Method for detecting neuroendocrine cancer in saliva

Abstract

The present invention relates to methods for detecting neuroendocrine cancer in saliva, determining the completeness of surgery, determining whether neuroendocrine cancer is stable or progressing, and assessing response to neuroendocrine cancer treatment.

Description

[Technical Field]

[0001] This application claims priority to and the benefit of U.S. Provisional Application No. 63 / 377,808, filed September 30, 2022, the contents of which are incorporated herein by reference in their entirety.

[0002] Electronic Sequence Listing Reference The contents of the electronic sequence listing (LBIO-007_001WO_SeqList_ST26.xml, size: 207,502 bytes, and creation date: September 27, 2023) are incorporated herein by reference in their entirety. [Background technology]

[0003] Neuroendocrine cancers, also called neuroendocrine neoplasms (NENs) or neuroendocrine tumors (NETs), are tumors that arise from specialized cells in the body's neuroendocrine system. These cells have characteristics of both hormone-producing endocrine cells and neuronal cells. They are found throughout the body, including the gastrointestinal tract, pancreas, and lungs, but can also arise in other parts of the body, such as the adrenal glands (pheochromocytoma), central nervous system (paraganglioma), or pituitary gland. The incidence and prevalence of NETs / NENs has increased by 100 to 600 percent in the United States over the past 30 years, but survival rates have not increased significantly. Symptoms of neuroendocrine cancer include flushed and sweating skin, wheezing, coughing, and difficulty breathing, diarrhea, coughing, sweating, weight gain, pain from cancer that has spread to the bones or other sites, and increased heart rate and significant changes in blood pressure.

[0004] The heterogeneity and complexity of these tumors make diagnosis, treatment, and classification challenging. These neoplasms lack several mutations commonly associated with other cancers and rarely exhibit microsatellite instability. Although individual histopathological subtypes, as determined from tissue sources (e.g., biopsies), can be associated with distinct clinical behaviors, there is no definitive, generally accepted molecular pathological classification and prediction scheme, which hinders diagnosis, staging, treatment evaluation, and follow-up.

[0005] Existing diagnostic and prognostic approaches for tumors include imaging (such as CT or MRI), histology, measurement of circulating hormones and proteins (e.g., chromogranin A), and detection of several gene products. Available methods are limited, for example, by low sensitivity and / or specificity, an inability to detect early-stage disease, and continued exposure to radiation risks associated with imaging protocols. Tumors often go undiagnosed until they have metastasized and are often untreatable. Furthermore, follow-up is difficult, especially if patients have residual disease burden.

[0006] Although molecular genetic information has been used to understand the biology of neuroendocrine cancers, the molecular mechanisms underlying the pathogenesis remain incompletely understood, and there are no molecular-based biomarkers that can be used to predict sensitivity to therapeutic drugs. Therefore, it is important to develop diagnostic methods that can be used to more accurately define the disease state, identify susceptibility to treatment, and ultimately better monitor disease progression.

[0007] Surveillance remains a cornerstone approach for monitoring neuroendocrine cancers and detecting recurrence at an early stage. After potentially curative resection, monitoring can be performed by measuring blood biomarkers and / or imaging such as CT to detect asymptomatic metastatic disease early.

[0008] The current biomarker used for monitoring is chromogranin A (CgA). This marker has low sensitivity and specificity, and other hormonal markers specific to the primary tumor may be used. In any case, detecting residual disease remains difficult, and protocols typically cause great concern for patients and physicians.

[0009] Histological grading is used to identify the timing of imaging, but has also been shown to have low sensitivity and specificity for predicting recurrence.

[0010] Saliva is an important testing compartment that allows for the evaluation of biomarkers for viral, bacterial, and fungal parasitic infections, as well as for the measurement of markers characterizing systemic and nonsystemic diseases. Human RNA obtained from cell-free saliva has been evaluated using sequencing and PCR techniques. Extracellular RNA from healthy individuals contains over 3,000 mRNA species. RNA typically enters the oral cavity from desquamated oral epithelial cells via secretions (from the parotid, submandibular, and sublingual glands) as a component of gingival crevicular fluid. RNA can originate from acinar cells or from the circulation.

