity of at least about 80%, preferably at least 85% and up to 95%. In some forms, co-detection of increased expression of CCL20 (CCL20 high) + IL32 (IL32 high) + low expression of LCN2 (LCN2 low) achieves 100% sensitivity.

In some forms, decreased expression of any one or more of SLPI , WFDC2 , AQP3 , LCN2 , BPIFB1 , SERPINB3 , CLU , SCGB1A1 , CRIP1 , KRT14 , LYPD2 , TFF3 , TSPAN1 , SCGB3A1 , PIGR , MUC16 , C19orf33 , PRSS23 , MSMB , CAPS , CXCL17 , ANXA1 , AGR2, GSTA1, BPIFA1, MT1X, ALDH3A1, C20orf85, LGALS3, MUC4, and TACSTD2 can be used to detect NPC.

In some forms, low RNA and/or protein levels of any one or more of SLPI, WFDC2, AQP3, LCN2, BPIFB1, SERPINB3, CLU, SCGB1A1, CRIP1, KRT14, LYPD2, TFF3, TSPAN1, SCGB3A1, PIGR, MUC16, C19orf33, PRSS23, MSMB, CAPS, CXCL17, ANXA1, AGR2, GSTA1, BPIFA1, MT1X, ALDH3A1, C20orf85, LGALS3, MUC4, and TACSTD2 can be used to detect NPC. Low expression of ERAP2, COL3A1, RAVER1, EPHA1, MANEAL, MAP2K2, SIGIRR, BRD4, HEMGN, BDH2, AKAP12, TM4SF1, LCMT1, CLEC14A, IGHV3-13, TBC1D20, TRAPPC2; TRAPPC2B, WDR41, OXA1L, TTN, DST, TMEM132A, CKM, KMT2B, JMY, IGHV3-21, DNAH6, COCH, NSL1, SPTB, WWC3, KRT9, IGKV2-29, IGKV3D-20, and KIFC3 can detect new metastases in NPC.

In some forms, NPC can be detected when the RNA or protein level of any one or more of the aforementioned genes in an individual is about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 35%, about 40%, about 45%, about 50%, about 55%, about 60%, about 65%, about 70%, about 75%, about 80%, about 85%, about 90%, about 92%, about 94%, about 96%, about 98%, or about 100% lower than a control individual or a predetermined control value.

Also provided are nasopharyngeal sample collection swabs, sample collection tubes, and NPC analysis kits. The kits may include a lateral flow device and/or a nasopharyngeal swab and sample collection tube. In some forms, lateral flow analysis is a probe-based assay format in which a test sample flows along a solid matrix via capillary action, wherein the DNA/RNA/protein biomarker targets detected by the probe include, but are not limited to, antibodies or aptamers. The lateral flow device includes a solid matrix, such as a membrane strip, having an application site, an optional binding region, a capture region, and an absorbent. The binding agent is present in the binding region to bind to one or more of the biomarkers disclosed herein. The capture agent is immobilized in the capture zone, which preferably contains a plurality of capture lines for detecting the captured analyte (capture complex).

The binding agent in the capture zone can be an antibody or a biomarker-binding fragment thereof or an aptamer. In some forms, the binding agent in the capture zone includes an antibody that binds a biomarker selected from the group consisting of PSMA4, CALML3, SLC2A1, SNX3, LY6D, YBX1, and RPMS1, and the device can be used to detect NPCR. In some forms, the binding agent in the capture zone includes an antibody that binds a biomarker selected from the group consisting of CKAP4, SYNGR2, CFL1, and RPMS1, and the device can be used to detect NPCD. In some forms, the device includes multiple capture zones for simultaneous detection of more than one biomarker and/or simultaneous detection of NPCR and NPCD.

Furthermore, a computer-implemented system (CIS) and/or method (CIM) comprising one or more discriminative artificial intelligence (AI) platforms is described for analyzing biological data using a signature matrix and outputting the incidence of NPCR/NPCD based on the expression levels of certain biomarkers or combinations of biomarkers in the biological data, wherein certain biomarkers or combinations of biomarkers are indicative of certain cell types. The rows in the signature matrix track cell types, while the columns track genes, and the numbers at each i-th column and j-th row represent the relative levels of gene expression for a particular gene in a cell type. The AI platform evaluates the biological data (preferably test results from an individual) and predicts the occurrence of NPCR/NPCD based on a comparison with the expression levels of the genes or combinations of genes in the signature matrix. In a clinical setting, if the gene expression data in an individual's test results contains a greater abundance of enzymatic SPB1 signatures when compared to the signature matrix (i.e., if the gene expression is similar to the signature matrix), this indicates that enzymatic SPB1 is more abundant or present in the individual's test sample, and/or if the gene expression data contains less non-enzymatic SPB1, the individual is likely to have NPCR. The assessment is performed by uploading the signature matrix and the individual's test results to an AI platform that analyzes the data and makes predictions. Predictions are provided in a visual format (e.g., a graphical user interface), an audio format (e.g., via an audio signal reporting the prediction), or a combination thereof. One or more AI platforms have been trained and validated using data related to gene expression levels of these biomarkers, associated cell types, and/or the occurrence of NPCR/NPCD.

It should be understood that, unless otherwise specified, the methods and compositions disclosed are not limited to specific synthetic methods, specific analytical techniques, or specific reagents, and therefore may vary. It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

Methods have been developed for detecting the development of nasopharyngeal carcinoma (NPC) in individuals and/or predicting NPC recurrence. The disclosed methods, based on the discovery of genetic mutations (SNVs and inDels) and expression biomarkers and cytological markers, demonstrate higher accuracy, specificity, positive predictive value, and sensitivity for diagnosing NPC and predicting NPC recurrence compared to existing expression biomarkers (such as plasma EBV DNA). These methods utilize analytes in samples from individuals for detection.

The expression of biomarkers can be assessed by measuring the RNA or protein encoded by the gene, as discussed in detail below. Thus, high/low expression of a gene can be determined by measuring the level of RNA or protein encoded by the gene.

The expression level of a biomarker (as disclosed herein) is compared to a control subject or a predetermined control value to determine "increased" or "decreased" expression of the biomarker.

The analyte to be detected may be any one or more of RNA, DNA, protein, peptide, or fragments thereof. For example, the analyte to be detected may be RNA. In some forms, the analyte to be detected is DNA. In some forms, the analyte to be detected is a protein, peptide, or fragment thereof. In some forms, a combination of two or more analytes is detected. For example, the analyte to be detected may be a combination of a nucleic acid and a peptide or protein. For example, the combination of analytes to be detected may be a protein biomarker, such as a NPC recurrence prediction protein biomarker, and a nucleic acid, such as a single nucleotide variant (SNV) associated with NPC. In another example, the analyte to be detected may be two nucleic acids, for example, one nucleic acid may be an SNV associated with NPC and the second nucleic acid may be an InDel mutation.

One of the fundamental reasons for using nucleic acid (e.g., DNA, RNA) and protein biomarker panels in the nasopharynx or plasma for non-invasive NPC diagnosis and recurrence prediction is that nucleic acids, such as DNA/RNA or proteins, with the described characteristics can be released from proliferative cells in various locations into nearby nasopharyngeal tissues or into plasma/lymph. Probes that detect or amplify these biomarkers can be used in nasopharyngeal secretions or plasma.

These embodiments are described in further detail below. The disclosed methods can use a combination of detecting mutations, RNA expression, and analyte expression to detect NPC or predict NPC recurrence.

I. Definition

The term "assay" refers to an in vitro procedure used to analyze a sample to determine the presence, absence, or amount of one or more analytes of interest.

The terms "control" and "calibrator" are used interchangeably in connection with the analyte and refer to the analyte used as an internal standard.

The term "analyte" refers to the chemical substance of interest that is a potential component of a biological sample and is analyzed by a detection method.

"Lateral flow" assays are devices intended to detect the presence (or absence) of a target analyte in a sample, in which the test sample flows along a solid matrix via capillary action.

As used herein, the term "membrane" refers to a solid matrix with sufficient porosity to allow antibodies or aptamers bound to the analyte to migrate along its surface and through its interior by capillary action.

The term "membrane strip" or "test strip" refers to a membrane of sufficient length and width to allow for the separation and detection of analytes.

The term "application point" is the location on the membrane where fluid can be applied.

The term "immobilization" refers to chemically or physically fixing a reagent or particle to a location on or within a matrix (such as a membrane). For example, a capture agent can be chemically bound to a membrane, and particles coated with the capture agent can be physically trapped within the membrane.

The term "capture particles" refers to particles coated with multiple capture agents. In a preferred embodiment, the capture particles are fixed in a defined capture zone.

The term "capture zone" refers to the point on the membrane strip where one or more capture agents are immobilized.

The term "antibody" refers to whole immunoglobulin molecules, fragments or polymers of immunoglobulin molecules, single-chain immunoglobulin molecules, human or humanized forms of immunoglobulin molecules, and recombinant immunoglobulin molecules, so long as they are selected for their ability to bind an analyte.

The term "aptamer" refers to an oligonucleotide or peptide molecule that binds to a specific target molecule. Aptamers are typically selected from a pool of random sequences and are capable of forming a unique tertiary structure, allowing them to identify their target molecule with high affinity and specificity.

Aptamers are oligonucleotides that bind to target molecules through their conformation. Aptamers can be composed of DNA, RNA, or a combination thereof. Aptamers are typically engineered using SELEX (systematic evolution of ligands by exponential enrichment).

Peptide aptamers are combinatorial peptide molecules with random amino acid sequences that are selected for their ability to bind to a target molecule. Aptamers are typically selected from combinatorial peptide libraries using yeast double hybrid or phage display assays.

The term "biological sample" refers to a tissue (e.g., a tissue biopsy), organ, cell, cell lysate, or body fluid from an individual. Non-limiting examples of body fluids include blood, urine, plasma, serum, tears, lymph, bile, cerebrospinal fluid, interstitial fluid, aqueous or vitreous humor, colostrum, sputum, amniotic fluid, saliva, anal and vaginal secretions, sweat, semen, exudates, secretions, and synovial fluid.

As used herein, a "sample collection device" refers to a device that can be used to collect a biological sample or in which a collected biological sample can be deposited or stored.

The term "metatype" refers to the analyte binding site of a binding agent when bound to an analyte. The term "individual genotype" refers to the analyte binding site of a binding agent in the absence of an analyte.

The term "anti-metatype" refers to a binding agent that selectively recognizes the binding agent-analyte complex (the metatype) but lacks specificity for free analyte or free binding agent. The term "anti-idiotype" refers to a binding agent that selectively recognizes the analyte binding site of another binding agent.

The terms "specifically bind" or "selectively bind" refer to a binding reaction that determines the presence of an analyte in a heterogeneous population. Generally, a first molecule that "specifically binds" a second molecule has an affinity constant (Ka) for the second molecule that is greater than about 10 ⁵ M ^-1 (e.g., 10 ⁶ M ^-1 , 10 ⁷ M ^-1 , 10 ⁸ M ^-1 , 10 ⁹ M ^-1 , 10 ¹⁰ M ^-1 , 10 ¹¹ M ^-1 , and 10 ¹² M ^-1 or greater).

The term "detectable tag" refers to any moiety that can be selectively detected in a screening assay. Examples include radiolabels (e.g., ^3H , ^14C , ^35S , ^125I , ^131I ), affinity tags (e.g., biotin/avidin or streptavidin), antibody binding sites, metal-binding domains, epitope tags, fluorescent or luminescent moieties (e.g., luciferin and derivatives, green fluorescent protein (GFP), rhodamine and derivatives, phospholipids), colorimetric probes, and enzymatic moieties (e.g., horseradish peroxidase, β-galactosidase, β-lactamase, luciferase, alkaline phosphatases).

As used herein, the term "sensitivity" refers to the ability of a test to correctly identify true positives, i.e., patients with NPC (or predict patients who will relapse). For example, sensitivity can be expressed as a percentage, i.e., the proportion of true positives correctly identified as such (e.g., the percentage of individuals with NPC correctly identified by the test as having NPC). A test with high sensitivity has a low rate of false negatives, i.e., cases in which the test fails to correctly identify NPC.

As used herein, the term "specificity" refers to a test's ability to correctly identify true negatives (i.e., individuals who do not have NPC). For example, specificity can be expressed as a percentage, representing the proportion of individuals who are truly negative that are correctly identified as such (e.g., the percentage of individuals who do not have NPC who are correctly identified as not having NPC by the test). A test with high specificity has a low rate of false positives, meaning individuals who do not have NPC are mistakenly identified as having NPC by the test.

Recitation of ranges of values herein are merely intended to serve as shorthand for referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is considered to be a part of this document as if it were individually recited herein.

The use of the term "about" is intended to describe values that are within a range of about +/- 10% above or below the stated value; in other embodiments, the values may be within a range of values that are within a range of about +/- 5% above or below the stated value; in other embodiments, the values may be within a range of values that are within a range of about +/- 2% above or below the stated value; in other embodiments, the values may be within a range of values that are within a range of about +/- 1% above or below the stated value. The foregoing ranges are intended to be clear from the context and no further limitations are implied.

All methods described herein can be performed in any suitable order unless otherwise indicated or clearly contradicted by the context. The use of any and all examples or exemplary language (e.g., "such as") provided herein is intended merely to better illuminate the embodiments and does not limit the scope of the embodiments unless otherwise claimed. No language in this specification should be construed as indicating any non-claimed element as essential to the practice of the invention.

Disclosed are substances, compositions, and components that can be used in, in conjunction with, in preparing, or as products of the disclosed methods and compositions. These and other materials are disclosed herein, and it is understood that when combinations, subcombinations, interactions, groups, etc. of these materials are disclosed, while specific references for various individual and collective combinations and arrangements of these compounds may not be explicitly disclosed, each is specifically contemplated and described herein. For example, unless specifically indicated to the contrary, if a ligand is disclosed and discussed and various modifications that can be made to various molecules comprising the ligand are discussed, each combination and arrangement of the ligand and possible modifications are specifically contemplated. Thus, if examples of a class of molecules A, B, and C and a class of molecules D, E, and F, and a combination of molecules A-D, are disclosed, each is individually and collectively contemplated, even though each is not individually recited. Thus, in this example, each of the combinations A-E, A-F, B-D, B-E, B-F, C-D, C-E, and C-F is specifically contemplated and should be considered disclosed according to the disclosure of A, B, and C; D, E, and F; and the example combination A-D. Similarly, any subset or combination of these molecules is also specifically contemplated and disclosed. Thus, for example, the subsets A-E, B-F, and C-E are specifically contemplated and should be considered disclosed according to the disclosure of A, B, and C; D, E, and F; and the example combination A-D. Furthermore, each of the substances, compositions, components, etc., as contemplated and disclosed above, may also be specifically and individually included in or excluded from any group, subgroup, list, collection, etc. of such substances.

These concepts apply to all aspects of the invention, including but not limited to steps in methods of making and using the disclosed compositions. Thus, if there are a variety of additional steps that can be performed, it is understood that each of these additional steps can be performed with any specific embodiment or combination of embodiments of the disclosed methods, and each such combination is specifically contemplated and should be considered disclosed.

All methods described herein can be performed in any suitable order unless otherwise indicated or clearly contradicted by the context. The use of any and all examples or exemplary language (e.g., "such as") provided herein is intended merely to better illuminate the embodiments and does not limit the scope of the embodiments unless otherwise claimed. No language in this specification should be construed as indicating any non-claimed element as essential to the practice of the invention.

II. NPC Diagnosis and recurrence detection

The disclosed methods can detect the presence of one or more analytes in a biological sample obtained from an individual. The one or more analytes can be DNA, RNA, proteins, or peptide fragments thereof. The disclosed methods can detect the presence/absence of specific genetic mutations, the expression (RNA/protein expression) of specific biomarkers, and the presence/absence of specific cell types, including the presence of specific mutations and the expression of specific biomarkers in these specific cell types. Abbreviations used in this disclosure are known in the art. Tables 2 and 3 provide commercially available sources of antibodies specific for the disclosed biomarkers.

In the disclosed methods, the presence, absence, or optimal amount of an analyte in a biological sample is assessed. In a preferred form, the biological sample includes plasma, cells, and fluid from the nasopharynx, which is the upper part of the pharynx that connects to the nasal cavity above the soft palate.

In another preferred embodiment, the biological sample includes cells and tissues, such as cells and/or tissues from the nasopharynx and lymph nodes. In a specific embodiment, the biological sample includes nasopharyngeal tumor cells, such as tumor cells extracted from a nasopharyngeal tumor. In other embodiments, the biological sample is a body fluid, such as whole blood, plasma, serum, saliva, or oral fluid.

A 、 Detection NPC relapse

Disclosed methods for detecting the presence of NPC recurrence (i.e., "NPCR") in a biological sample obtained from an individual utilize genetic marker (detection of gene mutations), biomarker (detection of biomarker expression), and/or cytological (cell type-specific) analyte detection. As discussed further below, cytological analyte detection includes detection of cell type-specific gene mutations and/or cell type-specific biomarker expression.

Detection of mutations : In some formats, NPC recurrence can be determined by detecting the presence of one or more of the following mutations, as shown in the table below, in a sample obtained from the individual: Project Gene Chromosome location mutation Mut #1 IL32 chr16:3,065,801 [T＞C] SNV mutation Mut#2 DHX57 chr2:38,868,300 [T＞A] SNV mutation Mut#3 HMGN2P3 chr16:26,032,755 [G＞A] SNV mutation Mut#4 DNAJC11 chr1:6,667,742 [TTC＞T.TTCTC] InDel mutation Mut #5 eIF2AK1 chr7:6,046,108 [A＞AT] InDel mutation Mut#6 FAM234A chr16:254,566 [G＞GT] InDel mutation Mut#7 PARPBP chr12:102,123,938 [G＞A.GA] InDel mutation Mut#8 ARL5A chr2:151,828,247 [A＞G] SNV mutation Mut#9* Mut#1+ Mut#2 / / Mut #10 ATPAF1 chr1:46,668,177 [A＞C] SNV mutation OR p.Val49Gly Mut#11 HDAC2 chr6:113,970,875 [CT＞C,CTT,CTTT,CTTTT,CTTTTT] InDel mutation Mut#12 TACSRD2 chr1:58,577,099 [CCAG＞C] InDel mutation OR p.Leu19del Mut#13 LMO4 1:87328973 [A＞T] SNV mutation

Figure 1E (NPC relapse predictive SNV-InDel DNA and RNA mutation biomarkers). In some formats, NPC relapse is determined by a combination of mutations in at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, or at least eleven of the aforementioned genes. In some formats, mutations in all twelve of the aforementioned genes are used to detect NPC relapse. In a preferred embodiment, the method detects the IL32 (chr16: 3,065,801) [T>C] SNV mutation and the DHX57 (chr2: 38,868,300) [T>A] SNV mutation.

Detection of Biomarker Expression : In some forms, NPC relapse can be determined by detecting the expression of one or more genes selected from the group consisting of: NEDD8 (neuronal progenitor cell expressed down-regulated protein 8), CALML3 (calcineurin-like protein 3), NDUFA13 (NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 13), BEX3 (brain expressed X-linked protein 3), HNRNPA0 (heterogeneous nuclear ribonucleoprotein A0), SLIRP (SRA stem-ring interacting RNA binding protein), ADH5 (alcohol dehydrogenase 5), GNG5 (guanine nucleotide binding protein G(I)/G(S)/G(O) subunit gamma-5), UBE2D2 in a sample obtained from an individual. (E3 ubiquitin-protein ligase E3D), PSMA4 (proteasome subunit alpha type 4), SLC2A1 (solute carrier family 2, glucose transporter member 1), SNX3 (sorting ligase-3), LY6D (lymphocyte antigen 6D), PSMA3 (proteasome subunit alpha type 3), PSMB1 (proteasome subunit beta type 1), YBX1 (Y-box binding protein 1), PSMB5 ( proteasome subunit beta type 5), PSMA6 ( proteasome subunit alpha 6), DHTKD1, PRMT9, SEH1L, MROH1, MED25, NCAN, GLS, DENND5B, COX8A, AUP1 and GAN (Figure 1E, (RNA and protein expression biomarkers for NPC recurrence prediction). The expression of each gene can be detected by measuring RNA or protein, peptide or fragment thereof encoded by one or more genes in a biological sample obtained from an individual. Increased expression of one or more of these genes in an individual compared to a control individual or a predetermined control value indicates NPCR. GAN, CYB561D2, DLS Increased expression of T, PRMT9, OGA, PFDN4, CNDP1, PPP6R1, DPP3, ESD, CDH1, DAG1, AUP1, PGAM5, DAD1, FCN1, FGL1, SRGN, PGA3, PGA4, PGA5, and FMOD predicted local recurrence. GAS2L1, HDAC1, MARCHF2, TRIM28, TRAPPC11, ADAMTS7, HLA-DRB3, PKM, CXCL6, PRMT9, MROH1, MED25, NCAN, Increased expression of AMDHD1, PCSK1, ARFGAP1, ZNHIT1, ZNF326, CBX1, LRP5, SYNPO2, HIVEP1, DEFA3, HLA-DPB1, DUSP7, STOML3, GLS, DENND5B, AUP1, SLC17A5, and SLC2A2 predicted distant relapse. NID2, APP, XYLT2, GNPAT, MRPL17, CONDMKN, CTSA, TNFSF12, SRGN, PROZ, CALU, C1S, Increased expression of FGL1 and SOD3 is predictive of cervical relapse. In some forms, a predictive outcome of NPC relapse is determined when the RNA or protein level of any one or more of the aforementioned genes in a subject is about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 35%, about 40%, about 45%, about 50%, about 55%, about 60%, about 65%, about 70%, about 75%, about 80%, about 85%, about 90%, about 92%, about 94%, about 96%, about 98%, about 100% or more higher than a control subject or a predetermined control value.