[0011] Saliva has been identified as a diagnostic compartment for other cancers, such as head and neck tumors. Typically, viral DNA (HPV) is isolated and amplified, which is used to provide a diagnosis of the disease. Recently, tumor RNA has been detected in saliva. For example, a four-gene RNA-based biomarker for oral cancer diagnosis has been developed. The RNA source may be obtained from the salivary gland itself or secondarily from cells, such as lymphocytes, secreted into the mouth. Salivary glands are vascularized and are known to filter blood products. This suggests that blood may also be a source of detectable RNA in saliva. Summary of the Invention

[0012] The present invention provides a method for identifying the presence or absence of neuroendocrine cancer in a subject in need thereof, comprising: (a) determining expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 36 biomarkers are AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21, and / or .... a) determining the expression of PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, ZXDC, and housekeeping genes, and (b) determining the expression of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OA. The expression levels of Z2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC were normalized to the expression levels of housekeeping genes, AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8 (c) obtaining a normalized expression level of each of D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC; (d) inputting each normalized expression level of step (b) into an algorithm to generate a score; and (d) comparing the score to a predetermined cutoff value.(e) identifying the subject as having neuroendocrine cancer if the score is equal to or greater than a predetermined cutoff value, or identifying the subject as not having neuroendocrine cancer if the score is less than the predetermined cutoff value. In some embodiments, the predetermined cutoff value is 26% on a scale of 0 to 100%.

[0013] The present invention provides a method for determining whether a subject has stable or progressing neuroendocrine cancer, comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject, the at least 36 biomarkers being AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PK (b) determining the expression of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, and AKAP8L, including ... The expression levels of PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC were normalized to the expression levels of housekeeping genes, AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, and GLT8D1. (c) obtaining a normalized expression level of each of KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC; (d) inputting each normalized expression level of step (b) into an algorithm to generate a score; and (d) comparing the score with a predetermined cutoff value.(e) determining that the neuroendocrine cancer is progressing if the score is equal to or greater than a predetermined cutoff value, or determining that the neuroendocrine cancer is stable if the score is less than the predetermined cutoff value. In some embodiments, the predetermined cutoff value is 50% on a scale of 0 to 100%.

[0014] The present invention provides a method for determining the completeness of surgery to remove a neuroendocrine cancer in a subject, comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject after surgery, the 36 biomarkers being AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PL D3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, ZXDC, and housekeeping genes; and (b) determining the expression levels of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PAN, and other genes. The expression levels of each of K2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC were normalized to the expression levels of housekeeping genes, thereby normalizing the expression levels of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, and ARHGEF40. (c) obtaining a normalized expression level of each of KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC; (d) inputting each normalized expression level of step (b) into an algorithm to generate a score; and (d) comparing the score to a predetermined cutoff value.(e) identifying the neuroendocrine cancer as not having been completely eliminated if the score is equal to or greater than a predetermined cutoff value, or identifying the neuroendocrine cancer as having been completely eliminated if the score is less than the predetermined cutoff value. In some embodiments, the predetermined cutoff value is 50% on a scale of 0 to 100%.

[0015] The present invention provides a method for assessing a subject's response to an anti-neuroendocrine cancer therapy in a subject having a neuroendocrine cancer, comprising: (a) determining, at a first time point, (i) the expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 38 biomarkers are: AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21 A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, ZXDC, and housekeeping genes, and (ii) determining AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, P The expression levels of each of ANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC were normalized to the expression levels of housekeeping genes, AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, L (iii) obtaining a normalized expression level of each of EO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC; (ii) inputting the normalized expression level of each of steps (a) and (ii) into an algorithm to generate a first score; and (b) obtaining a normalized expression level of each of EO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC at a second time point, the second time point being after the first time point, and(ii) determining the expression levels of at least 36 biomarkers in a test sample from the subject, including AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, and PK The expression levels of each of D1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC were normalized to the expression levels of housekeeping genes, AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ obtaining normalized expression levels of each of 10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC; and (iii) staging. (b)(ii) into an algorithm to generate a second score; (c) comparing the first score with the second score; and (d) identifying the subject as responsive to the anti-neuroendocrine cancer therapy if the second score is reduced compared to the first score, or identifying the subject as not responsive to the anti-neuroendocrine cancer therapy if the second score is not reduced compared to the first score. In some embodiments, the subject is identified as responsive to the anti-neuroendocrine cancer therapy if the second score is at least 5% lower than the first score.

[0016] In some aspects of the aforementioned methods, the housekeeping gene is selected from the group consisting of ATG4B, RHOA, TOX4, TPT1, and TXNIP.

[0017] In some embodiments of the foregoing methods, the housekeeping gene is RHOA.

[0018] In some embodiments of the aforementioned methods, the methods have a sensitivity of at least 90%.

[0019] In some embodiments of the aforementioned methods, the methods have a specificity of at least 90%.

[0020] In some embodiments of the foregoing methods, at least one of the at least 36 biomarkers is RNA, cDNA, or protein.

[0021] In some embodiments of the foregoing methods, when the biomarker is RNA, the RNA is reverse transcribed to produce cDNA, and the expression level of the produced cDNA is detected.

[0022] In some aspects of the foregoing methods, the expression level of the biomarker is detected by forming a complex between the biomarker and a labeled probe or primer.

[0023] In some embodiments of the foregoing methods, when the biomarker is a protein, the protein is detected by forming a complex between the protein and a labeled antibody. In some embodiments, the label is a fluorescent label.

[0024] In some embodiments of the foregoing methods, when the biomarker is RNA or cDNA, the RNA or cDNA is detected by forming a complex between the RNA or cDNA and a labeled nucleic acid probe or primer. In some embodiments, the label is a fluorescent label. In some embodiments, the complex between the RNA or cDNA and the labeled nucleic acid probe or primer is a hybridization complex.

[0025] In some embodiments of the foregoing methods, the first predetermined cutoff value is obtained from a plurality of reference samples obtained from subjects who do not have or have not been diagnosed with a neoplastic disease. In some embodiments, the neoplastic disease is neuroendocrine cancer.

[0026] In some embodiments of the foregoing method, the algorithm is XGB, RF, glmnet, cforest, CART, treebag, knn, nnet, SVM-radial, SVM-linear, NB, or mlp. In some embodiments, the algorithm is RF, and preferably, the RF algorithm is a grid search optimized random forest.

[0027] In some embodiments of the foregoing methods, the machine learning algorithm is trained using expression levels or normalized expression levels of at least 36 biomarkers from a plurality of reference samples obtained from subjects without neuroendocrine cancer and expression levels or normalized expression levels of at least 36 biomarkers from a plurality of reference samples obtained from subjects with neuroendocrine cancer.

[0028] In some embodiments of the aforementioned methods, the methods further comprise treating the subject identified as having neuroendocrine cancer with at least one anti-neuroendocrine cancer therapy.

[0029] In some aspects of the aforementioned methods, the anti-neuroendocrine cancer therapy comprises active surveillance, surgery, cryotherapy, chemotherapy, targeted therapy, radiation therapy, or any combination thereof.

[0030] In some aspects of the aforementioned methods, the targeted therapy comprises somatostatin analog therapy, everolimus, sunitinib, immunotherapy, or any combination thereof.

[0031] In some aspects of the aforementioned methods, the chemotherapy comprises capecitabine, temozolomide, or any combination thereof.

[0032] In some aspects of the aforementioned methods, the radiation therapy comprises peptide receptor radionuclide therapy (PRRT).

[0033] In some embodiments of the foregoing methods, the first time point is before administration of a treatment to the subject.