Reduced expression of HMGB2, SNRPF, SRP14, RANBP3L, SAMHD1, SART3, IFI16, CROCC, NCF1B, SLIT1, SH3BGR, IGHV3-53, ANTXR1, EPB42, and SLC4A1 predicted relapse. Reduced expression of ZNF428, HDGFL2, GALE, HNRNPL, LUC7L2, HNRNPA1, THYN1, ANK3, LAMTOR5, MFGE8, CD69, REEP5, CHST7, ATP6V0A1, RDX, SLIT1, HLA-A, SH3BGR, SYNPO2, CD2BP2, SET, SELENOH, CCL19, RHAG, CLEC3B, EZR, SLC4A1, SLC29A1, FRZB, and SEPTIN11 predicted local relapse. Decreased expression of GASK1A, ANKRD30A, SMIM15, RIPOR1, B4GALT5, ZNF846, PRF1, MINPP1, FBL, RALYL, EPB41, TMEM132D, NCF1B, JAG1, CLEC14A, GALC, SNRPB2, SLFN5, CALU, SLC12A4, CROCC, DSCAML1, IGHV3-53, TTN, IGHV4-39, CYP2C9, HSD11B1, COL2A1, and STAU2 predicted distal relapse. Decreased expression of AP1B1, CNBP, EXOC3L4, LSM6, ABCB6, ANTXR2, CLEC3B, LTF, F11, C1QTNF3, ANTXR1, and PIP4K2A predicted cervical relapse.

In some forms, prediction of NPC relapse is achieved by detecting a combination of at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, or at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, or at least seventeen of the aforementioned genes. In some forms, mutations in eighteen of the aforementioned genes ( NEDD8 ; CALML3 ; NDUFA13 ; BEX3 ; HNRNPA0 ; SLIRP ; ADH5 ; GNG5 ; UBE2D2 ; PSMA4 ; SLC2A1 ; SNX3 ; LY6D; PSMA3 ; PSMB1 ; YBX1 ; PSMB5 ; PSMA6) can be used to detect NPC relapse.

Detection of Cytological Analytes : In some formats, NPC recurrence can be determined by detecting in a sample obtained from an individual: (i) LY6D-positive (LY6D ⁺ ) mesenchymal cells, and/or (ii) specific mutations in LY6D ⁺ mesenchymal cells, and/or (iii) expression of specific biomarkers by LY6D ⁺ mesenchymal cells, and/or (iv) expression of specific biomarkers by LY6D ⁺ non-mesenchymal cells (Figure 1E (Alternative Predictive Cytological Biomarkers for NPC (Identifiable by Cell Type-Specific and Protein Biomarkers))). In each case, increased/decreased expression was determined by comparing epithelial cell expression of the same biomarker.

LY6D ⁺ mesenchymal cells : In some forms, NPC relapse is indicated by the presence of LY6D ⁺ mesenchymal cells, as reflected by the presence or increased expression of LY6D . Increased expression of LY6D is indicative of NPCR. In some preferred forms, NPC relapse prediction is performed using LY6D IHC as an example, ^and an Allred score ≥ 6 is indicative of NPC relapse. The modified Allred scoring system is a well-known, clinically validated scoring system, as shown in Table 1 (described in Arihilo et al., Am J Clin Pathol 2007;127(3):356-365).

Table 1: Modified Allred score Proportional Rating Intensity scoring (membrane and / or cytoplasmic staining) Modified Allred score 0 No cells stained positive by IHC 0 → Negative 0-1 → Negative 1 ≤1% of cells positively stained by IHC 1 → weak 2-3 → weak 2 1% to 10% of cells were positively stained by IHC 2 → Medium 4-6 → Moderate 3 11% to 33% of cells were positively stained by IHC 3 → Strong 7-8 → Strong 4 34% to 66% of cells were positively stained by IHC 5 67% to 100% of cells were positively stained by IHC

Specific mutations in LY6D ⁺ cells: In some forms, NPC recurrence is indicated by the presence of HDAC2 (chr6:113,970,875) [CT＞C,CTT,CTTT,CTTTT,CTTTTT] InDel and/or ATPAF1 (chr1:46668177) [A＞C]) mutant LY6D ⁺ proliferative secretory-inducing basal cluster 1 (SPB1) cells in samples obtained from the individual. In some forms, NPC recurrence is indicated by the presence of the following in a sample obtained from the individual: an in-frame deletion at TACSRD2 (chr 1:5857099) present in LY6D ⁺ mesenchymal SPB1 cells in the sample; and/or a 5'UTR single nucleotide mutation (A to T) at LMO4 (chr 1:87328973) present in LY6D ⁺ mesenchymal SPB1 cells in the sample.

The presence of one or more mutations indicates relapse, and the absence of the mutations disclosed herein indicates the absence of relapse. Preferably, the method detects relapse with at least 80% specificity. In a preferred embodiment, the presence of IL32 (interleukin 32) (SNV) and DHX57 (DExH box helicase 57) (SNV) indicates relapse with 100% sensitivity.

Expression of specific biomarkers by LY6D ⁺ mesenchymal cells: In some formats, NPC relapse detection is indicated by the presence of LY6D ⁺ mesenchymal SPB cells and/or increased expression of KRT16 , CEBPD , CDKN1A , PGM2 , and LY6D compared to the expression of these biomarkers in epithelial cells.

In some forms, prediction of NPC relapse is indicated by the presence of LY6D ^{+ proliferative} SPB1 cells, as reflected by the presence and/or increased expression of MEG3 , CTNNBIP1 , and LY6D compared to the expression of these biomarkers in epithelial cells.

In some forms, prediction of NPC relapse is indicated by the presence of LY6D ^{+ proliferative} SPB2 cells, as reflected by the presence and/or increased expression of IGF2BP3 , FAF1 , DUSP11 , CLDND1 , and LY6D compared to the expression of these biomarkers in epithelial cells.

In some forms, prediction of NPC relapse is indicated by the presence of LY6D ^{+ proliferative} SPB5 cells, as reflected by the presence and/or increased expression of BAG4 , SERPINB12 , AP3M2 , SIPA1L2 , and LY6D compared to the expression of these biomarkers in epithelial cells.

In some forms, NPC relapse prediction is indicated by the presence of "highly tumorigenic" proliferative SPB cells, as reflected by the expression of CALML3 , CLCA4 , GPX2 , LSP1 , and LY6D .

In some formats, prediction of NPC relapse is indicated by detecting the presence of LY6D ⁺ non-metaplastic SPB cells expressing a specific combination of biomarkers in a sample obtained from an individual. In these formats, low levels of (i) LY6D+ non-metaplastic SPB cells as reflected by expression of CLCA4 , SYT8 , FGFR3 , and LY6D and/or (ii) LY6D ⁺ non ^- metaplastic SPB1 cells as reflected by expression of SUSD4 , TNNT3 , NSG1 , and LY6D are indicative of NPCR.

The disclosed methods encompass combinations of cytological analytes for detection. For example, in some forms, NPC relapse prediction is indicated by: (i) the presence of HDAC mutant mesenchymal SPB1 cells, as reflected by the presence of mesenchymal SPB1 cells with HDAC2 InDel mutations; (ii) ATPAF1 mutant mesenchymal SPB1 cells, as reflected by the presence of mesenchymal SPB1 cells with ATPAF1 SNV mutations; (iii) low levels of non-mesenchymal SPB cells, as reflected by the expression of CLCA4 , SYT8 , FGFR3 , and LY6D ; (iv) low levels of non-mesenchymal SPB1 cells, as reflected by the RNA/protein expression of SUSD4 , TNNT3 , NSG1 , and LY6D ; and (v) highly tumorigenic mesenchymal SPB cells, as reflected by the expression of CALML3. , CLCA4 , GPX2 , LSP1 and LY6D expression.

Table A: Overview of Additional Biomarker Types Biomarker Type Diagnosis / Recurrence instruct Biomarker List HDAC mutant proliferative SPB1 cells relapse "Presence" indicates "NPCR" MEG3, CTNNBIP1, LY6D, and HDAC mutations ATPAF1 mutant proliferative SPB1 cells relapse "Presence" indicates "NPCR" MEG3, CTNNBIP1, LY6D, and ATPAF1 mutations EBV (DNA/RNA/protein) relapse High levels indicate NPCR RPMS1, BALF4, BALF5, BALF0 EBV (DNA/RNA/protein) Diagnosis "Existence" indicates "NPCD" RPMS1, LMP-1, LMP-2B

B 、 Detection NPC Existence

Detection of mutations: In some formats, the presence of NPC (i.e., NPCD) can be determined by detecting the presence of one or more of the following mutations, as shown in the table below, in a sample obtained from an individual: Project Gene Chromosome location mutation Mut #1 EMP2 chr16:10,543,633 [C＞CA] InDel mutation Mut#2 IL32 chr16:3,065,653 [AAGGTGACT……CGCCAGC＞A,AAGC] InDel mutation Mut#3 EEF2KMT chr16:5,091,852 [G＞A] SNV mutation Mut#4 CSTA chr3:122,337,565 [G＞GA] InDel mutation Mut #5 SOCS1 chr16:11,255,404 [A＞C] SNV mutation Mut#6 TESMIN chr11:68,750,666 [A＞T] SNV mutation Mut#7 IGFBP7 chr4:57,110,068 [T＞C] SNV mutation

In some forms, combinations of mutations in at least two, at least three, at least four, at least five, or at least six of the aforementioned genes can be used to detect NPC. In some forms, NPC is detected by detecting mutations in the seven genes described above (EMP2 + IL32 + EEF2KMT + CSTA + SOCS1 + TESMIN + IGFBP7).

The presence of a specified mutation in a biological sample obtained from an individual indicates NPC, and the absence of the mutation indicates the absence of NPC. The disclosed methods diagnose NPC in an individual with a sensitivity of at least about 80%, preferably at least 85%, 90%, or up to 95% (using the identification of the mutations described above).

Biomarker Expression Detection: In some forms, the presence of NPC (i.e., NPCD) can be determined by detecting the expression of one or more of the following genes in a sample obtained from an individual, wherein in some forms, high expression is indicative of NPC, and in some forms, low expression is indicative of NPC, as further disclosed below: In some forms, high expression of any one or more of the following: CKAP4 (cytoskeleton-associated protein 4), SYNGR2 (neural cell vesicle recycling protein (Synaptogyrin) 2), MARCKSL1 (macrophage myristoylated alanine-rich C kinase substrate), CFL1 (Protein CURLY FLAG LEAF 1), PDCD5 (programmed cell death protein 5), IL32 , RAN (Ras-associated nuclear protein), NME1 (N-methyl D-aspartate receptor subtype 2A), VCAM1 (vascular cell adhesion protein 1), GAPDH (glyceraldehyde-3-phosphate dehydrogenase), TUBB (tubulin beta class I), HSPD1 (60 kDa heat shock protein), LGALS1 (galectin-1), TNFAIP3 (TNFAIP3-interacting protein 2), ITGAV (integrin subunit α V), RPLP0 (large ribosomal subunit protein uL10), STMN1 (microtubule-destabilizing protein 1), PPIA (peptidyl-prolyl cis-trans isomerase A), CCL20 , FSCN1 (fasciclin 1), LGALS9 (galectin-9), RPS19 (small ribosomal subunit protein eS19), YBX1 (Y-box binding protein 1), TPI1 (triosephosphate isomerase), ICAM1 (intercellular adhesion molecule 1), ENO1 (enolase 1), C1QBP (complement component 1 Q component binding protein), CXCL3 (CXC motif interleukin 3), MIF (macrophage migration inhibitory factor), TAGLN2 (transglutaminase-1), CXCL10 (CXC motif interleukin 10), UBD (ubiquitin D), CSTA (peptide transporter CstA), NFKBIA (NF-κB inhibitor α), SOCS1 (suppressor of interleukin signaling 1), MYBPC1 (myosin binding protein C), NTRK2 (DNF/NT-3 growth factor receptor B), FKBP1A (Peptidyl-prolyl cis-trans isomerase FKBP1A), SOX4 (transcription factor SOX-4), TUBA1C (tubulin α-1C chain), PYCARD (CARD-containing apoptosis-associated speck-like protein) and TCIM (transcription and immune response modulator). Higher expression of PBX2 , CDK13 , NPEPL1 , DUSP19 , BPNT1 , LRRC75A , USP47 , MAGT1 , TARDBP , NDUFA12 , ATOX1 , MOSPD2 , HBM , VTI1A , NFE2 , MTHFS , IGFBP1 , GCG , PRAP1 , SYNPO2 , FNIP2 , NCAN , CRP , WWC3 , GAN , DPY19L1 , DHCR7 , CGNL1 , SLC25A1 , OMD , DPT , HLA-DRB3 , NTS , TRIM41 , and CCL17 indicates de novo metastasis.

NPC is detected when the level of RNA or protein expressed by any one or more of the aforementioned genes in a sample obtained from the individual is about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 35%, about 40%, about 45%, about 50%, about 55%, about 60%, about 65%, about 70%, about 75%, about 80%, about 85%, about 90%, about 92%, about 94%, about 96%, about 98%, about 100% or more compared to a control individual or a predetermined control value.

In some forms, underexpression of any one or more of the following can detect NPC: SLPI (WD repeat-containing protein slp1), WFDC2 (WAP tetra-disulfide core domain protein 2), AQP3 (aquaporin-3), LCN2 (lipocalin 2), BPIFB1 (BPI fold-containing family B member 1), SERPINB3 (serine protease inhibitor family B member 3), CLU (clustin), SCGB1A1 (secretory globulin family 1A member 1), CRIP1 (cysteine-rich protein 1), KRT14 (keratin 14), LYPD2 (lymphocyte antigen 6 family member D2), TFF3 (thrifty factor 3), TSPAN1 (tetraspanin 1), SCGB3A1 ( secretory globulin family 3A member 1), PIGR (polymeric immunoglobulin receptor), MUC16 (mucin 16), C19orf33 (chromosome 19 open reading frame 33), PRSS23 (protease, serine 23), MSMB (microsperm protein beta), CAPS (calcyphosine), CXCL17 (CXC motif chemokine ligand 17), ANXA1 (annexin A1), AGR2 ( pro-gradient protein 2), GSTA1 (glutathione S-transferase alpha 1), BPIFA1 (BPI-fold containing family A member 1), MT1X (metallothionein 1X), ALDH3A1 (aldehyde dehydrogenase 3 family member A1), C20orf85 (chromosome 20 open reading frame 85), LGALS3 (galectin 3), MUC4 ( mucin 4) and TACSTD2 (tumor-associated calcium signal transducer 2). Low expression of ERAP2 , COL3A1 , RAVER1 , EPHA1 , MANEAL , MAP2K2 , SIGIRR , BRD4 , HEMGN , BDH2 , AKAP12 , TM4SF1 , LCMT1 , CLEC14A , IGHV3-13 , TBC1D20 , TRAPPC2 ; TRAPPC2B , WDR41 , OXA1L , TTN , DST , TMEM132A , CKM , KMT2B , JMY , IGHV3-21 , DNAH6 , COCH , NSL1 , SPTB , WWC3 , KRT9 , IGKV2-29 , IGKV3D-20 , and KIFC3 detects de novo metastasis in NPC.

NPC is detected when the level of RNA or protein expressed by any one or more of the aforementioned genes in a sample obtained from the individual is about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 35%, about 40%, about 45%, about 50%, about 55%, about 60%, about 65%, about 70%, about 75%, about 80%, about 85%, about 90%, about 92%, about 94%, about 96%, about 98%, or about 100% lower than that of a control individual or a predetermined control value.

III. Biomarker detection

FIG1 is a schematic diagram showing exemplary proposed sampling methods and detection strategies utilizing the disclosed (1) human-based DNA, RNA/protein biomarker panels, (2) EBV-based panels, and (3) cytology panels for either (1) non-invasive rapid testing using biological samples from nasopharyngeal swabs or plasma, or (2) learning invasive endoscopic biopsy.

Protein expression can be detected using antibodies that bind to the protein and protein detection methods known in the art. mRNA can be detected using real-time polymerase chain reaction (RT-PCR) (primers are available at https://pga.mgh.harvard.edu/primerbank/).

RNA and protein expression can be measured using methods including, but not limited to, immunohistochemistry (IHC)/enzyme-linked immunosorbent assay (ELISA) and lateral flow assays. RNA and protein can be detected using lateral flow assays such as rapid antigen tests (antibody/aptamer probe-based lateral flow assays targeting DNA, RNA, and protein biomarkers).

The presence of one or more mutations can be assessed in samples obtained from an individual using methods such as DNA sequencing, droplet digital polymerase chain reaction (ddPCR) (using primers designed using the Bio-Rad platform at https://www.bio-rad.com/digital-assays/assays-create/mutation), RT-PCR, aptamer sequencing, and single-cell RNA sequencing.

Flow cytometry (single-cell-based assays targeting cell-type-specific, DNA, RNA, and protein biomarkers) may be used.

Other methods such as FISH (fluorescence in situ hybridization) and immunohistochemistry can be used to detect specific biomarkers as disclosed therein.

Real-time PCR, microarrays, sc-RNA sequencing, next-generation sequencing, and RNA or DNA single-cell sequencing can be used to detect SNVs and InDels in one or more of the genes disclosed herein, which can detect the presence of NPC or the recurrence of NPC. Single nucleotide variants (SNVs) are variations of a single nucleotide in the genome of a population. RNA sequencing (RNA-seq) is a genome-wide method for detecting and quantifying messenger RNA molecules in biological samples. scRNA-seq allows the comparison of the transcriptomes of individual cells. The first and most important step in performing scRNA-seq is to effectively isolate live single cells from the tissue of interest. The isolated individual cells are then lysed to allow the capture of as many RNA molecules as possible. To specifically analyze polyadenylated mRNA molecules and avoid capturing ribosomal RNA, poly[T] primers are often used. Poly[T]-primed mRNA is then converted to complementary DNA (cDNA) by reverse transcriptase. A practical guide to single-cell RNA sequencing for biomedical research and clinical applications is provided by Haques et al., Genome Medicine 9, 75, 2017.

Lateral flow analysis can be used to detect one or more of the expression biomarkers identified and disclosed herein, which detect the presence of NPC or the presence of NPC recurrence. Relevant mutations and expression biomarkers are discussed above.

The assay typically involves combining a biological sample with an analytical fluid, an analyte-binding agent that specifically binds to the drug analyte (e.g., an antibody or aptamer), a calibration/control analyte, and a calibration/control binding agent that specifically binds to the calibration analyte. Depending on whether the analyte is present in the fluid sample and bound to the analyte-binding agent on the binding particle, the captured particles may or may not have the analyte bound to the analyte-binding agent. Because capture particles have multiple binding sites for analytes, the amount and concentration of analytes bound to the particles vary. The concentration of analytes bound to the particles increases proportionally with the amount of analyte present in the fluid sample, and the probability of particles being trapped in the sample capture zone also increases with the amount of analyte bound to the drug-binding agent on the particles. Therefore, a population of exposed bound particles can contain particles with varying amounts of analyte bound to the analyte-binding agent, as well as particles without analyte bound to the drug-binding agent.

In some forms, the NPC analyte and the control analyte have similar physical properties. For example, a preferred control analyte is a molecule of similar size to the NPC analyte of interest. In some forms, the analyte-binding agent and the control binding agent also have similar properties. For example, if the analyte-binding agent is an antibody, the calibration binding agent is also preferably an antibody. Furthermore, the affinity and/or avidity of the calibration/control binding agent for the calibration/control analyte is preferably comparable (e.g., within an order of magnitude) to the affinity and/or avidity of the analyte-binding agent for the NPC analyte.

Preparation of sample buffer

In one embodiment, an analytical fluid, such as a sample buffer, is introduced into a biological sample to form a mixed fluid sample. Sample buffers used in antigen testing typically contain a combination of ingredients that help stabilize the sample and enhance test performance. These ingredients vary depending on the specific test kit but typically include one or more detergents, salts, blocking proteins, preservatives, and buffers.

The sample buffer typically includes one or more detergents that help cells break open and release viral particles. Exemplary detergents include Triton X-100, Triton™ X-114, IGEPAL® CA-630, TERGITOL™ 15-S-9, and Deviron® C16. Preferably, the detergent included in the sample buffer is Triton X-100 at a concentration of about 0.1% to about 1.0%, preferably about 0.5%. The sample buffer also includes one or more salts to maintain the pH of the buffer and prevent sample degradation. Exemplary salts include sodium chloride, ammonium sulfate, potassium chloride, and calcium chloride. In a preferred form, the salt concentration included in the sample buffer is from about 0.1% to about 1.0%, preferably about 0.9%, of sodium chloride.

The sample buffer includes one or more blocking agents, such as those that help stabilize cells and Epstein-Barr virus particles and prevent them from adhering to the test components. Exemplary blocking agents include bovine serum albumin (BSA), polyvinylpyrrolidone (PVP), and purified proteins (e.g., casein). In a preferred embodiment, the blocking agent is included in the sample buffer at a concentration of about 0.1% to about 2.0%, preferably about 1.0%, BSA.

In some forms, the sample buffer includes one or more preservatives to prevent bacterial growth and maintain buffer stability. Exemplary preservatives include sodium azide and 0.01% thimerosal (merthiolate). In preferred forms, the preservative is included in the sample buffer at a concentration of about 0.02% to about 0.1%, preferably about 0.02%, of sodium azide.

In some forms, the sample buffer includes one or more additional buffers to maintain the pH of the solution and prevent interference with the test results. Exemplary buffers include sodium phosphate, potassium phosphate, and sodium citrate. In a preferred form, the buffer included in the sample buffer is sodium phosphate at a concentration of about 10 mM to about 100 mM, preferably about 50 mM. In a preferred embodiment, the sample buffer contains 0.5% Triton X-100, 0.9% sodium chloride, 1.0% BSA, 0.02% sodium azide, and 50 mM sodium phosphate. Bovine serum albumin (BSA): A protein that helps stabilize cells and Epstein-Barr virus particles and prevents them from adhering to the test components.

A. Sidestream device / analyze

In some forms, a lateral flow assay device is used to detect one or more of the expression biomarkers disclosed in Section II herein. This device allows for rapid and simultaneous detection of genes/expression markers that detect both NPC recurrence and the presence of NPCs. In some forms, the device is a nucleic acid lateral flow (NALF) device. In some forms, the device is a nucleic acid lateral flow immunoassay (NALFIA).

In a preferred embodiment, the disclosed NPC assay is a lateral flow assay, an immunoassay format in which a test sample flows along a solid matrix via capillary action. As shown in FIG12A , a lateral flow device 10 comprises a solid matrix 12 , such as a membrane strip, having an application site 14 , an optional binding region 16 , a capture region 18 , and an absorbent region 20 (e.g., a wick pad). A binding agent is optionally present in the binding region 16. A capture agent is immobilized in the capture region 18 , which preferably contains a plurality of capture lines 22 for detecting captured analyte (capture complex). The sample pad ensures controlled flow of the test solution, which migrates to the binding pad, which houses nanoparticles labeled with antibodies (or any binding partners for the analyte in the sample). For example, the binding agent in the capture zone can be an antibody or its biomarker-binding fragment or aptamer. If the target analyte is present, the labeled antibody binds to it and continues to migrate to the detection pad. The material is captured by the immobilized antibody at the test line (T line), forming a colored band. The subsequent control line (C line) provides a colorimetric indicator of sufficient solution migration. Finally, the absorption pad absorbs excess sample.

In some forms, the lateral flow device can use a multiplex detection format to detect more than one target analyte. The analysis can be performed on a strip containing multiple test lines equal to the number of target analytes to be analyzed. It is ideal to analyze multiple analytes simultaneously under the same conditions. The multiplex detection format is suitable for clinical diagnosis, where it is necessary to detect multiple interdependent analytes when determining the stage of a disease. For this purpose, the lateral flow strip can be constructed in various ways, namely by increasing the length and test lines of the learning strip, or by preparing other structures such as star or T-shape. The shape of the strip used in the lateral flow device can be selected based on the number of target analytes.

Figures 11D through 11I illustrate different configurations of an exemplary multi -layer lateral flow device. The exemplary next- generation rapid diagnostic test platform/hub includes a sample collection/distribution architecture that maximizes consistent biomarker detection while minimizing sample input for detection. The detection strip's cut-offs can be customized for personalized disease and prognosis detection. For example, inflammation and drug resistance (DR) can be detected simultaneously. The test can be performed at home, in a community clinic, or in a medical laboratory setting. Probes for the DNA, RNA, and protein biomarkers shown in Tables 1 and 2 and/or the expression biomarkers disclosed herein can display the likelihood of early recurrence from low risk to high risk using different colors, indicating the transition from pre-cancer to high-grade malignancy, and predicting the probability of recurrence within the next 3.5 years if only conventional treatment is used.

In some forms, the binding agent in the capture zone comprises an antibody that binds a biomarker selected from the group consisting of, for example, PSMA4, CALML3, SLC2A1, SNX3, LY6D, YBX1, and RPMS1, and the device can be used to detect NPCR. In some forms, the binding agent in the capture zone comprises an antibody that binds a biomarker selected from the group consisting of CKAP4, SYNGR2, CFL1, and RPMS1, and the device can be used to detect NPCD. In some forms, the device comprises multiple capture zones for simultaneous detection of more than one biomarker and/or simultaneous detection of NPCR and NPCD. The disclosed devices can be used to detect the presence of any of the biomarkers disclosed herein using appropriate binding partners.

i. solid matrix

The solid matrix 12 (e.g., a membrane strip) can be made of a material with sufficient porosity to allow antibodies and analytes to migrate along its surface and through its interior by capillary action. Examples of suitable membrane materials include, but are not limited to, cellulose, nitrocellulose, cellulose acetate, glass fiber, polyester, polyelectrolyte ion exchange membrane, acrylic copolymer/polyester, and polyethersulfone. In one embodiment, the membrane strip is made of nitrocellulose (e.g., nitrocellulose membrane with a Mylar backing) or glass fiber. In a preferred form, the membrane strip can be made of nitrocellulose. Nitrocellulose is a common binding matrix due to its high affinity for RNA, proteins, and nucleic acids and its compatibility with various detection methods, such as Western blotting, dot-blot assays, and other RNA, protein, or nucleic acid methods.

In the exemplary embodiment, the membrane strip is FUSION 5™ material (Whatman), which is a single-layer substrate material that performs all the functions of a lateral flow strip.

In some forms, the membrane strip is a nitrocellulose membrane or a polyvinylidene fluoride (PVDF) membrane, preferably a nitrocellulose membrane. Nitrocellulose membranes are known in the art and are commercially available from, for example, ThermoFisher Scientific (Catalog No. 77010) and BioRad (Catalog Nos. 1620115, 1620113, and 1620114). For nitrocellulose membranes, the optimal pore size is about 0.1 μm to about 0.45 μm.

ii. Application point

The solid matrix 12 includes an application site 14 , which may optionally include an application pad. For example, an application pad may be used if the sample containing the analyte contains particles or components that should be preferentially excluded from the immunoassay. Application pads can be used to modify biological samples, such as adjusting pH, filtering out solid components, separating whole blood components, and adsorbing unwanted antibodies. If an application pad is used, it is placed on the membrane adjacent to or covering the application site. The application pad can be made of an absorbent material, and when a fluid sample is applied to the pad, the application pad can deliver the fluid sample to the application site on the membrane. Representative materials include cellulose, cellulose nitrate, cellulose acetate, nylon, polyelectrolyte ion exchange membrane, acrylic copolymer/nylon, polyether sulfone, or fiberglass. In one embodiment, the pad is a Hemasep™-V pad (Pall Corporation). In another embodiment, the pad is a Pall™ 133, Pall™ A/D, or fiberglass pad.

iii. Junction region

The solid matrix 12 optionally contains a binding region 16 , which includes a binding pad containing a binding agent. In some embodiments, the binding region contains a binding agent that binds to the analyte to be measured and the control analyte. When the sample migrates through the binding region containing the binding agent, the analyte in the sample interacts with the binding agent to form a capture complex. In some forms, the binding pad can be made of cellulose fiber, glass fiber, or plastic (such as polyester, polypropylene, or polyethylene). Exemplary binding pads include Whatman ^™ binder release pads, SureWick® cellulose fiber pads, and SureWick® glass fiber pads.

iv. Absorption zone

In some forms, the lateral flow device contains an absorbent zone. The absorbent zone 20 preferably contains an absorbent pad. As shown in Figure 12A, the absorbent pad is placed at the far end of the lateral flow device when in use. If an absorbent pad is present, it can be made of absorbent materials similar to those described for the application pad. In a preferred embodiment, the absorbent pad allows the liquid to continue to flow through the capture zone by capillary action and promotes the movement of non-binding agents away from the capture zone. In some forms, the absorbent pad increases the total volume of sample entering the lateral flow device. This increased sample volume helps to wash unbound agents from the test line and the control line, thereby reducing background and enhancing analytical sensitivity.

v. Capture area

The capture zone 18 contains a capture agent fixed (e.g., coated on the membrane and/or permeated into the membrane) to the membrane strip. In a preferred embodiment, the capture agent is bound to the capture particles fixed in the capture zone 18 .

The capture zone 18 is preferably organized into one or more capture lines containing a capture agent. In a preferred embodiment, the capture zone contains a plurality of capture lines for multiplexing, i.e., detecting two or more analytes. In addition, the capture zone 18 may contain one or more control capture lines (i.e., control or calibration capture zones) for detecting the presence of a control analyte. The "lines" used to interact with the material in the liquid flow can take on a variety of shapes, orientations, and relationships. Most commonly, the "lines" are linear strips of material perpendicular to the liquid flow. Also most commonly, different "lines" with different compositions are separate and do not overlap. These features are most consistent with the mechanism and operation of a side flow device. However, the lines can be shaped other than strips, can be oriented non-perpendicular to the liquid flow, and can be overlapping. For example, some side-flow devices have a test line and a control line that are perpendicular to each other and overlap, forming a + sign when both lines exhibit a detectable signal. Preferably, the control analyte capture agent specifically binds to the control analyte but does not interact with the sample analyte being measured.

The calibration capture zone is preferably positioned so that the sample capture zone is between the application point and the calibration capture zone. In a preferred embodiment, the calibration capture zone is in close proximity to the sample capture zone so that the kinetics of capillary action of the analyte are similar (e.g., substantially identical) in both the calibration capture zone and the sample capture zone. For example, the two capture zones are close enough so that the velocity of the liquid flow over the two zones is similar. Despite the close proximity of the zones, the calibration capture zone and the sample capture zone are also sufficiently spaced apart so that particles trapped in each zone can be quantified individually (e.g., without crosstalk). Furthermore, in some versions, the sample capture zone is separated from the application point by a relatively large spatial distance, compared to the small distance between the sample capture zone and the calibration capture zone. Because particle capture is the rate-limiting step in the analysis, the distance between the application point and the capture zone (where the particles are captured) must be sufficient to slow the velocity of the liquid stream to a sufficiently slow rate to allow particles to be captured as the liquid stream moves over the sample capture zone. The optimal distance between components on the membrane strip can be determined and adjusted using routine experimentation.

In some embodiments, capture zone 18 contains at least one capture line 22 with a capture agent for detecting a dilute control analyte, i.e., an analyte typically present at a predictable concentration in a biological sample. Creatine is a particularly preferred dilute control analyte when the biological sample is urine. The typical human reference range for serum creatinine is 0.5 to 1.0 mg/dL (approximately 45 to 90 μmol/L) for women and 0.7 to 1.2 mg/dL (60 to 110 μmol/L) for men, which is used as a control analyte for nasal mucus.

In some embodiments, capture zone 18 contains one or more capture lines with capture agents used to detect reference analytes. Reference analytes can be administered to a biological sample at known concentrations. These reference values facilitate quantitative correlation between tag detection and analyte quantity.

vi. Capture particles

Capture particles are particles, such as polymer particles, that can be coated with a capture agent and fixed to the membrane in the capture zone 18. In a preferred embodiment, the particles are physically captured within the membrane. This allows for the selection of an optimal particle chemistry that is not affected by the need for chemical fixation. Suitable capture particles include liposomes, colloidal gold, organic polymer latex particles, inorganic fluorescent particles, and phosphorescent particles. In some embodiments, the particles are polystyrene latex beads, and most particularly, polystyrene latex beads prepared in the absence of a surfactant, such as surfactant-free Superactive Uniform aldehyde/sulfate latex (Interfacial Dynamics Corp., Portland, Oregon).

In a preferred embodiment, the particles are monodisperse polymer microspheres based on melamine resin (MF) (e.g., available from Sigma-Aldrich). Melamine resin microspheres are produced by acid-catalyzed hydrothermal polymerization of hydroxymethylmelamine in the absence of any surfactant at temperatures ranging from 70°C to 100°C. Unmodified MF particles have a hydrophilic, charged surface due to a high density of polar triazine-amine and triazine-imine groups. Surface functional groups (hydroxymethyl, amine, etc.) allow for the covalent attachment of other ligands. For specialized applications, MF particles can be modified by incorporating additional functional groups (e.g., carboxyl groups). This increases the potential for surface derivatization, such as chromophore or fluorophore labeling.

The particles can be labeled to facilitate detection in a manner that does not significantly affect the physical properties of the particles. For example, the particles can be internally labeled (i.e., the label is included within the particle, such as within a liposome or within a polystyrene latex bead). Representative labels include luminescent labels; chemiluminescent labels; phosphorescent labels; fluorescent labels; phosphorescent labels; enzyme-linked labels; chemical labels, such as electroactive agents (e.g., ferrocyanide); and colorimetric labels, such as dyes. In one embodiment, a fluorescent label is used. In another embodiment, phosphorescent particles, particularly upconversion phosphorescent particles, such as those described in U.S. Patent No. 5,043,265, are used.

The particles are preferably coated with capture agents, such as a sample analyte capture agent and a control analyte capture agent. They can be prepared by mixing the capture agents in a binding buffer. Covalent coupling to the particles is then performed, resulting in random binding of the capture agents to the particles.

b. Binding agents and capture agents

Binding agents for use in the disclosed assays include any molecule that selectively binds to an NPC analyte or calibration analyte. In preferred embodiments, the binding agent is an antibody, such as a monoclonal antibody, or an aptamer, such as a nucleic acid or peptide aptamer.

i. Antibodies

Antibodies that can be used in the compositions and methods include intact immunoglobulins (i.e., whole antibodies) of any class, fragments thereof, and synthetic RNAs and proteins containing at least the antigen-binding variable domain of an antibody. The variable domains differ in sequence between antibodies, which determines each antibody's binding ability and specificity for its specific antigen. However, this variability is typically not evenly distributed throughout the variable regions of an antibody. It is typically concentrated in three segments, called complement-determining regions (CDRs) or hypervariable regions, within the light and heavy chain variable domains. The more highly conserved portions of the variable domains are called framework regions (FRs). The variable domains of native heavy and light chains each comprise four FR regions connected by three CDRs. These FR regions generally adopt a β-sheet configuration. The CDRs form loops connecting, and in some cases, forming part of, the β-sheet structure. The CDRs in each chain are tightly bound together by the FR regions and, together with the CDRs from the other chains, contribute to the formation of the antibody's antigen-binding site. Thus, the disclosed antibodies contain at least the CDRs necessary to maintain DNA binding and/or interfere with DNA repair.

Biologically active antibody fragments can also be used. These fragments may or may not be attached to other sequences and may include insertions, deletions, substitutions, or other selective modifications of specific regions or amino acid residues, provided that the activity of the fragment is not significantly altered or diminished compared to the unmodified antibody or antibody fragment.

These techniques can also be adapted to prepare single-chain antibodies that are specific for the antigenic RNA and proteins disclosed herein. Methods for producing single-chain antibodies are well known to those skilled in the art. Single-chain antibodies can be constructed by fusing the variable domains of the heavy and light chains together using a short peptide linker, thereby reconstructing the antigen binding site on a single molecule. The following single-chain antibody variable fragment (ScFv) has been developed, in which the C-terminus of one variable domain is tethered to the N-terminus of another variable domain via a 15 to 25 amino acid peptide or linker without significantly interfering with antigen binding or the specificity of binding. The linker can be selected to allow the heavy and light chains to be bound together in the appropriate conformational orientation.

Bivalent single-chain variable fragments (bis-scFvs) can be engineered by linking two scFvs. This is achieved by creating a single peptide chain with two VH and two VL regions, resulting in a tandem scFv. ScFvs can also be designed with a short linker peptide (approximately five amino acids) that prevents the two variable regions from folding together, thus promoting scFv dimerization. This type of scFv is known as a bifunctional antibody. Studies have shown that the dissociation constant of bifunctional antibodies is 40-fold lower than that of the corresponding scFv, meaning that these bifunctional antibodies have a higher affinity for their target. Shorter linkers (one or two amino acids) lead to the formation of trimers (tribodies). Tetrafunctional antibodies have also been prepared that display even higher affinities for their targets than bifunctional antibodies.

Antibodies suitable for use in the disclosed NPC assays are known in the art and/or commercially available.

Anti-PSMA4 antibodies can be obtained as follows: RNA and Protein tech (polyclonal - catalog number 11943-2-AP; monoclonal - catalog number 68203-1-Ig); Kondo et al., J. Biol Chem ., 295(6):1658-1672 (2020); Benvenuto et al., Sci Rep ., 11(1):19051 (2021); Chadchankar et al., PLoS ONE , 14(11): e0225145 (2019); PSMA4 monoclonal antibody (RNA and protein tech); Abcam (catalog number ab191403); ThermoFisher Scientific (monoclonal - catalog number MA5-25812; polyclonal - catalog number PA5-76658); Novus Biologicals (catalog number NBP2-38754); and G Biosciences (ITA7053-100u).

Anti-CALML3 antibodies can be obtained as follows: Millipore Sigma (multiple strains - catalog number SAB1400036); ThermoFisher Scientific, catalog number PA5-30232; see Bunbanjerdsuk et al., Mod. Pathol ., 32(7):943-956 (2019); ThermoFisher Scientific (single strain - catalog number MA5-29079; multi strain - catalog number PA5-118992); Novus Biologicals (catalog number NBP2-15667); RNA and Protein Tech (catalog number 17275-1-AP) and Novus Biologicals (catalog number - NBP2-90114B).

Anti-SLC2A1 antibodies include (but are not limited to): Glut1 polyclonal antibody (Novus Biologicals, catalog number NB110-39113; see Pham et al., Cancers (Basel) , 14(5):1311 (2022); Balukoff et al., Nat Commun ., 11(1):5755 (2020)).

Anti-GLUT1 antibodies can be obtained as follows: GLUT1 polyclonal antibody (ThermoFisher Scientific, catalog number PA5-16793 and catalog number PA5-32428; Liu et al., Nat Cell Biol., 22(4):476-486 (2020)); GLUT1 recombinant rabbit monoclonal antibody (SA0377) (ThermoFisher Scientific, catalog number MA5-31960; Wang et al., Cell Reports, 28(5):1323-1334.e4 (2019) and Risha et al., Sci. Reports, 10(1):13572 (2020)); recombinant anti-glucose transporter GLUT1 antibody (Abcam catalog number ab115730; Sato T et al., Sci Rep 12:74 (2022); Wu F et al., Cell Death Discov 8:3 (2022); and Zhou X et al., Bioengineered 13:2471-2485 (2022)); and RNA and protein tech, catalog number 21829-1-AP, Li et al., Cancer Cell, 33(3):368-385.e7 (2018); Wang et al., Int J Oral Sci. 10(3):27 (2018)).

Anti-SNX3 antibodies can be obtained as follows: SNX3 polyclonal antibody (Cusabio Biotech, catalog number CSB-PA589999); rabbit polyclonal anti-SNX3 antibody (Abcam, catalog number ab56078; Cicek E et al., Oncogene 41:220-232 (2022); Yang et al., Cells 11(21):3358 (2022); and Cui Y et al., Traffic 22:123-136 (2021)); SNX3 polyclonal antibody (RNA and protein tech; catalog number 10772-1-AP; O'Farrell et al., Nat Cell Biol, 19(12):1412-1423 (2017); McGough et al., Nature Commun., 9(1):3737 (2018); and Lu et al., Cell Death Differentiation, 28(10):2871-2887 (2021)); SNX3 polyclonal antibody (ThermoFisher Scientific, catalog number PA5-101906); and SNX3 monoclonal antibody (G-7) (Santa Cruz, catalog number sc-376667; Xu et al., Neurodegenerative Diseases, 18(1):26-37 (2018)).

Anti-LY6D antibodies can be obtained as follows: anti-LY6D polyclonal antibody (Sigma-Aldrich, catalog number HPA024755); LY6D polyclonal antibody (ThermoFisher Scientific, catalog number PA5-64167; DePianto et al., JCI Insight, 6(8):e143626 (2021)); Ly-6D monoclonal antibody (49-H4), PE, eBioscience™ (ThermoFisher Scientific, catalog number 12-5974-80; Barros-Silva et al., Cell Reports, 25(12):3504-3518.e6 (2018)); and LY6D polyclonal antibody (RNA and protein tech, catalog number 17361-1-AP; Yao et al., Nat Commun, 11(1):5079 (2020); Steiner et al., Cell Reports, 42(4):112377 (2023); and Zhang et al., Diabetes, 66(6):1535-1547 (2017).

Anti-YBX1 antibodies can be obtained as follows: YBX1 polyclonal antibody (RNA and protein tech, catalog number 20339-1-AP, refer to An et al., Nature, 583(7815):303-309 (2020); and Zhang et al., Sci Adv, 8(5):eabj3967 (2022)); YBX1 recombinant rabbit monoclonal antibody (10H29L41) (ThermoFisher Scientific, catalog number 702245); YBX1 polyclonal antibody (ThermoFisher Scientific, catalog number PA5-83493, refer to Lin et al., Stem Cell Res Ther., 10(1):263 (2019)); and anti-YB1 rabbit polyclonal antibody (Abcam, catalog number ab12148, refer to Feng et al., JCI Insight 7(6):e150091 (2022) and Gao et al., Oncogenesis 11:13 (2022).

Anti-RPMS1 antibodies can be obtained as follows: RPMS polyclonal antibody (Zhang et al., J Virol., 75(6):2946-2956 (2001); and anti-RPMS1 peptide (SGQPRWWPWG) antibody; Smith et al., J Virol., 74(7):3082-3092, (2000)).

Anti-HMGN2P3 antibody can be obtained as follows: Novus Biologicals (Catalog No. NBP3-12793).