[0034] In some embodiments of the foregoing methods, the first time point is after administration of a treatment to the subject.

[0035] In some aspects of the foregoing methods, the test sample is saliva.

[0036] In some aspects of the foregoing methods, the test sample is self-collected saliva in a container containing a stabilizing liquid. [Brief explanation of the drawings]

[0037] [Figure 1] FIG. 1 is a graph showing the relationship between gene expression in blood and saliva.

[0038] [Figure 2A] Figure 2A shows the XY scatter plot graphs showing the agreement between Ct values ​​in blood and saliva (Figure 2A) and normalized gene expression in blood and saliva (Figure 2B). The red line represents the linear correlation. The vertical and horizontal lines represent the SEM and SD of the mean values ​​of the 36 target genes, respectively. [Figure 2B] Figure 2B is an XY scatter plot graph showing the agreement between Ct values ​​in blood and saliva (Figure 2A) and normalized gene expression in blood and saliva (Figure 2B). The red line represents the linear correlation. The vertical and horizontal lines represent the SEM and SD of the mean values ​​of the 36 target genes, respectively.

[0039] [Figure 3] Figure 3 shows the relationship between normalized gene expression in tumor samples and saliva. The red line represents the linear correlation. The vertical and horizontal lines represent the SEM and SD of the mean values ​​of the 36 target genes, respectively.

[0040] [Figure 4] Figure 4 shows gene expression in age- and sex-matched controls (n=30) and neuroendocrine cancer cases (n=15). Expression levels were significantly (p<0.05) elevated for 28 genes and significantly decreased for 8 of the target genes.

[0041] [Figure 5A] Figure 5A shows a graph visualizing 36 putative marker genes identified by the random forest algorithm in a derivation cohort of n=274 control samples and 76 cancer samples. (Figure 5A) Expression normalized to ATG4B. (Figure 5B) Expression normalized to XoRHOA. (Figure 5C) Expression normalized to TXNIP. [Figure 5B] Figure 5B shows a graph visualizing 36 putative marker genes identified by the random forest algorithm in a derivation cohort of n=274 control samples and 76 cancer samples. (Figure 5A) Expression normalized to ATG4B. (Figure 5B) Expression normalized to XoRHOA. (Figure 5C) Expression normalized to TXNIP. [Figure 5C] Figure 5C shows a graph visualizing 36 putative marker genes identified by the random forest algorithm in a derivation cohort of n=274 control samples and 76 cancer samples. (Figure 5A) Expression normalized to ATG4B. (Figure 5B) Expression normalized to XoRHOA. (Figure 5C) Expression normalized to TXNIP.

[0042] [Figure 6] Figure 6 shows the NET saliva scores in an independent set of controls (n=108) and neuroendocrine carcinoma (n=30). NET (57±15) had significantly higher levels compared with controls (15±11) (p<0.0001).

[0043] [Figure 7] Figure 7 shows a graph showing receiver operator curve analysis of the independent test partitions. The AUROC was 0.98. The Youden J index was 0.88. The Z-statistic was highly significant (54.9; p<0.0001).

[0044] [Figure 8] Figure 8 is a graph showing the metrics of the assay for determining neuroendocrine cancer: sensitivity was 100% and specificity was 88%.

[0045] [Figure 9] Figure 9 is a graph showing the effect of surgery on NET saliva scores. Pre-surgery levels were high (66±10%). Surgery reduced levels to 30±12% (p<0.0001), no difference from control levels.