Anti-DNAJC11 antibodies can be obtained as follows: DNAJC11 polyclonal antibody (RNA and protein tech, catalog number 17331-1-AP, Violitzi et al., J Proteome Res., 18(11):3896-3912 (2019)); recombinant anti-DNAJC11 antibody [EPR15065(B)] - C-terminus (Abcam, catalog number ab183518); DNAJC11 polyclonal antibody (ThermoFisher Scientific, catalog number PA5-85470; catalog number PA5-100479; and catalog number PA5-55956).

Anti-EIF2AK1 antibodies can be obtained as follows: EIF2AK1 polyclonal antibodies (Novus Biologicals, catalog number NBP1-83210; catalog number NBP1-56484; catalog number NBP3-05000; and catalog number NBP3-04999) and EIF2AK1 monoclonal antibody (2H1F3) (RNA and protein tech, catalog number 20499-1-AP, see Fessler et al., Nature, 579(7799):433-437 (2020)).

Anti-FAM234A antibodies can be obtained as follows: FAM234A mouse monoclonal antibody [A5-A10] (HUABIO, catalog number M1010-1); and anti-FAM234A polyclonal antibody (Altas Antibodies, catalog number HPA071871).

Anti-PARPBP antibodies can be obtained from Novus Biologicals (Catalog No. NBP1-93969) and ThermoFisher Scientific (Catalog No. PA5-58877).

Anti-ARL5A antibodies can be obtained as follows: ARL5A polyclonal antibody (ThermoFisher Scientific, catalog number PA5-30509, catalog number PA5-67615; catalog number PA5-114063; and catalog number PA5-55479) and anti-ARL5A rabbit polyclonal antibody (Abcam, catalog number ab104008).

Anti-DHX57 antibodies include, but are not limited to, DHX57 rabbit polyclonal antibodies (Thermofisher Scientific, catalog number PA5-57548 and catalog number PA5-95809); and polyclonal anti-DHX57 antibodies (Atlas Antibodies, product number HPA036160).

Tables 2 and 3 provide other exemplary antibodies that can be used in the disclosed NPC assays.

Table 2: List of exemplary antibodies that can be used in the disclosed assays Gene Antibody name Catalog Number company NEDD8 NEDD8 polyclonal antibody bs-3812R-Biotin Bioss Inc. CALML3 CALML3 antibody NBP2-90114B Novus Biologicals NDUFA13 Rabbit anti-NDUFA13 038858-Biotin United States Biological BEX3 Rabbit anti-NGFRAP1 038955-Biotin United States Biological HNRNPA0 Immunotag™ ROA0 Polyclonal Antibody ITN0047-50u G Biosciences SLIRP SLIRP/C14orf156 polyclonal antibody bs-21033R Bioss Inc. ADH5 Immunotag™ ADH5 Antibody ITA6855-100u G Biosciences GNG5 GNG5 antibody orb24361 Biorbyt UBE2D2 UBE2D2 antibody orb415016 Biorbyt PSMA4 Immunotag™ PSMA4 Antibody ITA7053-100u G Biosciences SLC2A1 Slc2A1 recombinant antibody CAC12206 Biomatik SNX3 Immunotag™ SNX3 Antibody ITA5415-100u G Biosciences LY6D LY6D antibody orb29008 Biorbyt PSMA3 PSMA3 antibodies orb50969 Biorbyt PSMB1 Proteasome subunit β type 1 antibody orb239675 Biorbyt YBX1 Mouse anti-YBX1 antibody 135476-Biotin United States Biological PSMB5 PSMB5 antibody orb46795 Biorbyt PSMA6 Immunotag™ PSMA6 Antibody ITA6988-100u G Biosciences CKAP4 CKAP4 antibody orb414676 Biorbyt SYNGR2 Mouse anti-SYNGR2 antibody 134136-Biotin United States Biological MARCKSL1 Mouse anti-MARCKSL1 antibody 129435-Biotin United States Biological CFL1 CFL1 antibody orb44566 Biorbyt PDCD5 PDCD5 antibodies NBP1-76988B Novus Biologicals IL32 Mouse anti-human interleukin-32: biotin antibody MBS226187 MyBioSource.com RAN Ran polyclonal antibodies bs-4282R-biotin Bioss Inc. NME1 NME1 polyclonal antibody bs-1066R-Biotin Bioss Inc. VCAM1 VCAM1 rabbit polyclonal antibody AP22317BT-N OriGene Technologies GAPDH Goat anti-GAPDH antibody MBS423610 MyBioSource.com TUBB Tubulin β polyclonal antibody bs-4511R-Biotin Bioss Inc. HSPD1 HSPD1 antibodies orb687431 Biorbyt LGALS1 LGALS1 antibody orb352068 Biorbyt TNFAIP3 TNFAIP3 antibodies orb47522 Biorbyt ITGAV ITGAV antibody orb414808 Biorbyt RPLP0 RPLP0 antibody orb352268 Biorbyt STMN1 STMN1 Antibody orb46146 Biorbyt PPIA Ppia recombinant antibody CAC12118 Biomatik CCL20 Biotin anti-human CCL20 534604 BioLegend FSCN1 FSCN1 antibody orb401039 Biorbyt LGALS9 Galectin-9 antibody orb240263 Biorbyt RPS19 Rabbit anti-RPS19, CT antibody 041238-Biotin United States Biological TPI1 TPI1 antibody orb46998 Biorbyt ICAM1 ICAM1 antibody orb46974 Biorbyt ENO1 α-enolase antibodies orb239777 Biorbyt Q1QB Mouse anti-human C1QBP monoclonal antibody CTMM-0322-JL145 Creative Biolabs CXCL3 CXCL3 antibodies orb27053 Biorbyt MIF Goat anti-MIF, biotinylated antibody MBS423623 MyBioSource.com TAGLN2 Rabbit anti-TAGLN2, CT antibody 042589-Biotin United States Biological CXCL10 Mouse anti-human CXCL10: Biotin AB MBS226131 MyBioSource.com UBD UBD antibody orb350791 Biorbyt CSTA CSTA antibody orb350388 Biorbyt NFKBIA NFKBIA antibodies orb688111 Biorbyt SOCS1 Anti-SOCS1 antibodies ABIN5539590 antibodies-online MYBPC1 MYBPC1 antibody orb415924 Biorbyt NTRK2 Mouse anti-NTRK2 antibody 130601-Biotin United States Biological FKBP1A Mouse anti-FKBP1A antibody 126829-Biotin United States Biological SOX4 SOX4 antibody orb51073 Biorbyt TUBA1C Rabbit anti-TUBA1C, CT antibody 043449-Biotin United States Biological PYCARD Anti-PYCARD antibody ABIN5539792 antibodies-online TCIM Anti-C8orf4 TCIM antibody A31995 BosterBio SLPI Mouse anti-SLPI antibody 133526-Biotin United States Biological WFDC2 Mouse anti-WFDC2 antibody 135376-Biotin United States Biological AQP3 Aquaporin 3 antibody biotin conjugated MBS540993 MyBioSource.com LCN2 NGAL (LCN2) biotinylated mouse monoclonal detection antibody TA700555 OriGene Technologies BPIFB1 BPIFB1 antibody NBP2-90203B Novus Biologicals SERPINB3 Serpinb3 polyclonal antibody CAC07323 Biomatik CLU CLU antibody orb238210 Biorbyt SCGB1A1 SCGB1A1 antibody orb46074 Biorbyt CRIP1 Rabbit anti-CRIP1, CT antibody 034224-Biotin United States Biological KRT14 Keratin, type I cytoskeleton 14 antibody orb242952 Biorbyt LYPD2 LYPD2 antibodies MBS969993 MyBioSource.com TFF3 Anti-Trifolium factor 3 (TFF3) antibodies 130-10147B-50 RayBiotech TSPAN1 TSPAN1 antibody orb357352 Biorbyt SCGB3A1 Mouse anti-SCGB3A1 antibody 133004-Biotin United States Biological PIGR Rabbit anti-PIGR, CT antibody P4165-50-Biotin United States Biological MUC16 MUC16/CA125 monoclonal antibody LS-C86749 LSBio C19orf33 C19ORF33 antibody orb1520525 Biorbyt PRSS23 Serine protease 23 antibody orb242376 Biorbyt MSMB MSMB antibody orb400511 Biorbyt CAPS Caps polyclonal antibodies CAC11552 Biomatik CXCL17 Immunotag™ CXCL17 Antibody ITA2451-100u G Biosciences ANXA1 Annexin A1 antibody orb241488 Biorbyt AGR2 AGR2 antibody orb686795 Biorbyt GSTA1 GSTA1 antibody orb24485 Biorbyt BPIFA1 Anti-Plunc/BPIFA1 antibody A03162-2 BosterBio MT1X Metallothionein-1X Antibody orb241394 Biorbyt ALDH3A1 ALDH3A1 Antibody MBS1499519 MyBioSource.com C20orf85 C20orf85 antibody PA5-64228 ThermoFisher Scientific LGALS3 Galectin-3 antibody orb238943 Biorbyt MUC4 Mucin 4 biotin-linked antibody MBS2005469 MyBioSource.com TACSTD2 TROP2 / TACSTD2 Antibodies 10428-MM01-B Sino Biological, Inc. KRT16 KRT16 antibody orb517911 Biorbyt CEBPD CEBP δ antibody NBP2-41192B Novus Biologicals CDKN1A Mouse anti-CDKN1A antibody 124806-Biotin United States Biological PGM2 Mouse anti-PGM2 antibody 131218-Biotin United States Biological MEG3 MEG3 antibody ABIN7138336 Antibodies-online CTNNBIP1 Mouse anti-CTNNBIP1 antibody 125456-Biotin United States Biological IGF2BP3 IGF2BP3 antibody orb46811 Biorbyt FAF1 FAF1 antibody orb47258 Biorbyt DUSP11 DUSP11 antibody orb686647 Biorbyt CLDND1 CLDND1 antibody orb687667 Biorbyt BAG4 BAG4 antibody orb47874 Biorbyt SERPINB12 SERPINB12 antibody orb34842 Biorbyt AP3M2 AP3M2 antibody orb21258 Biorbyt SIPA1L2 SIPA1L2 antibody orb356716 Biorbyt CLCA4 CLCA4 antibody MBS8561978 MyBioSource.com SYT8 Rabbit anti-SYT8, ID antibody 042538-Biotin United States Biological FGFR3 FGFR3 antibody biotin conjugation MBS541609 MyBioSource.com SUSD4 SUSD4 antibody orb357008 Biorbyt TNNT3 Anti-TNNT3 antibodies ABIN5539524 antibodies-online NSG1 Anti-NSG1 antibody A12107-1 BosterBio GPX2 GPX2 antibody orb52630 Biorbyt LSP1 LSP1 antibody NBP3-00254B Novus Biologicals RPMS1 Zhang et al., J. Virology, 75(6):2946-2956 BALF4 InVivoMAb anti-EBV/HHV4 gB/BALF4 antibody VVV22002 Antibody System BALF5 BALF5 antibody orb351876 Biorbyt BALF0 Zhao et al. , Viruses 11(12): 1099); Shao et al. , Protein Expr. Purif . 2019;162:44-50. LMP-1 Anti-EBV/HHV-4 LMP1/BNLF1 Antibodies RVV07003 Antibody System LMP-2B EBV LMP2 polyclonal antibody BS-4700R ThermoFisher Scientific

Table 3: Additional List of Exemplary Antibodies That Can Be Used in the Disclosed Assays Gene Antibody name Catalog Number company ABCB6 Mouse anti-ABCB6 antibody 122795-Biotin United States Biological ADAMTS7 ADAMTS7 antibody orb353824 Biorbyt AKAP12 Mouse anti-AKAP12 antibody 123132-Biotin United States Biological AMDHD1 AMDHD1 antibody CSB-PA850402LD01HU CUSABIO Technology LLC ANK3 Ank3 polyclonal antibody CAC11390 Biomatik ANKRD30A Immunotag™ ANKRD30A Polyclonal Antibody ITT0232-50u G Biosciences ANTXR2 ANTXR2 polyclonal antibody MBS2541669 MyBioSource.com AP1B1 AP1B1 antibody MBS3220159 MyBioSource.com APP APP recombinant antibody CAC11986 Biomatik ARFGAP1 ARFGAP1 polyclonal antibody CAF50460 Biomatik ATOX1 ATOX1 antibodies orb517155 Biorbyt ATP6V0A1 Atp6V0A1 polyclonal antibody CAC09593 Biomatik B4GALT5 B4Galt5 polyclonal antibody CAC09235 Biomatik BDH2 BDH2 antibody orb354056 Biorbyt BPNT1 Mouse anti-BPNT1 antibody 243865-Biotin United States Biological BRD4 Anti-BRD4 Antibody Picoband™ A00123-2 BosterBio CBX1 Mouse anti-CBX1 antibody 124402-Biotin United States Biological CD69 Anti-CD69 Antibody Picoband™ A00529-2 BosterBio CDH1 CDH1 monoclonal antibody CAC10270 Biomatik CDK13 Anti-CDK13 Antibody Picoband™ A05292-1 BosterBio CHST7 anti-CHST7 antibody GTX120778 GeneTex CLEC14A CLEC14A antibody orb517775 Biorbyt CLEC3B CLEC3B/tetranectin antibodies NBP3-00044B Novus Biologicals CNBP CNBP antibody orb350368 Biorbyt CNDP1 Rabbit anti-CNDP1, ID antibody 034035-Biotin United States Biological COL3A1 COL3A1 antibody CSB-PA11459D0Rb CUSABIO Technology LLC CROCC Immunotag™ CROCC Polyclonal Antibodies ITN1244-50u G Biosciences CTSA CTSA antibodies orb23460 Biorbyt CXCL6 CXCL6 antibody orb354512 Biorbyt CYB561D2 Immunotag™ CYB561D2 polyclonal antibody ITT1164-50u G Biosciences DAG1 DAG1 antibody orb686839 Biorbyt DHTKD1 Rabbit anti-DHTKD1, ID antibody 034629-Biotin United States Biological DLST Anti-DLST Antibody Picoband™ A05097-1 BosterBio DPP3 Rabbit anti-DPP3, CT antibody 034781-Biotin United States Biological DUSP19 DUSP19 antibody MBS7002143 MyBioSource.com EPB41 EPB41 polyclonal antibody CAF50672 Biomatik EPHA1 EPHA1 antibody orb45833 Biorbyt ERAP2 Anti-ERAP2 Antibody Picoband™ A04269-1 BosterBio ESD Esd polyclonal antibodies CAC11845 Biomatik EXOC3L4 EXOC3L4 antibody PA5-60455 Thermo Fisher Scientific FBL Anti-Fibrin/FBL Antibody Picoband™ A03178-1 BosterBio GALE Anti-GALE Antibody Picoband™ A00551-1 BosterBio GAN Mouse anti-GAN antibody 127153-Biotin United States Biological GAS2L1 Gas2L1 polyclonal antibody CAC10792 Biomatik GASK1A GASK1A rabbit polyclonal antibody TA372310 OriGene Technologies GNPAT GNPAT antibody orb414764 Biorbyt HBM Immunotag™ HBM Polyclonal Antibodies ITN2128-50u G Biosciences HDAC1 HDAC1 Antibody orb355160 Biorbyt HDGFL2 Anti-HDGFL2 Antibody Picoband™ A32401-2 BosterBio HEMGN Rabbit anti-HEMGN, CT antibody 036505-Biotin United States Biological HLA-DRB3 HLA-DRB3 antibodies orb400275 Biorbyt HMGB2 HMGB2 antibodies orb52041 Biorbyt HNRNPA1 HNRNPA1 antibody CSB-PA010600HD01HU CUSABIO Technology LLC HNRNPL HNRNPL antibody orb355208 Biorbyt IFI16 Anti-IFI16 Antibody Picoband™ A00848-3 BosterBio JAG1 Anti-Jagged1/JAG1 Antibody Picoband™ A00640-2 BosterBio LAMTOR5 LAMTOR5 antibody MBS9416937 MyBioSource.com LCMT1 LCMT1 antibodies orb752613 Biorbyt LRP5 Mouse anti-LRP5 antibody 129208-Biotin United States Biological LRRC75A Anti-LRRC75A Antibody Picoband™ A18461-1 BosterBio LSM6 LSM6 antibody orb355492 Biorbyt LUC7L2 Immunotag™ LUC7L2 Antibody ITA2149-100u G Biosciences MAGT1 Rabbit anti-MAGT1, NT antibody 038074-Biotin United States Biological MANEAL MANEAL Antibody MBS3209665 MyBioSource.com MAP2K2 MAP2K2 antibodies orb46962 Biorbyt MARCHF2 Mouse anti-MARCH2 antibody 129428-Biotin United States Biological MED25 MED25 antibody MBS7043801 MyBioSource.com MFGE8 MFGE8 antibody CSB-PA07974D0Rb CUSABIO Technology LLC MINPP1 Minpp1 polyclonal antibody CAC09946 Biomatik MOSPD2 Anti-MOSPD2 Antibody Picoband™ A16695-1 BosterBio MROH1 MROH1 antibody MBS3219094 MyBioSource.com MRPL17 Immunotag™ MRPL17 Antibody ITA8843-100u G Biosciences MTHFS MTHFS antibody MBS1493889 MyBioSource.com NCAN Immunotag™ NCAN Antibody ITA5114-100u G Biosciences NDUFA12 Rabbit anti-NDUFA12, CT antibody 038857-Biotin United States Biological NFE2 Anti-NFE2 Antibody Picoband™ A03765-1 BosterBio NID2 Mouse anti-NID2 antibody 130371-Biotin United States Biological NPEPL1 Mouse anti-NPEPL1 antibody 130495-Biotin United States Biological OGA Mouse anti-MGEA5 antibody 129653-Biotin United States Biological PBX2 Mouse anti-PBX2 antibody 130921-Biotin United States Biological PCSK1 Mouse anti-PCSK1 antibody 249885-Biotin United States Biological PFDN4 PFDN4 antibody CSB-PA017814LD01HU CUSABIO Technology LLC PKM PKM antibody MBS7006426 MyBioSource.com PPP6R1 PPP6R1 antibody MBS9215757 MyBioSource.com PRF1 PRF1 antibody MBS7004334 MyBioSource.com PRMT9 PRMT9 antibodies MBS7045457 MyBioSource.com RALYL RALYL antibody orb356316 Biorbyt RANBP3L RANBP3L antibody orb517839 Biorbyt RAVER1 Anti-RAVER1 Antibody Picoband™ A09359-2 BosterBio RDX Immunotag™ RDX Antibody ITA5254-100u G Biosciences REEP5 Mouse anti-REEP5 antibody 132462-Biotin United States Biological RIPOR1 RIPOR1 antibody MBS3218431 MyBioSource.com SAMHD1 Anti-SAMHD1 Antibody Picoband™ A00592-4 BosterBio SART3 SART3 antibody orb356580 Biorbyt SEH1L SEH1L polyclonal antibody bs-8721R-Biotin Bioss Inc SIGIRR Sigirr polyclonal antibodies CAC10868 Biomatik SMIM15 smim15 antibody orb861403 Biorbyt SNRPF SNRPF polyclonal antibody MBS9431508 MyBioSource.com SRP14 Anti-SRP14 antibody GTX101867 GeneTex TARDBP TARDBP antibody orb515603 Biorbyt TBC1D20 TBC1D20 antibody MBS969043 MyBioSource.com THYN1 THYN1 polyclonal antibody bs-7104R-Biotin Bioss Inc. TM4SF1 TM4SF1 antibody orb357132 Biorbyt TMEM132D TMEM132D antibody orb515821 Biorbyt TNFSF12 TNFSF12 antibody MBS1492223 MyBioSource.com TRAPPC11 TRAPPC11 antibody orb51793 Biorbyt TRAPPC2 Mouse anti-TRAPPC2 antibody 134692-Biotin United States Biological TRAPPC2B Anti-ZNF547 antibody orb1880124 Biorbyt TRIM28 Trim28 polyclonal antibody CAC07189 Biomatik USP47 USP47 antibody orb357500 Biorbyt VTI1A VTI1A antibody orb51897 Biorbyt WDR41 WDR41 antibody orb687647 Biorbyt XYLT2 XYLT2 antibody MBS8505767 MyBioSource.com ZNF326 Rabbit anti-ZNF326, NT antibody 044194-Biotin United States Biological ZNF428 ZNF428 polyclonal antibodies PA5-71584 Thermo Fisher Scientific ZNF846 ZNF846 antibody NBP3-20736B Novus Biologicals ZNHIT1 Immunotag™ ZNHIT1 Antibody ITA2300-100u G Biosciences GLS GLS Antibody MBS7003190 MyBioSource.com DENND5B Rabbit anti-DENND5B, CT antibody 034585-Biotin United States Biological COX8A Immunotag™ COX82 Antibody ITA2380-100u G Biosciences AUP1 Anti-AUP1 Antibody Picoband™ A08937-1 BosterBio IGFBP1 IGFBP1 monoclonal antibody CAC10267 Biomatik GCG GCG antibody MBS7136090 MyBioSource.com PRAP1 Anti-PRAP1 antibodies MBS8307774 MyBioSource.com SYNPO2 SYNPO2 antibody NBP2-82037B Novus Biologicals FNIP2 FNIP2 antibody NBP2-81941B Novus Biologicals PGAM5 PGAM5 antibody orb52329 Biorbyt DAD1 DAD1 antibody CSB-PA03124D0Rb CUSABIO Technology LLC FCN1 Mouse anti-FCN1 antibody 126725-Biotin United States Biological FGL1 Fgl1 polyclonal antibody CAC11231 Biomatik SRGN SRGN antibody orb517305 Biorbyt PGA3 PGA3 antibody CSB-PA11289D0Rb CUSABIO Technology LLC FMOD FMOD antibody orb52562 Biorbyt HIVEP1 HIVEP1 antibody MBS3219261 MyBioSource.com DEFA3 Mouse anti-DEFA3 antibody 245281-Biotin United States Biological HLA-DPB1 Hla-Drb3 polyclonal antibody CAC11113 Biomatik DUSP7 DUSP7 polyclonal antibody bs-7928R-Biotin Bioss Inc. STOML3 STOML3 antibody orb53702 Biorbyt SLC17A5 Anti-SLC17A5 Antibody Picoband™ A06657-2 BosterBio SLC2A2 SLC2A2 Antibody orb46262 Biorbyt PROZ Immunotag™ Protein Z monoclonal antibody ITM0538-50u G Biosciences CALU Mouse anti-CALU antibody 244176-Biotin United States Biological C1S C1S antibody orb414640 Biorbyt SOD3 SOD3 antibody orb356900 Biorbyt CRP Crp recombinant antibody CAC12107 Biomatik WWC3 WWC3 antibody orb687019 Biorbyt DPY19L1 DPY19L1 antibody orb418046 Biorbyt DHCR7 DHCR7 antibody orb516422 Biorbyt CGNL1 Rabbit anti-CGNL1, NT antibody 033821-Biotin United States Biological SLC25A1 Anti-Slc25a1 Picoband™ Antibody A05995-1 BosterBio OMD OMD antibodies orb516500 Biorbyt DPT Mouse anti-DPT antibody 126005-Biotin United States Biological NTS NTS Antibody orb351808 Biorbyt TRIM41 Rabbit anti-TRIM41, CT antibody 043239-Biotin United States Biological CCL17 Biotin anti-human CCL17 (TARC) 523003 BioLegend SLIT1 Rabbit anti-SLIT1, NT antibody 042009-Biotin United States Biological SH3BGR SH3BGR antibody orb356692 Biorbyt ANTXR1 TEM8/ANTXR1 Antibody NBP3-21501B Novus Biologicals EPB42 Mouse anti-EPB42 antibody + B282:B289 126370-Biotin United States Biological SLC4A1 Mouse anti-SLC4A1 antibody 133495-Biotin United States Biological OXA1L Rabbit anti-OXA1L, CT antibody 039550-Biotin United States Biological TTN Ttn polyclonal antibody CAC07287 Biomatik DST Dst polyclonal antibodies CAC08167 Biomatik TMEM132A Anti-TMEM132A Antibody Picoband™ A12054-2 BosterBio CKM Ckm polyclonal antibody CAC07384 Biomatik KMT2B KMT2B Antibody orb686407 Biorbyt HLA-A Immunotag™ HLA-A Antibody ITA6967-100u G Biosciences CD2BP2 Rabbit anti-CD2BP2, NT antibody 033489-Biotin United States Biological SET SET polyclonal antibodies CAF50321 Biomatik SELENOH SELENOH Antibody MBS9630600 MyBioSource.com CCL19 Ccl19 recombinant antibody CAC12049 Biomatik RHAG Immunotag™ CD241 Polyclonal Antibody ITT5284-50u G Biosciences EZR EZR antibody CSB-PA02159D0Rb CUSABIO Technology LLC SLC29A1 SLC29A1 antibody orb46518 Biorbyt FRZB Anti-FRZB Antibody Picoband™ PB9372 BosterBio SEPTIN11 SEPT11 antibody MBS7134993 MyBioSource.com GALC Mouse anti-GALC antibody 127114-Biotin United States Biological SNRPB2 SNRPB2 antibody orb351044 Biorbyt SLFN5 SLFN5 antibody MBS9430160 MyBioSource.com SLC12A4 SLC12A4 antibody orb39051 Biorbyt DSCAML1 Mouse anti-DSCAML1 antibody 245499-Biotin United States Biological CYP2C9 Immunotag™ CP2C9 Polyclonal Antibody ITN1835-50u G Biosciences HSD11B1 Anti-HSD11B1 Antibody Picoband™ A01565 BosterBio COL2A1 COL2A1 Antibody MBS7136224 MyBioSource.com STAU2 Mouse anti-STAU2 antibody 252178-Biotin United States Biological LTF Biotin-linked antibody to lactoferrin MBS2003498 MyBioSource.com F11 F11 polyclonal antibody CAC11850 Biomatik C1QTNF3 C1Qtnf3 polyclonal antibody CAC08270 Biomatik pip4k2a PIP4K2A polyclonal antibody CAF50266 Biomatik JMY JMY antibody orb416048 Biorbyt IGHV3-21 https://www.sciencedirect.com/science/article/pii/S0006497119869884 DNAH6 DNAH6 antibody MBS3212343 MyBioSource.com COCH Rabbit anti-COCH, CT antibody 034070-Biotin United States Biological NSL1 NSL1 antibody orb518019 Biorbyt SPTB Anti-β1 Spectrin/SPTB Antibody Picoband™ A02392 BosterBio KRT9 Rabbit anti-KRT9, ID antibody 037637-Biotin United States Biological IGKV2-29 Recombinant anti-human IGKV2-29 antibody MOB-2718z Creative Biolabs IGKV3D-20 Recombinant anti-human IGKV3D-20 antibody MOB-1384z Creative Biolabs KIFC3 Anti-KIFC3 antibody GTX114507 GeneTex PGA4 Rabbit anti-PGA4, CT antibody 039908-Biotin United States Biological PGA5 Mouse anti-PGA5 antibody 131197-Biotin United States Biological

Antibodies that specifically bind to the analyte can also be prepared using conventional methods. For example, antibodies can be purified from animals immunized with the analyte. Monoclonal antibodies can be produced by fusing myeloma cells with spleen cells from mice immunized with the opioid analyte or with lymphocytes immunized in vitro. Antibodies can also be produced using recombinant technology.

The capture agent of the disclosed compositions and methods can be an antibody, such as an anti-meta-type antibody. An anti-meta-type antibody is an immunological reagent that is specific for the conformation of the active site of a liganded antibody and does not interact with bound ligand or unliganded antibody. Antibodies that selectively bind to the captured complex but not to free analyte can be obtained using standard methods known in the art. For example, a phage display library of native scFv antibody fragments can be used to select antibodies that bind to immune complexes with the analyte and Fab fragments of antibodies that specifically bind to the analyte. First, phage are pre-cultured to select those phage that bind to the Fab fragments as is. Unbound phage are isolated and incubated with a mixture of analyte and immobilized Fab to select for phage that bind to the immune complex formed between the immobilized Fab and analyte. Unbound phage are washed away, and phage bound to the complex are eluted. Background is monitored by examining binding to the Fab in the absence of analyte. After several rounds of screening, a number of clones are selected for sequencing and expression, yielding scFv fr.

ii. Aptamers

Aptamers are typically oligonucleotides ranging in length from 15 to 50 bases, which fold into defined secondary and tertiary structures, such as stem-rings or G-quadruplexes. Oligonucleotides can be DNA or RNA and can be modified to improve stability. Aptamers generally have higher specificity and affinity for target molecules than antibodies. Aptamers preferably bind to target molecules with a Kd of less than ^10-6 , ^10-8 , ^10-10 or ^10-12 . Aptamers can also bind to target molecules with extremely high specificity. Preferably, the Kd of an aptamer with a target molecule is at least 1/10, 1/100, 1/1000, 1/10,000 or 1/100,000 of the Kd with other molecules. In addition, the number of target amino acid residues required for aptamer binding may be smaller than that of the antibody.

Aptamers are typically isolated from a complex library of synthetic oligonucleotides through an iterative process of adsorption, recovery, and re-amplification. For example, aptamers can be prepared using the SELEX (Systematic Evolution of Ligands by Exponential Enrichment) method. The SELEX method involves selecting RNA molecules that bind to a target molecule from an RNA pool, the RNA pool consisting of RNA molecules each having a random sequence region and a primer binding region at both ends; amplifying the recovered RNA molecules by RT-PCR; using the obtained cDNA molecules as a template for transcription; and using the resultant as an RNA pool for subsequent procedures. This type of procedure is repeated several to dozens of times to select RNA with a stronger ability to bind to the target molecule. The base sequence length of the random sequence region and the primer binding region is not particularly limited. Typically, the random sequence region contains approximately 20 to 80 bases, and the primer binding region contains approximately 15 to 40 bases. Specificity for the target molecule can be enhanced by preemptively mixing molecules similar to the target molecule with the RNA pool and using a pool containing RNA molecules that do not bind to the molecule of interest. RNA molecules obtained as the final product of this technique are used as RNA aptamers. Representative examples of the preparation and use of aptamers to bind to a variety of target molecules can be found in U.S. Patent Nos. 5,476,766, 5,503,978, 5,631,146, 5,731,424, 5,780,228, 5,792,613, 5,795,721, 5,846,713, 5,858,660, 5,861 ...2,613, 5,795,721, 5,846,713, 5,858,660, 5,861,613, 5,795,721, 5,846,713, 5,858,660, 5,792,613, 5,795,721, 5,846 U.S. Patent No. 5,861,254, U.S. Patent No. 5,864,026, U.S. Patent No. 5,869,641, U.S. Patent No. 5,958,691, U.S. Patent No. 6,001,988, U.S. Patent No. 6,011,020, U.S. Patent No. 6,013,443, U.S. Patent No. 6,020,130, U.S. Patent No. 6,028,186, U.S. Patent No. 6,030,776, and U.S. Patent No. 6,051,698. An aptamer database containing comprehensive sequence information for aptamers and non-natural ribozymes generated by in vitro selection methods is available at aptamer.icmb.utexas.edu.

In some embodiments, aptamers may contain one or more modified nucleic acids (also known as xenonucleic acids or XNAs) for the addition of chemical functionality, which can increase the binding affinity of the aptamer to the immune complex. Non-limiting examples of modified nucleic acids include, but are not limited to, unnatural base pairs (UBPs), base modifications (e.g., C7-modified deaza-adenine, C7-modified deaza-guanosine, C7-modified deaza-cytosine, C7-modified deaza-uridine), and sugar modifications, such as ribulose nucleic acid (ribulonucleic acid), α-L-thiosyl nucleic acid (TNA), 3'-2'-phosphinomethyl-thiosyl nucleic acid (tPhoNA), and 2'-deoxyxylose nucleic acid (dXNA). In some forms, modified nucleic acids can be introduced into aptamers by in vitro evolution using alternatives to the phosphodiester backbone, such as, for example, phosphorothioates, phosphoroborates, phosphonates, alkylphosphonate nucleic acids, and peptide nucleic acids. In some forms, modified nucleic acids can be introduced into aptamers via a mutant T7 RNA polymerase that tolerates substitutions at the 2' position of the furanose ring. Substitutions that can be attached to C2' include, but are not limited to, fluorine, amine, or methoxy. In some forms, modified nucleic acids can be introduced into aptamers via R group modification at the 5th position of uracil. In these forms, the R group can be one of many different side chains known to those skilled in the art, ranging from hydrophobic to hydrophilic. The incorporation of synthetic nucleotides into nucleic acid aptamers using phosphodiester substitution and modified base groups is known to those skilled in the art (see, e.g., Mayer G. Angew Chem Int Ed Engl. (2009) 48: 2672-2689; Keefe, A.D. and Cload, S.T. Curr Opin Chem Biol. (2008); 12: 448-456; Appella, D.H. Curr. Opin. Chem. Biol. (2009) 13(5-6): 687-696).

iii. Peptide aptamers

Peptide aptamers are small peptides with a randomized amino acid sequence that are selected for their ability to bind to a target molecule. Various systems can be used to screen for peptide aptamers, but the yeast double hybrid system is currently the most commonly used. Peptide aptamers can also be selected from combinatorial peptide libraries, which are constructed by phage display and other surface display technologies, such as mRNA display, ribosome display, bacterial display, and yeast display. This experimental procedure is also called biopanning. In the peptides obtained from biopanning, mimicking the antigenic determinant can be considered a peptide aptamer. All peptides panned from combinatorial peptide libraries have been stored in a special database called MimoDB.

Analytical fluid

An aqueous analytical fluid can also be introduced into the biological sample to form a mixed fluid sample. The analytical fluid supports the reaction between the analyte and the labeled binding agent (e.g., does not interfere with binding) and has a sufficiently low viscosity to allow the analytical fluid to move by capillary action. In some embodiments, the analytical fluid contains one or more of the following components: a buffer (e.g., phosphate); a salt (e.g., NaCl); a protein stabilizer (e.g., bovine serum albumin "BSA", casein, serum); and a detergent, such as a non-ionic detergent or surfactant (e.g., NINATE® 411, ZONYL® FSN 100, AEROSOL OT 100%, GEROPON® T-77, BIO-TERGE® AS-40, STANDAPOL® ES-1, TETRONIC® 1307, SURFNYOL® 465, SURFYNOL® 485, SURFYNOL® 104PG-50, IGEPAL® CA210, TRITON™ X-45, TRITON™ X-100, TRITON™ X305, SILWET® L7600, RHODASURF® ON-870, CREMOPHOR® EL, TWEEN® 20, TWEEN® 80, BRIJ 35, CHEMAL LA-9, PLURONIC® L64, SURFACTANT 10G, SPAN™ 60). The analytical fluid may contain a thickening agent, if necessary. Representative analytical fluids include saline or 50 mM Tris-HCl, pH 7.2. In some embodiments, the analytical fluid is water.

Preparation of side flow device

A method for preparing a lateral flow device for detecting one or more analytes from a nasal swab sample for NPC diagnosis and recurrence prediction is disclosed. In a preferred embodiment, the lateral flow device is a multiplex test strip for detecting NPC analytes, such as peptides and nucleic acid antigens.

Exemplary Test Strip Preparation Methods

Materials used to prepare the disclosed test strips include one or more of the following: a membrane strip (e.g., a nitrocellulose membrane), a binding pad, a sample pad, an absorbent, an absorbent pad, a backing card, aptamers or antibodies for one or more target antigens, capture particles (e.g., colloidal gold particles), a blocking agent, a buffer solution, a dispenser, and a laminator.

Procedure for preparing strips

An exemplary process for preparing the disclosed lateral flow devices (such as test strips) includes one or more of the following steps:

1. Prepare the nitrocellulose membrane, binding pad, sample pad, and absorption pad according to the required size of the lateral flow strip (approximately 5 mm (width) × 40 mm (length)).

2. Coat the nitrocellulose membrane with one or more capture aptamers or antibodies against one or more desired target antigens. Antibodies used for NPC analysis are generally diluted to an appropriate level, generally determined by the concentration and affinity of the antibody. For example, antibodies can be used at a dilution of 1:50, 1:100, 1:200, 1:300, 1:400, 1:500, 1:750, 1:1000, 1:2000, 1:3000, 1:4000, 1:5000, 1:6000, 1:7000, 1:8000, 1:9000, or 1:10,000. For antibodies specific for the NPC analyte, a dilution of 1:200 is preferred. Preferred dilutions of the secondary antibody are 1:7000, 1:8000, 1:9000, or 1:10,000 (range 1:7000 to 1:10,000).

3.     Capture particles, such as colloidal gold particles (the amount of which depends on the amount of antibody or aptamer on the binding pad), are conjugated to one or more detection aptamers or antibodies for one or more desired target antigens.

4. Apply the bonded gold particles to the bonding pad and allow to dry.

5. Apply a blocking agent (e.g., BSA or casein) to the nitrocellulose membrane and/or binding pad to prevent nonspecific binding.

6. Assemble the lateral flow strip with the sample pad, binding pad, nitrocellulose membrane, and absorbent pad in the order described.

7. Cut the assembled test strips to the required size and shape.

Apply sample to lateral flow device

In one format, before applying the fluid sample to the lateral flow device, a buffer solution is applied to the sample pad to wet it and to the binding pad to activate the capture particles (e.g., colloidal gold particles).

Referring to FIG12A , a sample is applied to an application point 14 of the membrane strip, or to an application pad (if present). After the membrane strip and sample are in contact, the membrane strip is maintained under conditions (e.g., sufficient time and fluid volume) that allow the labeled binding agent to migrate along the membrane to a capture zone 18 by capillary action, pass through the capture zone, and subsequently out of the capture zone 18 (e.g., into a wicking pad), thereby removing any unbound labeled binding agent from the capture zone. In some embodiments, the sample migrates through a binding zone containing the binding agent. Analytes in the sample interact with the binding agent to form a capture complex.

As the applied sample passes through the membrane strip, the analyte (sample/control analyte) bound to the binding agent (capture complex) is immobilized in the capture zone 18 by the capture agent, preferably bound to immobilized capture particles. The capture zone 18 is preferably organized into one or more capture lines within a specific region of the capture zone. At that region, the one or more capture lines are used to capture the capture complex as it migrates through the capture lines. The capture zone 18 preferably contains multiple capture lines 22 for multiplex analysis and quantification.

Capillary action then continues to move any uncaptured binding agent beyond capture zone 18 , for example, into the wicking pad following capture zone 18. A secondary wash step can be used if necessary. The analytical fluid can be applied at the application point after the mixed fluid sample has been immersed in the membrane or, if present, in the application pad. The secondary wash step can be performed at any time thereafter, provided it does not dilute the mixed fluid sample. When captured particles are detected, the secondary wash step can help reduce background signal.

Analyte detection and interpretation of results

The amount of analyte bound to the capture agent in the capture zone (sandwich complex) can then be detected. Labeled binding agents or capture agents are preferably detected using a method appropriate for the type of label used. In some formats, the appearance of lines and/or color changes on the nitrocellulose membrane indicates the presence or absence of the desired analyte.

The amount of analyte in the sample is directly related to the amount of detector detected in the capture line. This value is preferably normalized by the amount of another detectable label immobilized in the membrane (e.g., the capture zone) to account for variations in the detection device and parameters (e.g., light intensity). This normalized value can then be plotted as a standard curve or response surface, correlating these normalized values to the analyte concentration. For example, a standard curve or response surface can be prepared in advance using analyte standards. In addition, three or more internal standard analytes can be detected in the analysis, and these internal standard analytes can be used to adjust or select the standard curve or surface based on the reference curve or surface.

In a preferred embodiment, the test result, indicated by the intensity and/or contrast of the visible color, reflects the detection strength of different NPC analytes. For example, in some embodiments, the test result output on the test strip is "no NPC," "pre-malignant NPC," and/or "malignant NPC." In some embodiments, the test result may indicate the probability of recurrence after NPC diagnosis, with output options such as "low" and/or "high."

IV. Sample collection equipment

Quantitative real-time analysis can involve the use of a sample collection device that is not in contact with the solid phase device fluid. The sample collection device can be any device that can contain a binding agent and to which a measured volume of a fluid sample can be added. Representative sample collection devices include sample tubes, test tubes, vials, pipettes or pipette tips, or syringes.

Sample collection swab

In a preferred embodiment, the sample collection device is a nasopharyngeal swab. Figures 11A to 11I illustrate a non-invasive rapid test sampling method using a next-generation 3D-printed nasopharyngeal swab, sample processing tube, and customized probe set for rapid antigen testing (RAT) for NPC diagnosis and recurrence prediction at home, in a community clinic, hospital, or medical laboratory. Figure 11A shows a 3D-printed nasopharyngeal swab customized for high throughput and minimal discomfort in collecting cellular and interstitial samples. Figure 11B is an enlarged side view of the swab tip design with indicated dimensions. Figure 11C is an enlarged top view of the swab tip design with indicated dimensions. Figures 11D to 11G show the design of a next-generation rapid diagnostic test station/hub, which includes a sample collection/distribution structure that maximizes the detection of biomarkers with consistency and can minimize sample input to detect more than one biomarker panel. The sections of the detection strip can be customized for personalized disease diagnosis and prognosis. For example, inflammation and drug resistance (DR) can be detected simultaneously in one strip. Figure 11D shows a view of all RAT boxes inserted into the sampling hub. Figure 11E shows another view showing the assembly or structure of the RAT box and before and after insertion into the sampling hub. Figure 11F shows a top view of a RAT cartridge inserted into a sampling hub (cut in the middle). Figure 11G shows a side view of a RAT cartridge inserted into a sampling hub (cut in the middle). Figure 11H shows a next-generation sample processing tube that includes a brush/bristles for more efficient release of biological material from a swab tip. Figure 11I shows a specially designed sample processing tube with short bristles designed to release material from a (nasopharyngeal) swab.

The dimensions disclosed in Figures 11B, 11C, 11F, and 11G are not limiting, as the various dimensions can be easily adjusted to within approximately +/- 10% above or below the recited values.

As shown in Figure 11B, the device includes a "head" for insertion into the nasal cavity, which is connected to a handle via a series of preferably cylindrical "rods" of varying diameters. 3D printing is a layered manufacturing technology that creates three-dimensional objects by building up successive layers of materials such as metals, plastics, and ceramics. These objects are generated from digital files, rendered using magnetic resonance imaging (MRI) or computer-aided design (CAD) drawings, allowing manufacturers to easily change or adapt the product as needed. 3D printing methods can differ in how the layers are deposited and the type of material used. A wide variety of 3D printers are available on the market, ranging from inexpensive models designed for consumers that can print small, simple parts, to commercial-grade printers capable of producing larger, more complex products.

The device is preferably made from a heat-pressable, medical-grade, high-modulus photopolymer resin, which is known in the art and summarized in, for example, Gutteridege et al., Annals of 3D Printed Medicine, Vol. 5, 100044 (https://doi.org/10.1016/j.stlm.2021.100044), Tables 3 and 4 therein.