[0046] [Figure 10A] Figure 10A is a spider plot graph showing the effect of treatment on NET saliva scores. Pre-treatment levels were high (63±44%). In patients who responded to treatment, levels decreased by -40±31% and -51±25% at the two follow-up time points (p<0.0001). In patients who progressed despite treatment, levels increased by +7±16% and +32±30%, respectively (p<0.05 for each). (Figure 10A) Spider plot for all patients. (Figure 10B) Spider plots for individual responders (blue) and progressors (red). [Figure 10B]Figure 10B is a spider plot graph showing the effect of treatment on NET saliva scores. Pre-treatment levels were high (63±44%). In patients who responded to treatment, levels decreased by -40±31% and -51±25% at the two follow-up time points (p<0.0001). In patients who progressed despite treatment, levels increased by +7±16% and +32±30%, respectively (p<0.05, respectively). (Figure 10A) Spider plot for all patients. (Figure 10B) Spider plots for individual responders (blue) and progressors (red). DETAILED DESCRIPTION OF THE INVENTION

[0047] Details of the invention are set forth in the accompanying description below. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, exemplary methods and materials are described herein. Other features, objects, and advantages of the present invention will become apparent from the description and claims. In this specification and the appended claims, the singular forms include the plural forms unless the context clearly dictates otherwise. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. All patents and publications cited herein are incorporated by reference in their entirety.

[0048] Described herein are methods for quantifying (scoring) the molecular signature of salivary neuroendocrine carcinoma with high sensitivity and specificity for purposes including, but not limited to, detecting NETs / NENs, determining whether NETs / NENs are stable or progressing, determining the completeness of surgery, assessing a subject's response to neuroendocrine cancer treatment, treating NETs / NENs in a subject, or combinations thereof. Without wishing to be bound by theory, the present invention is based on the discovery that the expression levels of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC, normalized by the expression levels of housekeeping genes, are higher in subjects with neuroendocrine cancer compared to healthy subjects.

[0049] As described herein, measuring the expression levels of the above-mentioned circulating neuroendocrine cancer transcripts (collectively referred to as "NET salivary transcripts") in a subject's saliva sample can be used to diagnose neuroendocrine cancer. In a non-limiting example, the expression levels of NET salivary transcripts measured from a subject's saliva sample can be input into an algorithm to generate a score (referred to herein as a "NET salivary score"), which can be used to diagnose the presence of NETs / NENs in the subject. Furthermore, a decrease in a subject's NET salivary score after administration of one or more anti-neuroendocrine cancer treatments (e.g., surgery and chemotherapy), optionally in combination with standard clinical assessment and imaging, can be used to determine the subject's response to one or more treatments.

[0050] Accordingly, the present disclosure provides a method for identifying the presence or absence of neuroendocrine cancer in a subject in need thereof, comprising: (a) determining expression levels of at least 36 biomarkers in a test sample from the subject, the at least 36 biomarkers being AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK 2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, ZXDC, and housekeeping genes, and (b) determining AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, and ... The expression level of each of NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC was normalized to the expression level of housekeeping genes, thereby determining the expression levels of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, and ARHGEF41A. and (c) identifying the presence or absence of neuroendocrine cancer in the subject based on the normalized expression levels of FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC.In some aspects, identifying the presence or absence of neuroendocrine cancer in the subject based on the normalized expression level of step (b) can include comparing the normalized expression level to a corresponding predetermined cutoff value, and identifying the presence or absence of neuroendocrine cancer in the subject based on a relationship (e.g., greater than, greater than or equal to, less than, less than or equal to, or equal to) between the normalized expression level and the corresponding predetermined cutoff value.

[0051] The present invention provides a method for identifying the presence or absence of neuroendocrine cancer in a subject in need thereof, comprising: (a) determining expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 36 biomarkers are AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, (b) determining the expression of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF, and other genes, including PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, ZXDC, and housekeeping genes. The expression levels of each of 21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC were normalized to the expression levels of housekeeping genes, AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF (c) obtaining a normalized expression level of each of 4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC; (d) inputting each normalized expression level of step (b) into an algorithm to generate a score; and (d) identifying the presence or absence of neuroendocrine cancer in the subject based on the score.In some embodiments, identifying the presence or absence of neuroendocrine cancer in the subject based on the score can include comparing the score to a predetermined cutoff value and identifying the presence or absence of neuroendocrine cancer in the subject based on a relationship between the score and the predetermined cutoff value (e.g., greater than, greater than or equal to, less than, less than or equal to, or equal to).

[0052] Accordingly, the present disclosure provides a method for identifying the presence or absence of neuroendocrine cancer in a subject in need thereof, comprising: (a) determining expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 36 biomarkers are AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PH (b) determining the expression of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, and FZD7 / GLT8D1, including F21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, ZXDC, and housekeeping genes; The expression levels of each of OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC were normalized to the expression levels of housekeeping genes, AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT (c) obtaining a normalized expression level of each of 8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC; (d) inputting each normalized expression level of step (b) into an algorithm to generate a score; and (e) comparing the score with a predetermined cutoff value.(e) identifying the subject as having neuroendocrine cancer if the score is equal to or greater than the predetermined cutoff value, or identifying the subject as not having neuroendocrine cancer if the score is less than the predetermined cutoff value.

[0053] Accordingly, the present disclosure provides a method for identifying the presence or absence of neuroendocrine cancer in a subject in need thereof, the method comprising: (a) determining expression levels of at least 36 biomarkers in a test sample from the subject; and wherein the at least 36 biomarkers are selected from the group consisting of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, and PHF. (b) determining the expression of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, O2, and PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, ZXDC, and housekeeping genes; The expression levels of AZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC were normalized to the expression levels of housekeeping genes, AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, and GLT8. (c) obtaining a normalized expression level of each of D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC; (d) inputting each normalized expression level of step (b) into an algorithm to generate a score; and (d) comparing the score to a predetermined cutoff value.(e) determining that the subject has neuroendocrine cancer if the score is greater than the predetermined cutoff value, or determining that the subject does not have neuroendocrine cancer if the score is equal to or less than the predetermined cutoff value.

[0054] In some embodiments of the foregoing methods, the predetermined cutoff value may be 26% on a scale of 0 to 100%.

[0055] Accordingly, the present disclosure provides a method for identifying a subject's risk of having neuroendocrine cancer, comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 36 biomarkers are: AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1 (b) determining the expression of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ, and other genes, including PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, ZXDC, and housekeeping genes. The expression levels of each of PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC were normalized to the expression levels of housekeeping genes, thereby determining the expression levels of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, and ARHGEF40. and (c) identifying a risk of the subject having neuroendocrine cancer based on the normalized expression levels of GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC.In some embodiments, identifying the risk of the subject having neuroendocrine cancer based on the normalized expression level of step (b) can include comparing the normalized expression level to a corresponding predetermined cutoff value and identifying the risk of the subject having neuroendocrine cancer based on the relationship (e.g., greater than, greater than or equal to, less than, less than or equal to, or equal to) between the normalized expression level and the corresponding predetermined cutoff value.

[0056] The present invention provides a method for identifying a subject's risk of having neuroendocrine cancer, comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 36 biomarkers are AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, P (b) determining the expression of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, and / or AKAP8L, including QBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, ZXDC, and housekeeping genes; and The expression levels of each of PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC were normalized to the expression levels of housekeeping genes, thereby normalizing the expression levels of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NU (c) obtaining a normalized expression level of each of DT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC; (d) inputting each normalized expression level of step (b) into an algorithm to generate a score; and (d) identifying a risk of the subject having neuroendocrine cancer based on the score.In some embodiments, identifying the risk of the subject having neuroendocrine cancer based on the score can include comparing the score to a predetermined cutoff value and identifying the risk of the subject having neuroendocrine cancer based on the relationship between the score and the predetermined cutoff value (e.g., greater than, greater than or equal to, less than, less than or equal to, or equal to).

[0057] Accordingly, the present disclosure provides a method for identifying a subject's risk of having neuroendocrine cancer, comprising: (a) determining expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 36 biomarkers are: AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PL (b) determining the expression of genes including AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PAN, and AKAP8L, and (c) determining the expression of genes including AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PAN, and AKAP8L. The expression levels of each of K2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC were normalized to the expression levels of housekeeping genes, thereby normalizing the expression levels of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, and ARHGEF40. (c) obtaining a normalized expression level of each of KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC; (d) inputting each normalized expression level of step (b) into an algorithm to generate a score; and (d) comparing the score to a predetermined cutoff value.(e) identifying the subject as being at high risk of having neuroendocrine cancer if the score is equal to or greater than a predetermined cutoff value, or determining that the subject is at low risk of having neuroendocrine cancer if the score is less than a predetermined cutoff value.