The head portion of the device is generally cylindrical and includes "bristles," which are hollow cylindrical protrusions extending from the head body that collect cells and secretions. The head portion is hollow to collect scraped cells and secretions and allow the collected material to be released later for detection at the test strip. The dimensions of the head portion can be approximately 3 mm in diameter and approximately 12 to 15 mm in length (excluding the bristles), or approximately 5 mm in outer diameter/width and 14 to 17 mm in length if the bristles are included (see Figures 11B and 11C).

The bristles on the hollow cylindrical protrusion of the swab's head are used to collect scraped cells and nasopharyngeal secretions and allow the collected material to be released later for testing, such as at a test strip. The hollow cylindrical bristles are located throughout the head and at the tip of the swab to maximize material (nasopharyngeal secretions and cells) and collection. Each bristle is a hollow cylinder having an outer radius (R), an inner radius (r), a height, and a thickness (R-r). The hollow cylindrical bristles are approximately 0.3 mm thick, approximately 2 mm outer diameter, and approximately 1.3 mm high. The hollow cylindrical bristles can be located at approximately 0.75 mm intervals across the entire outer surface of the head of the device.

The dome-shaped crosshairs (approximately 0.1 mm thick) on the cylindrical protrusions are used to gently scrape cells, press against tissue, and squeeze out secretions. The dome-shaped crosshairs protrude approximately 0.2 mm from the plane of the villi, so the length from the top of the dome to the base of the hollow cylindrical villi (i.e., the part that rests on the outer surface of the head) is approximately 1.5 mm.

Connecting the head and handle of the device are: a first rod (connected to the head) with a diameter of approximately 1 mm; a second/middle rod with a diameter of approximately 2 mm and hollow, configured to extend or shorten the device by pushing the second rod onto (shortening) or away from (extending) the first rod; and a third rod with a diameter of approximately 1 mm. The length of the first rod can be approximately 20 to 50 mm, the length of the second/middle rod can be approximately 20 to 50 mm, and the length of the third rod can be approximately 10 to 30 mm.

In some embodiments, the handle of the device is designed as a hollow sphere. When squeezed, the material can be forced out of the swab. In contrast, when released, the material can be drawn into the swab. In some embodiments, the hollow sphere is made of a suitable plastic material.

Sample collection tube

A specialized tube for collecting biological material collected by a nasopharyngeal swab is provided. In some forms, the tube is a microcentrifuge tube having a brush/bristle as disclosed herein. Microcentrifuge tubes are small, tapered tubes. The top opening of the tube typically has a cap attached to the top of the tube by a plastic strap or hinge. These tubes are widely used by molecular biologists and biochemists. Microcentrifuge tubes are described in U.S. Patent No. 5,254,314, the contents of which are incorporated herein by reference.

The sample collection tube comprises a tube having a sealed bottom end, an open top end, and a cap that seals the top opening of the tube, the improvement comprising a flexible bristle/bristle brush.

This specially designed plastic tube, with flexible bristles/brushes on its inner wall, can enhance the efficiency and sensitivity of diagnostic tests for conditions such as nasopharyngeal cancer, where optimal sample collection is crucial for early detection and effective treatment.

design:

1. The tube is made of plastic material, usually polypropylene or a similar durable plastic, such as those listed above for swabs.

2. The inner wall of the tube is lined with multiple rows of soft, flexible, velvety bristles or brushes. In some versions, the tube has an inner diameter (excluding the brush) of about 9 mm and an outer diameter of about 11 mm. The long bristles (3.5 mm in length) result in an inner diameter of 2 mm for the opening into which the swab head is inserted, while the short bristles (1.5 mm in length) result in an inner diameter of 6 mm for the opening into which the swab head is inserted. The depth of the tube is about 40 mm.

3. The bristles are arranged in a spiral or helical pattern along the length of the interior of the tube, thereby producing a textured surface. In some forms, the bristles are cylindrical in shape with a spherical tip. In some forms, the bristles are "long bristles" having a length ranging from about 1.5 to 5 mm, preferably about 2 to about 3.5 mm. In this form, the bristle tips can be spaced about 2 to 6 mm apart. Thus, for example, when the bristle length is about 3.5 mm, the bristle tips are spaced about 2 mm apart (Figure 11H). The bristles can have a diameter of about 0.5 mm, with spaces between the bristles of about 1 to 2 mm. In some embodiments, the bristles are "short" bristles having a length ranging from about 0.5 to 2.5 mm, preferably from about 1 to about 1.5 mm. In this embodiment, the bristle tips can be spaced about 2 to 6 mm apart ( FIG. 11I ).

4. The bristles are made of synthetic medical-grade materials such as polypropylene (PP) that are gentle but effective in removing and capturing cells and other biological materials.

Function:

1.     When the nasopharyngeal swab is inserted into the tube, the bristles on the inner wall come into contact with the tip of the swab.

2. As the swab is rotated or moved gently back and forth in the tube, the bristles gently scrape and remove the collected biological material, including cells, mucus, and other relevant analytes, from the surface of the swab.

3. The removed biological material is then suspended in a liquid medium within the tube, producing a more concentrated sample for downstream diagnostic testing.

4. The spiral or helical configuration of the bristles helps ensure that the entire surface area of the swab tip is effectively scraped and sampled.

Benefits:

1. Increased sample throughput: Flexible bristles efficiently collect higher amounts of biological material from nasopharyngeal swabs, thereby improving the sensitivity and accuracy of diagnostic tests, such as nucleic acid amplification tests or cytological analysis.

2.     Sample collection consistency: Standardized tube design and controlled swiping action ensure more reproducible and reliable sample collection compared to traditional swab-only methods.

3. Reduced sample loss: The containment properties of the tube and the capture of biological material by the bristles help minimize the loss of sample material.

4. Ease of use: The simple tubing design allows for straightforward sample collection and processing, requiring minimal specialized training for healthcare providers.

An exemplary procedure for collecting a biological sample using a nasopharyngeal swab and processing the sample using a lateral flow assay as disclosed herein for NPC diagnosis and recurrence prediction is described. In this example, the lateral flow assay has two lines for NPC diagnosis and recurrence prediction, respectively. One or more of the following materials can be used to collect and process the biological sample: a nasopharyngeal swab, such as the 3D-printed nasopharyngeal swab disclosed above for tumor sample collection; a lateral flow device, such as a rapid test (with two lines for NPC diagnosis and recurrence prediction; a sample buffer; and a dropper).

The collection and processing of biological samples may include one or more of the following steps:

1. Collect a nasopharyngeal sample from the patient. Methods for collecting a nasopharyngeal sample are known and may include one or more of the following steps: (i) tilting the patient backward approximately 70 degrees, (ii) gently rotating a nasopharyngeal swab and inserting it into the nostril parallel to the palate (not upward) to a depth of less than one inch (approximately 2 cm) until resistance is met at the turbinates, (iii) rotating the swab against the nasal wall several times, and (iv) repeating steps (i) to (iii) in the other nostril using the same swab.

2. Insert the nasopharyngeal swab into the sample buffer tube and swirl the swab to ensure it is completely immersed in the buffer.

3. Place a few drops of sample buffer on the sample reservoir of the side flow device.

4.     Wait for the control line to appear, which confirms that the device is working properly.

5. After the control line appears, wait several minutes to detect the test analyte, if present. In some formats, the wait time can be up to 15 minutes.

6. Assess the intensity of the colors on the two lines to interpret the test results. The results, presented as the intensity and contrast of the visible colors, will reflect the detection strength of different biomarkers, indicating the degree of likelihood of having NPC disease. In some forms, if both lines appear with high intensity (i.e., the heavier or darker line), a positive result for NPC diagnosis and recurrence prediction can be concluded. A positive result for NPC diagnosis can only be concluded when the line indicator for NPC diagnosis appears with high intensity. If neither line appears with high intensity, it can be interpreted as a negative result. In some forms, the intensity of each line varies depending on the amount and type of biomarker detected, with higher intensities indicating a greater likelihood of NPC diagnosis or recurrence.

V. Set

Described herein are kits for diagnosing nasopharyngeal carcinoma (NPC) and/or monitoring its potential for recurrence. Specifically, a kit is provided for detecting one or more target analytes that serve as biomarkers for the development and recurrence of NPC. In one embodiment, the kit includes a lateral flow device as disclosed herein. Optionally, the kit includes a sample collection device (e.g., a nasopharyngeal swab), a sample buffer, and a dropper.

Kit components may additionally include known concentrations of analyte for generating a standard curve, capture particles, particles and binding buffer for coating the particles with a binding agent, disposal equipment (e.g., biohazard waste bags), and/or other information or instructions regarding the sample collection device (e.g., batch information, expiration date, etc.). The kit contains some or all of the materials required to measure one or more of the following analytes: HMGN2P3 ( high mobility group nucleosome binding domain 2 pseudogene 3 ); DNAJC11 ( DnaJ heat shock protein family ( Hsp40 ) member C11 ); EIF2AK1 ( eukaryotic translation initiation factor 2 alpha kinase 1 ); FAM234A ( sequence similarity family 234 member A ); PARPBP ( PARP1 binding protein ); ARL5A ( ADP- ribosylation factor -like GTPase 5A ) ; IL32 ( interleukin 32 ); and DHX57 ( DEXH box helicase 57 ), PSMA4 ( proteasome 20S subunit alpha 4 ), CALML3 ( calcineurin-like 3 ), SLC2A1 ( solute carrier family 2 member 1 ), SNX3 ( sorting ligand 3 ). ), LY6D ( lymphocyte antigen family 6 member D ), YBX1 ( Y- box binding protein 1 ), RPMS1 (open reading frame of the right transcript of Bam HI-A of Espionage virus).

In some versions, the kit contains a test strip that provides a positive reading only when one or more target analytes are detected. The test strip reading helps clinicians determine with sensitivity and specificity whether a patient has nasopharyngeal carcinoma and/or the likelihood of recurrence. In some versions, the kit can provide a positive reading for a single biomarker or for multiple biomarkers, such as a multiplex test strip.

VI. Computer-implemented system and method

Also described are CIS and/or CIM systems that include one or more AI platforms for analyzing biological data and outputting a cancer prevalence, such as NPCR/NPCD. The analysis is based on the expression of certain biomarkers or combinations of biomarkers in the biological data. The biomarkers or combinations of biomarkers are indicative of certain cell types. Preferably, the AI platform utilizes a "signature matrix," which has numerical entries for the expression of these biomarkers in a cell type. In some forms, the biomarkers are genes. For example, the rows in the signature matrix track the cell type, while the columns track the biomarkers (e.g., genes). Thus, the numbers at each i-th column and j-th row represent the relative gene expression in the cell type. The AI platform evaluates biological data (preferably test results from an individual) and predicts the occurrence of NPCR/NPCD based on comparison to the expression levels of genes or combinations of genes in the signature matrix. Exemplary biomarkers (predicting recurrence) are cytological (cell type) biomarkers, specifically markers of the LY6D ⁺ angioplastic SPB1/SPB epithelial subpopulation. For example, in a clinical setting, if the gene expression profile in the biological data contains a higher level of angioplastic SPB1 signature when compared to the signature matrix (i.e., the gene expression is similar to the signature matrix), this indicates an increased level of angioplastic SPB1 in the biological data, and/or if the gene expression profile contains a lower level of non-angioplastic SPB1, the individual is likely to have NPCR. This process can be performed by uploading the signature matrix and biological data (e.g., gene expression data for individual samples) to an AI platform. The AI platform analyzes the data and provides predictions. Predictions are provided in a visual format (e.g., a graphical user interface), an audio format (e.g., an audio signal announcing the prediction), or a combination thereof. One or more AI platforms have been trained and validated using data related to gene expression levels of these biomarkers, associated cell types, and/or the occurrence of NPCR/NPCD.

Also described is an AI platform that uses a signature matrix and biological data (e.g., test results from an individual) to predict the occurrence of cancer, such as NPCR/NPCD, wherein the AI platform is operably connected to a computer processor, wherein the AI platform is configured to analyze the data in the signature matrix and the biological data (e.g., test results from an individual) to make the prediction. The analysis is based on the expression of certain biomarkers or combinations of biomarkers in the biological data. The biomarkers or combinations of biomarkers are indicative of certain cell types. Preferably, the AI platform utilizes a "signature matrix" that has numerical entries for the expression of these biomarkers in the cell types. In some forms, the biomarkers are genes. For example, the rows in the signature matrix track the cell types, and the columns track the biomarkers (e.g., genes). Therefore, the numbers in each i-th column and j-th row represent the relative gene expression in a cell type. The AI platform evaluates biological data (preferably from individual test results) and predicts the occurrence of NPCR/NPCD based on comparison with the gene expression levels or combinations of genes in the signature matrix. Exemplary biomarkers (predicting recurrence) are cytological (cell type) biomarkers, specifically markers for the LY6D ⁺ proliferative SPB1/SPB epithelial subpopulation.

Also described is a non-transitory computer-readable medium having computer-executable instructions stored thereon, wherein the instructions are executed by a processor to perform a method for predicting the occurrence of cancer (e.g., NPCR/NPCD). The method involves (i) uploading a signature matrix and biological data (e.g., test results from an individual) to a computing device, and (ii) analyzing the data using an AI platform and delivering the results to a human, wherein the analysis is based on the expression of certain biomarkers or combinations of biomarkers in the biological data. The biomarkers or combinations of biomarkers are indicative of certain cell types. Preferably, the AI platform utilizes a "signature matrix" having numerical entries for the expression of these biomarkers in the cell types. In some forms, the biomarkers are genes. For example, the rows in the signature matrix track cell types, while the columns track biomarkers (e.g., genes). Therefore, the number at each i-th column and j-th row represents the relative gene expression level in a cell type. The AI platform evaluates biological data (preferably from individual test results) and predicts the occurrence of NPCR/NPCD based on comparisons to the gene expression levels or combinations of genes in the signature matrix. Exemplary biomarkers (predicting recurrence) are cytological (cell type) biomarkers, specifically markers for the LY6D ⁺ proliferative SPB1/SPB epithelial subpopulation.

The machine learning process may involve various supervised machine learning techniques, various semi-supervised machine learning techniques, and/or various unsupervised machine learning techniques. For example, machine learning programs can use logical regression, Gaussian Naive Bayes, random forest, gradient boosting, adaptive boosting, LPBoost, TotalBoost, BrownBoost, MadaBoost, LogitBoost, Extra Trees, linear discriminant analysis, support vector machine, decision tree, k-nearest neighbor method, alternating decision tree (ADTree), decision tree, function tree (FT), logical model tree (LMT), linear classifier, factor analysis, principal component analysis, neighbor component analysis, sparse filtering, random neighbor embedding, autoencoder, stacked autoencoder, neural network, convolutional neural network, feedforward neural network, tabular attention network (Tabular Attention Network) or any other machine learning algorithm or statistical algorithm. In some forms, the machine learning procedure includes, but is not limited to, support vector regression (SVR), linear least squares regression (LLSR), microarray microanatomy and differential analysis (MMAD), and digital ranking algorithm (DSA). The machine learning analysis can be performed using one or more of a variety of programming languages and platforms, such as R, Weka, Python, and/or Matlab. The machine learning analysis can be executed using a machine learning platform such as BigML.

Several computational tools have been applied to deconvolution of complex gene expression program (GEP) mixtures to infer cellular composition, including linear least squares regression (LLSR), microarray microdissection and differential analysis (MMAD), and digital sorting algorithms (DSA). While these methods are effective in enumerating highly diverse cell types in mixtures with minimal unknowns (e.g., lymphocytes, monocytes, and neutrophils in whole blood), their sensitivity to experimental noise, high unknown content, and closely related cell types limits their utility in TIL assessment. CIBERSORT is a computational method designed to address these challenges. Like other methods, CIBERSORT requires a specialized knowledge base of gene expression labels, called a "signature matrix," for deconvolution of the cell types of interest. However, compared to previous efforts, CIBERSORT implements a machine learning method called support vector regression (SVR), which improves deconvolution performance by combining feature selection with robust mathematical optimization techniques. In benchmark-calibrated experiments, CIBERSORT was more accurate than other methods in resolving closely related cell subpopulations and mixtures with unknown cell types, such as solid tissues. Therefore, CIBERSORT is suitable for high-throughput characterization of different cell types from complex tissues, such as TILs. This invention provides users with a practical blueprint for analyzing the white blood cell content in tumor gene expression datasets using CIBERSORT.

A bar graph shows the recurrence prediction performance of matched biomarkers derived from more expensive single-cell RNA sequencing data and data obtained from an AI-driven platform that performed deconvolution and cell fraction estimation based on less expensive whole-sample performance data (data not shown). The results show that both methods performed comparably and outperformed the existing gold-standard plasma EBV DNA test, particularly in terms of accuracy and positive predictive value.

FIG13 is a flow diagram of a computer implemented system (CIS) and/or method (CIM) comprising one or more discriminative artificial intelligence (AI) platforms for analyzing biological data using a signature matrix and outputting the incidence of NPCR/NPCD based on the expression levels of certain biomarkers or combinations of biomarkers in the biological data, wherein the certain biomarkers or combinations of biomarkers are indicative of certain cell types.

The disclosed methods may further include providing a diagnosis and prescribing one or more treatments to an individual when the presence and/or increased levels of one or more of the above-listed biomarkers are detected. The methods may include further testing, such as a biopsy.

The presence of specific biomarkers (DNA, RNA, and protein) associated with NPC diagnosis and disease recurrence should prompt the development of alternative treatment strategies, including 6-7 weeks of definitive radiation therapy at a dose of at least 70 Gy, with or without chemotherapy as an induction, concurrent, and/or adjuvant to radiation therapy; immunotherapy, including but not limited to the use of immune checkpoint inhibitors in the setting of induction, concurrent, and/or adjuvant to radiation therapy; and vaccination and/or targeted therapy for biomarkers of disease recurrence in the same setting of radiation therapy. If there are no biomarkers associated with disease recurrence, but there are biomarkers associated with the diagnosis of NPC, the patient should be treated: (1) with current standard of care, with radical radiotherapy, with or without induction, concurrent, and/or adjuvant chemotherapy; and immunotherapy, including (but not limited to) the use of immune checkpoint inhibitors in an induction, concurrent, and/or adjuvant setting; or (2) with preventive measures, including regular monitoring of diagnostic biomarker levels and, as appropriate, with or without vaccination and/or targeted therapy for the diagnostic biomarkers.

Treatment is by any method known in the art and includes, but is not limited to, radiation therapy or chemotherapy. Commonly used chemotherapeutic agents for the treatment of NPC include, but are not limited to, carboplatin (Paraplatin), epirubicin (Ellence), paclitaxel (Taxol), docetaxel (Taxotere), gemcitabine (Gemzar), capecitabine (Xeloda), and methotrexate. Chemotherapeutic agents may be used alone or in combination with other drugs. Thus, the disclosed methods include determining the presence of one or more mutations or expression biomarkers as disclosed herein and administering to an individual one or more drugs for treating cancer. The methods may further include guiding a treatment regimen for determining whether to initiate, continue, or interrupt treatment for nasopharyngeal carcinoma in an individual.

The disclosed methods, compositions, and devices can be further understood with the help of the following non-limiting examples.

Example

method

Patient recruitment and biopsy sample collection

This study was approved by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster (UW 19-157). All datasets/studies mentioned were conducted in accordance with ethical guidelines, and all patients provided written informed consent. Seventy-four patients with a histological diagnosis of NPC underwent nasal endoscopy and biopsy of the primary tumor and ultrasound-guided fine-needle aspiration of cervical lymph nodes (if clinically palpable and localizable by ultrasound). A total of 155 biopsies from multiple sites, including normal adjacent tumors, primary tumors in the nasopharynx, and tumors in cervical lymph nodes (if present), were collected and immediately processed for single-cell isolation and transcriptome sequencing.

single cell RNA Sequencing

84,187 epithelial cells were successfully profiled. After tissue digestion, single-cell suspensions were counted and viability assessed using a Countess II FL automated cell counter. Single-cell encapsulation and cDNA libraries were prepared using the Chromium Next GEM Single Cell V(D)J Reagent Kit v1.1 and the Chromium Next GEM Chip G Single Cell Kit according to the 10X Genomics Protocol. Cells in the suspension were counted and loaded into individual wells of a 10X Chromium Single Cell Chip. Single cells were then encapsulated in Gel Beads-in-emulsion (GEM) using the 10X Chromium Single Cell Controller. Subsequent steps were performed using the Single Cell V(D)J Kit v1 or v1.1 according to the manufacturer's protocol. Library size and concentration were determined by Qubit, quantitative PCR, and bioanalyzer analysis. Illumina sequencing (151 bp paired-end sequencing) was performed at the Centre for PanorOmic Sciences (CPOS), Genomics Core, LKS Faculty of Medicine, The University of Hong Kong.

Bioinformation Analysis

Quality control (QC) filters (200 = < nFeature_RNA <= 9000 & percent.mt <= 50) were applied. Scrublet doublet analysis was performed on cells from each sample to remove doublets. We followed the workflow recommended by the authors in their tutorial, setting the doublet rate to 5% of recovered cells. FastMNN was used for data integration and batch effect removal. Unsupervised clustering was used for cell type classification. CNVs were inferred using inferCNV using macrophages as reference cells, while SNVs were called using our modified Mutect2 pipeline. Progression-free survival analysis was performed using Kaplan-Meier (KM) curves, log-rank tests, and Cox regression to identify the association between abundance and relapse. Deconvolution analysis of GSE102349 (a publicly available bulk RNA sequencing dataset with relapse information) using CIBERSORTX was performed to validate the relapse prediction of selected biomarkers within the relapse-associated accretive subclusters.

Immunohistochemistry

NPC patient tissues were formalin-fixed, paraffin-embedded, and sectioned at 3 μm. Paraffin sections were deparaffinized in xylene and rehydrated through a gradient of ethanol. Prior to antibody staining, antigen retrieval was performed using Target Retrieval Solution (Dako) in 10 mM citrate buffer (pH 6.0). Sections were mounted with DPX mounting medium (Sigma), and stained sections were imaged using a Nikon Model Eclipse Ni-U microscope (Nikon).

Epstein - Barr virus code RNA In situ hybridization

Formalin-fixed, paraffin-embedded tumor sections were tested using in situ hybridization, and all patient tumors tested positive for Epstein-Barr virus-encoded RNA (EBER). Recurrent tumor samples (where available) from the three patients with local recurrence were also tested for EBER, and all were positive.

Droplet digital polymerase chain reaction

DNA was extracted from nasopharyngeal brush biopsies using the Qiagen DNeasy Blood and Tissue Kit. DNA concentration was measured using an SMA4000, and purity was assessed by measuring the OD260/OD280 ratio. The extracted DNA was stored at −20°C until use. Digital droplet PCR (ddPCR) analysis was performed on a QX200 AutoDG Droplet Digital PCR System (Bio-Rad). Genomic mutations were detected using a custom ddPCR probe kit according to the manufacturer's protocol. After droplet generation, the ddPCR protocol was as follows: 95°C for 10 min; 94°C for 15 s and 58°C for 60 s for 40 cycles; 98°C for 10 min; and 4°C for 5 min. The reaction temperature was changed at a rate of 2°C/s.

Enzyme-linked immunosorbent assay

Add 100 μl of sample to two wells of a Dynex Immulon 4HBX 96-well plate. After overnight incubation at 4°C, centrifuge the plate at 1400 rpm for 10 minutes. Discard the supernatant, dry the wells with a hair dryer for 5 minutes, and then fix with methanol for 10 minutes at room temperature. After discarding the methanol, use the plate immediately or wrap it in aluminum foil and store at -20°C. For probing, wash the plate four times with PBS containing 0.05% Tween 20 and then block with PBS containing 1% human serum albumin (HSA) for 1 to 2 hours at room temperature. After washing once, add the primary antibody. After a 1-hour incubation at room temperature, the plate was washed four times with PBS/Tween, followed by the addition of a 1:1000 dilution of biotin-conjugated rabbit anti-mouse immunoglobulin (Dako, Catalog No. 0354) dissolved in PBS/HSA/Tween. The plate was incubated at room temperature for 1 hour. Next, the plate was washed, and a 1:1000 dilution of ExtrAvidin peroxidase (Sigma, Catalog No. E-2886) dissolved in PBS/HSA/Tween was added. After a 45-minute reaction at room temperature, the plate was washed and TMB (Sigma) was added. After 5-10 minutes, color development was stopped by the addition of HCl.

Statistical analysis

Data are expressed as mean ± SEM. Parametric and nonparametric continuous variables were compared using the independent sample t-test/Wilcoxon rank-sum test and the ANOVA/Kruskal-Wallis test. Pearson/Spearman correlation and the chi-square test were used to assess correlations between variables. Univariate and multivariate analyses were performed using binary logistic regression to identify predictors of NPC recurrence. The Karben-Meier method was used to assess survival endpoints, including relapse-free survival (RFS) and overall survival (OS). The restricted mean survival time (RMST) was also performed to provide survival estimates. The log-rank test was performed to compare survival differences in subgroup analyses. Cox proportional hazards models were used for univariate and multivariate analyses to identify prognostic factors for RFS and OS. All statistical analyses were performed using the Statistical Package for Social Sciences (SPSS) version 25 (IBM, USA) or R programming. A P value < 0.05 (two-sided) was considered statistically significant.

result

The disclosure in this invention is based on large-scale NPC single-cell RNA sequencing (scRNA-seq).

Figures 1A and 1B are schematic diagrams illustrating the steps involved in discovering novel biomarkers for disease diagnosis and relapse prediction from single-cell RNA sequencing (scRNA-seq) studies of NPC using the assays presented herein. Seventy-four patients were enrolled in the scRNA-seq study. Matched biopsies were collected from normal adjacent tumors, primary tumors in the nasopharynx, and tumors from cervical lymph nodes. The median follow-up time for patients was three years. Five patients experienced relapse. Bioinformatic analyses were performed to identify human-based DNA mutations, RNA/protein expression, and cytological biomarkers associated with NPC malignancy and NPC relapse at initial diagnosis. As shown in Figure 1B, microbial EBV RNA transcripts were also detected and analyzed. Identify biomarkers with the highest phenotypic or NPCD and/or NPCR accuracy. Figure 1C is a schematic diagram showing the proposed sampling method and detection strategy that utilizes the present invention's (1) human-based DNA, RNA/protein biomarker panel, (2) EBV-based panel, and (3) cytology panel for either: (1) non-invasive rapid testing using biological samples from nasopharyngeal swabs or plasma, or (2) learning invasive endoscopic biopsy. Figure 1D is a schematic diagram showing that epithelial subpopulation (i.e., cytological) biomarkers can be applied to an AI-driven platform to analyze expression data (e.g., transcriptomics data including RNA sequencing and microarrays, and proteomics data including mass spectrometry) to calculate two risk scores (i.e., risk score 1 = abundance of anaplastic SPB1, and risk score 2 = difference between anaplastic SPB1 and non-anaplastic SPB1) to predict relapse at initial diagnosis before treatment and to inform early follow-up. Figure 1E is a schematic diagram showing an overview of all biomarkers discovered for NPCD and/or NPCR.

Figures 2A and 2B show mutations (i.e., SNVs and InDels) uniquely identified in neoplastic cells from all potential malignant sites during the initial diagnosis phase, before treatment, to predict relapse (i.e., NPCR). Figure 2A shows the top eight somatic mutations uniquely identified in neoplastic cells, which were strongly associated with relapse even at initial diagnosis. Figure 2B shows the performance metrics for the top eight relapse-predicting somatic mutations. Receiver operating characteristic (ROC) curves showed that the area under the ROC curve (AUC) for the top two relapse-predicting somatic mutations was 82.9% (IL32) and 79.5% (DHX57), respectively. Karben-Meier (KM) curves were generated for the first two relapse-predicting somatic mutations (data not shown). Combining the IL32 and DHX57 SNVs further enhanced relapse prediction performance, achieving 100% sensitivity, 84.2% specificity, and 86% accuracy. The receiver operating characteristic (ROC) curve exhibited an area under the ROC curve (AUC) of 92.1%.

Figures 3A to 3C show emerging mutations (i.e., SNVs and InDels) with high incidence in NPC patients at the time of initial diagnosis. Figure 3A shows the top three somatic mutations identified in proliferative cells in NPC patients. The results also showed that EMP2 mutations have high coverage in EBV-negative NPC patients. Figure 3B shows the performance indicators of individual EMP2 mutations in NPC diagnosis. Figure 3C shows that the disclosed individual mutation combination group can achieve NPC diagnosis with a sensitivity of 93.0%, which is significantly higher than the gold standard plasma EBV DNA copy number alone. If the mutation combination is used together with the EBV plasma EBV DNA copy number, the sensitivity can be further enhanced to 95.3%.

Figures 4A to 4E demonstrate that lymphocyte antigen 6 family member D (LY6D)-positive proliferative secretory basal cells (SPBs), specifically SPB1 (secretory induced basal cell cluster 1, hereinafter referred to as SPB1), are a unique and novel proliferative subpopulation consistently associated with relapse. Figure 4A is a UMAP showing a total of 30 distinct epithelial subpopulations discovered by unsupervised clustering from scRNA-seq data with known epithelial markers. Figure 4B is a violin plot demonstrating that SPB1 is a novel epithelial subpopulation derived from SPBs, discovered by scRNA-seq analysis of the present invention. LY6D was discovered as a biomarker for SPBs, particularly SPB1. The exogenous SPB1 was found to have the highest LY6D expression, while the remaining non-SPB1 subpopulations expressed lower LY6D. Statistical analysis demonstrated differences in the abundance of certain exogenous subpopulations between those who relapsed and those who did not, regardless of relapse time. Figure 4C is a table showing the following: Exogenous SPB1, SPB2, and SPB5 were found to be the most highly exogenous subpopulations consistently associated with relapse, as revealed by the Wilcoxon rank-sum test. Statistical analysis demonstrated differences in the abundance of certain exogenous subpopulations between those who relapsed and those who did not, considering relapse time. Figure 4D shows the log-rank test supporting the association of exogenous SPB1, SPB2, and SPB5 with relapse. Specifically, Cox regression analysis revealed that exogenous SPB1 was the only exogenous subpopulation that predicted relapse. A KM plot also showed that the presence of SPB1 was associated with a lower probability of relapse-free survival. Figure 4E presents a performance analysis, demonstrating that exogenous SPB1 had an AUC of 0.773 for relapse prediction, with an accuracy of 81%. The figure shows the AUC, a measure of performance without a specific detection threshold, while the table displays performance metrics at specific detection thresholds, including sensitivity, accuracy, and specificity.

Identification of relapse-associated genetic mutations in LY6D-positive nasopharyngeal SPB1 revealed that specific mutations harbored by LY6D ⁺ nasopharyngeal SPB1, as well as by general epithelial cells, were predominant in relapsed patients. As shown by scRNA-seq data, an HDAC2 mutation ( HDAC2 (chr6:113,970,875) [CT>C,CTT,CTTT,CTTTT,CTTTTT] InDel) was detected in nasopharyngeal tumor and lymph node nasopharyngeal SPB1. The results showed that HDAC2-mutant nasopharyngeal SPB1 in nasopharyngeal tumors was highly predictive of relapse, with a specificity of 96.1%, a sensitivity of 75%, and an accuracy of 94.5%. However, HDAC2-mutant apoptotic SPB1 in nasopharyngeal tumors or cervical lymph nodes was found to be highly predictive of relapse with a specificity of 96.1%, a sensitivity of 80%, and an accuracy of 94.6% (logistic regression-odds ratio = 9.8 and P value = 0.001; Cox regression-hazard ratio = 35.06 and P value = 0.001). A missense SNV [A to C] in the ATP synthase mitochondrial F1 complex assembly factor 1 (ATPAF1) gene (chr1:46668177) in LY6D-positive apoptotic SPB1 was identified as a predictor of early disease relapse. Results showed that ATPAF1 mutations harbored by LY6D-positive pluripotent SPB1 or epithelial cells were more prevalent in relapsed patients (Figure 5). The table shows the performance metrics for identifying relapses using scRNA-seq data generated in this paper, including detection of pluripotent SPB1 in nasopharyngeal or cervical lymph node tumors and ATPAF1 mutations harbored by different epithelial subpopulations. KM curves demonstrate that ATPAF1 mutant pluripotent SPB1 in nasopharyngeal tumors or lymph nodes is associated with lower relapse-free survival (RFS) using scRNA-seq data generated in this paper (data not shown). The results showed that ATPAF1 mutant SPB1 in nasopharyngeal tumors or lymph nodes significantly predicted recurrence, with an AUC of 0.890, which was superior to plasma EBV DNA testing (AUC = 0.736).

Survival probability of the LY6D-positive polyplasty subpopulation over time. LY6D immunohistochemical staining of nasopharyngeal FFPE sections collected at initial diagnosis confirmed that the LY6D-positive polyplasty subpopulation (with an Allred score ≥6) was primarily present in patients with relapse, as revealed by log-rank test and Cox regression analysis. Median survival was not reached for patients with a LY6D IHC Allred score <6; median survival was 27.9 months for patients with a LY6D IHC Allred score ≥6 (data not shown). Figure 6 shows the receiver operating characteristic (ROC) calculated using LY6D immunohistochemical staining for different histopathological scores to predict relapse (i.e., NPCR). The graph shows the AUC, a method of displaying performance without setting a specific detection threshold, while the table shows performance metrics, including sensitivity, accuracy, and specificity, at specific detection thresholds.

Figure 7A shows a performance table of the most significant relapse prediction biomarkers located in the extracellular space or cell surface categories in Cox regression analysis, Wilcoxon test, and logistic regression analysis. Figure 7B shows a performance table of the most significant relapse prediction biomarkers located not necessarily in the extracellular space or cell surface categories in Cox regression analysis, Wilcoxon test, and logistic regression analysis.

Figures 8A to 8C show newly identified diagnostic expression biomarkers with a high prevalence in NPC patients at initial diagnosis. Figure 8A shows the performance metrics of diagnostic expression biomarkers located in the extracellular space or cell surface categories in the Wilcoxon test and logistic regression, with a median difference between positive and negative results of at least 0.5. The top-performing biomarkers, CKAP4, SYNGR2, and CFL1, had AUCs of 96.3%, 92.2%, and 91.7%, respectively. Figure 8B shows expression biomarkers that were not necessarily located in the extracellular space or cell surface categories but showed significance in the Wilcoxon test and were higher in NPC patients than in non-NPC patients. Figure 8C shows diagnostic biomarkers that are not necessarily localized in the extracellular space or on the cell surface but are significantly expressed in the Wilcoxon assay in non-NPCs compared to NPCs. The biomarker combination of clonidine (C-C motif) ligand 20 (CCL20), IL-32, and lipocalin-2 (LCN2) achieved 100% sensitivity and 96.3% PPV for NPC diagnosis (i.e., NPCD).

Figure 9A shows that EBV biomarker detection in nasopharyngeal tumors is more sensitive than plasma EBV DNA. EBV transcripts detected using the scRNA-seq method of this invention achieved 100% sensitivity in identifying NPC patients. Even patients with plasma EBV DNA copies of 0 copies/ml could be identified. Figure 9B demonstrates that EBV expression biomarkers, particularly RPMS1, can predict NPC recurrence (i.e., NPCR).

Figures 10A to 10C demonstrate the performance of artificial intelligence (AI)-driven SPB1-guided relapse prediction/diagnosis prior to treatment using an independent RNA-sequencing dataset. Figure 10A is a table showing AI-driven subpopulation abundance estimates using biomarkers identified from various epithelial subpopulations, both ectopic and non-ectopic, in an independent RNA-seq dataset. A Wilcoxon rank-sum test was used to identify significant differences between relapse and non-relapse groups. A KM plot shows the correlation between AI-driven ectopic SPB1 abundance estimates (i.e., risk score 1) and relapse. AI-driven ectopic SPB1 abundance estimates achieved an AUC of 0.790 for relapse identification. AI-driven entheogenic SPB1 abundance estimation achieved a sensitivity and accuracy of 75.0% in recurrence prediction. Figures 10B and 10C demonstrate the discriminatory results of AI-driven non-entheogenic SPB1 abundance assessment for false-positive findings. The optimized AI-driven SPB1 recurrence prediction score (i.e., risk score 2), calculated based on the difference between entheogenic and non-entheogenic SPB1, achieved a sensitivity of 87.5% and an accuracy of 86.1%. Figure 10D is a schematic diagram illustrating the use of the optimized AI-driven SPB1-guided recurrence prediction score for NPC recurrence diagnosis before treatment, aiming to maximize the beneficial outcomes of treatment.

Figures 11A through 11I illustrate a rapid antigen test (RAT) for NPC diagnosis and recurrence prediction, performed at home, in a community clinic, hospital, or medical laboratory. It uses a next-generation 3D-printed nasopharyngeal swab, sample processing tube, and custom probe set to provide a non-invasive and rapid test sampling method. Figure 11A shows a 3D-printed nasopharyngeal swab customized for high throughput and minimal discomfort during cellular and interstitial sample collection. Figure 11B is an enlarged side view of the swab tip design with dimensions indicated. Figure 11C is an enlarged top view of the swab tip design with dimensions indicated. Figures 11D to 11G show the design of a next-generation rapid diagnostic test station/hub, which includes a sample collection/distribution structure that maximizes the detection of biomarkers with consistency and can minimize sample input to detect more than one biomarker panel. The sections of the detection strip can be customized for personalized disease diagnosis and prognosis. For example, inflammation and drug resistance (DR) can be detected simultaneously in one strip. Figure 11D shows a view of all RAT boxes inserted into the sampling hub. Figure 11E shows another view showing the assembly or structure of the RAT box and before and after insertion into the sampling hub. Figure 11F shows a top view of the RAT cartridge inserted into the sampling hub (cut in the middle). Figure 11G shows a side view of the RAT cartridge inserted into the sampling hub (cut in the middle). Figure 11H shows a next-generation sample processing tube that includes a brush/bristles for more efficient release of biological material from the swab head. Figure 11I shows a specially designed sample processing tube with short bristles designed to release material from a (nasopharyngeal) swab.

Figures 12A-12B are schematic diagrams of a side-flow device made from a membrane strip with an application point at the proximal end, followed by a binding zone, a capture zone, and an absorption zone. Arrows indicate the direction of side-flow from the proximal end to the distal end. Multiple capture lines are shown in the capture zone.

A bar graph was generated to demonstrate the recurrence prediction performance of matched biomarkers derived from more expensive single-cell RNA sequencing data and data obtained from an AI-driven platform that performed deconvolution and cell fraction estimation based on less expensive whole-sample performance data (data not shown). The results showed that both methods performed comparably and outperformed the existing gold-standard plasma EBV DNA test, particularly in terms of accuracy and positive predictive value.

Figure 14A is a line graph showing the association between different epithelial cell types and relapse. In normal tissue adjacent to the tumor, non-emulsifying fibroblasts tended to be more abundant in the absence of relapse compared with relapse, while non-emulsifying multipotent basal cells (MPB) tended to be more abundant in relapse compared with the absence of relapse. In primary nasopharyngeal tumors, non-emulsifying fibroblasts and non-emulsifying cells undergoing the transition to secretory goblet cells tended to be more abundant in the absence of relapse, while non-emulsifying MPB and secretory-primed basal cells (SPB) tended to be more abundant in relapse. Along with the increase in proliferative cells, SPBs and proliferating basal cells (PB) tended to be more abundant in relapse. In lymph nodes, activated basal cells (AB) tended to be more abundant in the absence of relapse, while basal cells (excluding AB, SPB, MPB, and PB) tended to be more abundant in relapse. Figure 14B shows a statistical analysis of the abundance of different major epithelial cell states between relapse and non-relapse in different tissue types. In normal tissue adjacent to tumors and nasopharyngeal primary tumors, basal cell states increased significantly in relapse, while ciliary and goblet cells tended to decrease. Figure 14C shows a statistical analysis of the abundance of different major epithelial subtypes in different tissue types, between relapse and non-relapse groups. Ciliary cells were the only subtype found to be significantly decreased in normal tissue adjacent to the tumor at relapse compared to the non-relapse group. In normal tissue adjacent to the tumor, the MPB and SPB showed an increase in relapse, while goblet-like transition cells tended to decrease in relapse. In primary nasopharyngeal tumors, basal cells, which typically comprise the SPB (excluding AB, MPB, and goblet-like transition cells), tended to increase in relapse, while AB, ciliary, goblet, and goblet-like transition cells tended to decrease in relapse. In lymph nodes, AB tends to decrease in relapse, while basal cells (excluding AB, MPB, SPB, and goblet cell transition cells) tend to increase in relapse, and AB tends to decrease in relapse.

FIG15 shows a table showing that the proliferative MPB1 subset was significantly upregulated in lymph nodes in the relapse group compared to the non-relapse group.

Univariate and multivariate Cox regression analyses confirmed that an Allred score ≥6, the presence of enlarged SPB1 subsets in the nasopharynx and nasopharynx/lymph nodes, and the presence of enlarged ATPAF1 mutant SPB1 subsets in the nasopharynx and nasopharynx/lymph nodes were independent predictors of recurrence. Univariate and multivariate Cox regression analyses confirmed that an Allred score ≥6 was an independent predictor of overall survival.

In summary, the present invention discovered several previously disclosed DNA mutation and expression biomarkers that outperform many previously identified biomarkers involved in NPC diagnosis and recurrence prediction.

Those skilled in the art will be able to identify or ascertain, using no more than routine experimentation, numerous equivalent variations to the specific embodiments of the methods and compositions described herein. Such equivalents are intended to be encompassed by the appended claims.

MIC SNT signatures for SPB and SPB1 subsets compared to other epithelial cell types used for AI analysis :

10: Lateral flow device 12: Solid matrix 14: Application point 16: Binding zone 18: Capture zone 20: Absorption zone 22: Capture line

Figures 1A and 1B are schematic diagrams illustrating the steps involved in the discovery of novel biomarkers from single-cell RNA sequencing (scRNA-seq) studies of NPC, the largest-scale test to date for disease diagnosis and relapse prediction. Seventy-four patients were enrolled in the scRNA-seq study. Matched biopsy samples were collected from normal adjacent tumors, primary nasopharyngeal tumors, and tumors from cervical lymph nodes. The median follow-up duration for patients was 3 years. Five patients experienced relapse. Bioinformatic analyses were performed to identify human-based DNA mutations, RNA/protein expression, and cytological biomarkers associated with NPC malignancy and NPC relapse at initial diagnosis ( Figure 1A ). Microbial EBV RNA transcripts were also detected and analyzed. Identify biomarkers with the highest neoplasticity or NPCD and/or NPCR accuracy ( Figure 1B ). Figure 1C is a schematic diagram showing exemplary sampling methods and detection strategies that utilize the disclosed (1) human-based DNA, RNA/protein biomarker panels, (2) EBV-based panels, and (3) cytology panels for either (1) non-invasive rapid testing using biological samples from nasopharyngeal swabs or plasma, or (2) learned invasive endoscopic biopsy. Figure 1D is a schematic diagram showing how an epithelial subpopulation (i.e., cytological) biomarker panel can be applied to an AI-driven platform to analyze expression data (e.g., transcriptomics data including RNA sequencing and microarrays, proteomics data including mass spectrometry) and calculate two risk scores (i.e., risk score 1 = abundance of anaplastic SPB1 and risk score 2 = difference between anaplastic SPB1 and non-anaplastic SPB1) to predict relapse at initial diagnosis before treatment and provide early information during follow-up. Figure 1E is a schematic diagram showing an overview of biomarkers discovered for NPCD and/or NPCR.

Figures 2A and 2B show mutations (i.e., SNVs and InDels) uniquely identified in blasts from all potential malignant sites that predict relapse at initial diagnosis (i.e., NPCR) before treatment. Figure 2A shows the top eight somatic mutations uniquely identified in blasts that were strongly associated with relapse even at initial diagnosis. Figure 2B shows the performance metrics for the top eight somatic mutations that predict relapse.

Figures 3A to 3C show emerging mutations (i.e., SNVs and InDels) with high incidence in NPC patients at the time of initial diagnosis. Figure 3A shows the top three somatic mutations identified in proliferative cells in NPC patients. EMP2 mutations were also found to have high coverage in EBV-negative NPC patients. Figure 3B shows the performance indicators of individual EMP2 mutations in NPC diagnosis. Figure 3C shows that the disclosed individual mutation combination group can achieve NPC diagnosis with a sensitivity of 93.0%, which is significantly higher than the gold standard plasma EBV DNA copy number alone. If the mutation combination is used together with the EBV plasma EBV DNA copy number, the sensitivity can be further enhanced to 95.3%.

Figures 4A to 4E demonstrate that lymphocyte antigen 6 family member D (LY6D)-positive proliferative secretory-induced basal (SPB) cells, specifically SPB1 (secretory-induced basal cell cluster 1, hereinafter referred to as SPB1), are a unique and novel proliferative subpopulation that consistently correlates with relapse and predicts relapse with high accuracy. Figure 4A is a UMAP showing a total of 30 distinct epithelial subpopulations discovered by unsupervised clustering from scRNA-seq data with known epithelial markers. Figure 4B is a violin plot demonstrating that SPB1 is a novel epithelial subpopulation derived from the SPB, discovered by scRNA-seq analysis of the present invention. LY6D was discovered as a biomarker for the SPB, specifically SPB1. Ecologic SPB1 was found to have the highest LY6D expression, while the remaining non-SPB1 subpopulations expressed lower LY6D. Figure 4C is a statistical analysis table showing the differences in abundance of some ecologic subpopulations between those who relapsed and those who did not, regardless of relapse time. Ecologic SPB1, SPB2, and SPB5 were identified as the most highly ecologic subpopulations consistently associated with relapse, as revealed by the Wilcoxon rank sum test. Figure 4D shows the log-rank test results supporting the association of ecologic SPB1, SPB2, and SPB5 with relapse. Specifically, Cox regression analysis revealed ecologic SPB1 as the only ecologic subpopulation that predicted relapse. The KM plot also showed that the presence of SPB1 was associated with a lower probability of relapse-free survival. Figure 4E shows the performance analysis, which shows that the AUC for relapse prediction using exogenous SPB1 was 0.773, with an accuracy of 81%.

FIG5 shows the performance indicators for identifying relapses by detecting metastatic SPB1 and ATPAF1 mutations in different epithelial subsets in nasopharyngeal or cervical lymph node tumors using scRNA-seq data from the present invention.

Figure 6 shows the ROC curves of the AUCs of different histopathological scores with LY6D immunohistochemistry for predicting recurrence (i.e., NPCR).

Figure 7A shows the performance metrics of the most significant relapse prediction expression biomarkers in the extracellular space or cell surface categories in Cox regression analysis, Wilcoxon test, and logistic regression analysis. Figure 7B shows the performance metrics of the most significant relapse prediction expression biomarkers in the Cox regression analysis, Wilcoxon test, and logistic regression analysis, but not necessarily in the extracellular space or cell surface categories.

Figures 8A to 8C show newly identified diagnostic expression biomarkers with a high prevalence in NPC patients at initial diagnosis. Figure 8A shows the performance metrics of diagnostic expression biomarkers that were significant in the Wilcoxon test and logistic regression, falling within the extracellular space or cell surface categories, with a median difference of at least 0.5 between positive and negative results. Figure 8B shows diagnostic expression biomarkers that were significant in the Wilcoxon test, but not necessarily within the extracellular space or cell surface categories, and were expressed more in NPC than non-NPC. Figure 8C shows diagnostic biomarkers that were significantly expressed in the Wilcoxon assay, not necessarily in the extracellular space or cell surface compartments, and whose expression was higher in non-NPCs than in NPCs. The results showed that the expression biomarker combination of trending factor (CC motif) ligand 20 (CCL20), IL-32, and lipocalin-2 (LCN2) achieved 100% sensitivity and 96.3% PPV for NPC diagnosis (i.e., NPCD).

Figure 9A demonstrates that EBV biomarker detection in nasopharyngeal tumors is more sensitive than plasma EBV DNA. EBV transcripts detected by scRNA-seq achieved 100% sensitivity in identifying NPC patients. Even patients with plasma EBV DNA copies = 0 copies/ml were identified. Figure 9B demonstrates that EBV expression biomarkers, particularly RPMS1, can predict NPC relapse (i.e., NPCR).

Figures 10A to 10D demonstrate the performance of artificial intelligence (AI)-driven SPB1-guided relapse prediction/diagnosis before treatment using an independent RNA-sequencing dataset. Figure 10A is a table showing AI-driven subpopulation abundance estimates in an independent RNA-seq dataset. Significant differences between relapse and non-relapse groups were identified using the Wilcoxon rank-sum test. Figures 10B and 10C demonstrate the identification of AI-driven non-enriched SPB1 abundance assessment for detecting false positives and the calculation of an optimized AI-driven SPB1 recurrence prediction score (i.e., risk score 2) based on the difference between enriched and non-enriched SPB1, achieving 87.5% sensitivity and 86.1% accuracy. Figure 10D is a schematic diagram demonstrating the beneficial results of using the optimized AI-driven SPB1-guided recurrence prediction score for NPC recurrence diagnosis before treatment to maximize treatment efficacy.

Figures 11A to 11I illustrate the non-invasive rapid testing sampling process using a next-generation 3D-printed nasopharyngeal swab, sample processing tube, and customized probe set. This test is used for NPC diagnosis and recurrence prediction and can be performed as a rapid antigen test (RAT) at home, in community clinics, in hospitals, or in medical laboratories. Figure 11A shows a 3D-printed nasopharyngeal swab customized for high throughput and minimal discomfort during cellular and interstitial sample collection. Figure 11B is an enlarged side view of the swab tip design with designated dimensions. Figure 11C is an enlarged top view of the swab tip design with designated dimensions. Figures 11D to 11G show the design of a next-generation rapid diagnostic test station/hub, which includes a sample collection/distribution structure that maximizes the detection of biomarkers with consistency and can minimize sample input to detect more than one biomarker panel. The sections of the detection strip can be customized for personalized disease diagnosis and prognosis. For example, inflammation and drug resistance (DR) can be detected simultaneously in one strip. Figure 11D shows a view of all RAT boxes inserted into the sampling hub. Figure 11E shows another view showing the assembly or structure of the RAT box and before and after insertion into the sampling hub. Figure 11F shows a top view of the RAT cartridge inserted into the sampling hub (cut in the middle). Figure 11G shows a side view of the RAT cartridge inserted into the sampling hub (cut in the middle). Figure 11H shows a next-generation sample processing tube that includes a brush/bristles for more efficient release of biological material from the swab head. Figure 11I shows a specially designed sample processing tube with short bristles designed to release material from a (nasopharyngeal) swab.

Figures 12A - 12B are schematic diagrams of a side-flow device made from a membrane strip with an application point at the proximal end, followed by a binding zone, a capture zone, and an absorption zone. Arrows indicate the direction of side-flow from the proximal end to the distal end. Multiple capture lines are shown in the capture zone.

FIG13 is a flow diagram of a computer implemented system (CIS) and/or method (CIM) comprising one or more discriminative artificial intelligence (AI) platforms, which analyzes biological data using a signature matrix and outputs the incidence of NPCR/NPCD based on the expression levels of certain biomarkers or combinations of biomarkers in the biological data, wherein the certain biomarkers or combinations of biomarkers are indicative of certain cell types.

Figure 14A includes line graphs showing the correlation between different epithelial cell types and relapse. In normal tissue adjacent to tumors, non-emulsifying fibroblasts tend to be more abundant in the absence of relapse than in relapse, while non-emulsifying multipotent basal cells (MPB) tend to be more abundant in relapse than in the absence of relapse. In primary nasopharyngeal tumors, non-emulsifying fibroblasts and non-emulsifying cells that undergo the transition to secretory goblet cells tend to be more abundant in the absence of relapse, while non-emulsifying MPB and SPB cells tend to be more abundant in relapse. Along with more proliferative cells, SPBs and proliferating basal cells (PB) tend to be more abundant in relapse. In lymph nodes, activated basal cells (AB) tend to be more abundant in relapse-free tissues, while basal cells (excluding AB, SPB, MPB, and PB) tend to be more abundant in relapse. Figure 14B shows a statistical analysis of the abundance of different major epithelial cell states between relapse and non-relapse patients in different tissue types. Figure 14C shows a statistical analysis of the abundance of different major epithelial subtypes between relapse and non-relapse patients in different tissue types.

FIG15 shows a table showing that in lymph nodes , a proliferative MPB1 subset was found to be significantly upregulated in the relapse group compared to the non-relapse group.

Claims (30)

A method for detecting recurrence of nasopharyngeal carcinoma (NPC) in an individual, wherein the method comprises: detecting the presence of one or more markers selected from the group consisting of: (a) genetic mutations, and/or (b) cytological markers, wherein the cytological markers comprise LY6D ⁺ cells, and/or optionally, biomarker expression, wherein the genetic markers are: i) single nucleotide variations (SNVs) in: (a) HMGN2P3 (high mobility group nucleosome binding domain 2 pseudogene 3) (chr16:26,032,755); (b) ARL5A (ADP-ribosylation factor-like GTPase 5A) (chr2:151,828,247), DHX57 (DExH box helicase 57) (chr2:38,868,300); (c) IL32 (interleukin 32) (chr16:3,065,801), and/or (d) ATPAF1 (ATP synthase mitochondrial F1 complex assembly factor 1) (chr1:46,668,177); or ii) an InDel among the following: (a) DNAJC11 (DnaJ heat shock protein family (Hsp40) member C11) (chr1:6,667,742); (b) EIF2AK1 (eukaryotic translation initiation factor 2 alpha kinase 1) (chr7:6,046,108); (c) FAM234A (Family With Sequence Similarity 234 Member A) a) (chr16:254,566); (d) PARPBP (PARP1 binding protein) (chr12:102,123,938), and/or (e) HDAC2 (histone deacetylase 2) (chr6:113,970,875). The method of claim 1, wherein the SNV is selected from the group consisting of: a) [T>C] in IL32 ; (b) [T>A] in DHX57 ; (c) [G>A] in HMGN2P3 ; (d) [A>G] in ARL5A ; (e) [A>C] in ATPAF1 ; and/or (f) [A>T] in LMO4 . The method of claim 1, wherein the InDel is selected from the group consisting of: (a) [A＞AT] InDel in EIF2AK1 ; (b) [TTC＞T.TTCTC] InDel in DNAJC11 ; (c) [G＞GT] InDel in FAM234 ; (d) [G＞A.GA] InDel in PARPBP ; (e) [CT＞C,CTT,CTTT,CTTTT,CTTTTT] InDel in HDAC2 ; and (f) [CCAG＞C] InDel in TACSRD2 . The method of claim 1, wherein the cytological marker is a cytological mutation or a cytologically specific expression. The method of claim 3, wherein the cytological mutation is selected from the group consisting of: (i) HDAC2 (chr6:113,970,875) [CT>C,CTT,CTTT,CTTTT,CTTTTT] InDel in the angioblastic SPB1 cells present in the biological sample; (ii) [A to C] missense SNV ATPAF1 (chr1:46668177) in the LY6D ⁺ angioblastic SPB1 cells present in the biological sample; (iii) [CCAGC] in-frame deletion in TACSRD2 (chr 1:5857099) in the LY6D ⁺ angioblastic SPB1 cells present in the biological sample; and/or (iv) LMO4 in the LY6D ⁺ angioblastic SPB1 cells present in the biological sample. (A to T) 5'UTR single nucleotide sequence in (chr 1:87328973). The method of claim 3, wherein the method comprises detecting the presence of angiopoietic LY6D ⁺ cells in the biological sample, wherein the angiopoietic LY6D ⁺ cells express a marker selected from the group consisting of: (a) KRT16 (keratin 16); CEBPD (CCAAT enhancer binding protein delta); CDKN1A (cyclin-dependent kinase inhibitor 1A); PGM2 (phosphoglucomutase 2); (b) MEG3 (maternally expressed 3); and CTNNBIP1 (tetherin beta interacting protein 1); (c) IGF2BP3 (insulin-like growth factor 2 mRNA binding protein 3); FAF1 (Fas-associated factor 1); DUSP11 (bispecific phosphatase 11); and CLDND1 (claudin domain-containing 1); (d) BAG4 (BAG co-chaperone 4); SIPA1L2 (signal-induced proliferation-associated 1-like 2); AP3M2 (adaptor-associated protein complex 3 subunit μ2); and SERPINB12 (serine protease inhibitor family B member 12); and (e) CALML3 (calcineurin-like 3); CLCA4 (chloride channel accessory protein 4); GPX2 (glutathione peroxidase 2) and LSP1 (lymphocyte-specific protein 1). The presence or increased abundance of proliferative LY6D ⁺ cells compared to epithelial cells indicates relapse. The method of claim 3, wherein the method comprises detecting the presence of non-metastatic LY6D ⁺ cells in the biological sample, wherein the non-metastatic LY6D ⁺ cells express markers selected from the group consisting of: (a) CLCA4 (chloride channel auxin 4); SYT8 (synaptotagmin 8); FGFR3 (fibroblast growth factor receptor 3); and/or (b) SUSD4 (Sushi domain-containing 4); TNNT3 (tyrosin T3, fast skeletal type); and NSG1 (neuronal vesicle transport-associated 1), wherein the absence or low level of the non-metastatic LY6D ^{+ cells} indicates relapse. The method of any one of claims 1 to 7, wherein the method comprises detecting the presence of mutations in IL32 and DHX57 . The method of any one of claims 1 to 8, wherein the method detects recurrence with a specificity of at least 80%. The method of any one of claims 1 to 7, wherein the method detects recurrences with 100% sensitivity. A method for detecting the presence of NPC markers in an individual ("NPCD"), wherein the method comprises: detecting in a biological sample obtained from the individual: (a) SNVs in one or more genes selected from the group consisting of: (i) EEF2KMT ([G>A] (chr16:5,091,852); (ii) SOCS1 ([A>C] (chr16:11,255,404); (iii) TESMIN ([A>T] (chr11:68,750,666); and (iv) IGFBP7 ([T>C] (chr4:57,110,068), optionally in combination with EBV; (b) InDels selected from the group consisting of: (i) EMP2 ([C＞CA] (chr16:10,543,633); (ii) IL32 ([AAGGTGACT……CGCCAGC＞A,AAGC] (chr16:3,065,653); and (iii) CSTA ([G＞GA] I (chr3:122,337,565); and/or (c) expression of a biomarker selected from the group consisting of: (i) CKAP4 (cytoskeleton-associated protein 4), (ii) SYNGR2 (neural cell vesicle recycling protein (Synaptogyrin)-2), (iii) CFL1 (filin 1) and (iv) RPMS1; and/or (v) CCL20, (vi) IL32, (viii) LCN2, wherein CCL20 _high + IL32 _high + LCN2 _low indicates the presence of NPC, wherein CCL20 _high refers to a high level of CCL20, IL32 _high refers to a high level of IL32, and LCN2 _low refers to a low level of LCN2. The method of claim 11, wherein the presence of one or more mutations or the expression of one or more biomarkers indicates NPC in the individual with a sensitivity of at least about 80%, preferably at least 85%, 90% or at most 95%. The method of any one of claims 1 to 11, wherein the method comprises detecting mRNA expression in the biological sample, comprising performing DNA or RNA sequencing on the biological sample, optionally wherein the DNA or RNA sequencing is single cell RNA sequencing and RT-PCR. The method of any one of claims 1 to 11, wherein the method comprises detecting RNA and proteins/peptides or fragments thereof, the method comprising contacting the biological sample with a binding partner that binds to the one or more biomarkers, and detecting binding between the antibody and the one or more biomarkers. The method of any one of claims 1 to 12, wherein the mutation is detected by performing a DNA or RNA sequencing process on the biological sample. The method of claim 14, wherein the binding is determined by immunohistochemistry (IHC), enzyme-linked immunosorbent assay (ELISA), and in a lateral flow assay. The method of claim 16, wherein the lateral flow assay comprises: (a) reacting the biological sample with: (i) a binding agent that selectively binds the biomarker to form a capture complex of the binding agent and the analyte, and (ii) a capture agent that selectively binds the capture complex but does not bind free analyte to form a sandwich complex of the binding agent, the capture agent, and the analyte, and (b) measuring the formation of the sandwich complex; wherein the amount of the sandwich complex formed is directly related to the amount of the analyte in the biological sample. A lateral flow device comprises a solid matrix having an application site 26, a binding region 16, a capture region 18, and an absorption region 20, wherein the binding region comprises: (i) a binding partner that binds to a biomarker indicative of NPC recurrence, wherein the biomarker is selected from the group consisting of: PSMA4 (proteasome 20S subunit alpha 4), CALML3 (calcineurin-like 3), SLC2A1 (solute carrier family 2 member 1), SNX3 (sorting nexin 3), LY6D (lymphocyte antigen 6 family member D), YBX1 (Y-box binding protein 1), and RPMS1 (Epstein-Barr virus or (ii) a binding partner that binds a biomarker indicative of NPCs, wherein the biomarker is selected from the group consisting of markers associated with the presence of NPCs selected from the group consisting of CKAP4; SYNGR2; CFL1 and RPMS1. The device of claim 18, wherein the binding partner that binds to the biomarker is an antibody. A nasopharyngeal swab device comprises a head portion for insertion into the nasal cavity, a handle, and a series of preferably cylindrical rods of different diameters connecting the head to the handle, wherein the head is preferably a hollow cylinder and further comprises hollow cylindrical bristles disposed on its surface for collecting cells and secretions. A nasopharyngeal swab device as claimed in claim 20, wherein the dimensions of the head portion are approximately 3 mm in outer diameter and approximately 12 to 15 mm in length without taking into account the hollow cylindrical bristles; or the dimensions of the head portion are approximately 5 mm in outer diameter/width and 14 to 17 mm in length with taking into account the hollow cylindrical bristles. A nasopharyngeal swab device as claimed in claim 20 or 21, wherein each bristle is a hollow cylinder having an outer radius (R), an inner radius (r), a height (which is the distance between a first plane and a second plane of the bristle), a thickness (R-r), a first plane disposed on an outer surface of the head, and a second plane disposed on an exterior of the swab. The nasopharyngeal swab device of claim 22, wherein the hollow cylindrical bristles have a thickness of approximately 0.3 mm, an outer diameter of approximately 2 mm, and a height of approximately 1.3 mm. The nasopharyngeal swab device of any one of claims 20 to 23, wherein the hollow cylindrical bristles are located on the entire outer surface of the head of the device at intervals of approximately 0.75 mm. The nasopharyngeal swab device of any one of claims 20 to 24, comprising dome-shaped crosshairs (approximately 0.1 mm thick) disposed on the hollow cylindrical bristles, configured to gently scrape the cells, press against tissue, and squeeze out the secretions. A nasopharyngeal swab device as claimed in claim 25, wherein the dome-shaped cross line protrudes from the second plane of the hollow cylindrical bristles by approximately 0.2 mm, so that the length from the top of the dome to the base of the hollow cylindrical bristles (i.e., the portion resting on the outer surface of the head) is approximately 1.5 mm. A nasopharyngeal swab device as claimed in any one of claims 20 to 26, wherein the structure connecting the head and the handle of the nasopharyngeal swab device includes: a first rod (connected to the head) having a diameter of approximately 1 mm; a second/middle rod having a diameter of approximately 2 mm and being hollow and configured to extend or shorten the nasopharyngeal swab device by pushing the second rod onto (shortening) or off (extending) the first rod; and a third rod having a diameter of approximately 1 mm. The nasopharyngeal swab device of claim 27, wherein the length of the first rod is approximately 20 to 50 mm, the length of the second/middle rod is approximately 20 to 50 mm, and/or the length of the third rod is approximately 10 to 30 mm. A nasopharyngeal swab device as claimed in any one of claims 20 to 28, wherein the handle is a hollow sphere configured to be squeezed, wherein upon squeezing, material can be forced out of the swab and upon release, the material can be aspirated into the swab. A sample collection tube comprises a cap and a hollow interior having a brush/bristle disposed therein, wherein the brush/bristle is arranged in a spiral or helical pattern along the length of the interior of the sample collection tube, thereby producing a textured surface.