[0058] Accordingly, the present disclosure provides a method for identifying a subject's risk of having neuroendocrine cancer, comprising: (a) determining expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 36 biomarkers are: AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PL (b) determining the expression of genes including AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PAN, and AKAP8L, and (c) determining the expression of genes including AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PAN, and AKAP8L. The expression levels of each of K2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC were normalized to the expression levels of housekeeping genes, thereby normalizing the expression levels of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, and ARHGEF40. (c) obtaining a normalized expression level of each of KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC; (d) inputting each normalized expression level of step (b) into an algorithm to generate a score; and (d) comparing the score to a predetermined cutoff value.(e) identifying the subject as being at high risk of having neuroendocrine cancer if the score is greater than a predetermined cutoff value, or determining that the subject is at low risk of having neuroendocrine cancer if the score is equal to or less than a predetermined cutoff value.

[0059] In some embodiments of the foregoing methods, the predetermined cutoff value may be 26% on a scale of 0 to 100%.

[0060] The present disclosure provides a method for determining whether a subject has stable or progressing neuroendocrine cancer, comprising: (a) determining expression levels of at least 36 biomarkers in a test sample from the subject, the at least 36 biomarkers being AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PK (b) determining the expression of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, P The expression levels of each of ANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC were normalized to the expression levels of housekeeping genes, AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, K (c) obtaining a normalized expression level of each of RAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC; and (c) determining whether the neuroendocrine cancer in the subject is stable or progressing based on the normalized expression levels of step (b).In some aspects, determining whether the neuroendocrine cancer in the subject is stable or progressing based on the normalized expression level of step (b) comprises comparing the normalized expression level to a corresponding predetermined cutoff value and determining whether the neuroendocrine cancer in the subject is stable or progressing based on the relationship (e.g., greater than, greater than or equal to, less than, less than or equal to, or equal to) between the normalized expression level and the corresponding predetermined cutoff value.

[0061] The present invention provides a method for determining whether a subject has stable or progressing neuroendocrine cancer, comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject, the at least 36 biomarkers being AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, and PLD3. , PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, ZXDC, and housekeeping genes, and (b) determining the expression of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, P The expression levels of each of KD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC were normalized to the expression levels of housekeeping genes, thereby normalizing the expression levels of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NU (c) obtaining a normalized expression level of each of DT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC; (d) inputting each normalized expression level of step (b) into an algorithm to generate a score; and (d) determining whether the neuroendocrine cancer in the subject is stable or progressing based on the score.In some embodiments, determining whether the neuroendocrine cancer in the subject is stable or progressing based on the score includes comparing the score to a corresponding predetermined cutoff value and determining whether the neuroendocrine cancer in the subject is stable or progressing based on the relationship between the score and the cutoff value (e.g., greater than, greater than or equal to, less than, less than or equal to, or equal to).

[0062] Accordingly, the present disclosure provides a method for determining whether a neuroendocrine cancer in a subject is stable or progressing, comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject, the at least 36 biomarkers being AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, and the like. (b) determining the expression of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ, and other genes, including PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, ZXDC, and housekeeping genes. The expression levels of each of the following genes were normalized to those of the housekeeping genes: AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D, IFN-γ ... (c) obtaining a normalized expression level of each of KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC; (d) inputting each normalized expression level of step (b) into an algorithm to generate a score; and (d) comparing the score with a predetermined cutoff value.(e) determining that the neuroendocrine cancer is progressing if the score is equal to or greater than a predetermined cutoff value, or determining that the neuroendocrine cancer is stable if the score is less than a predetermined cutoff value.

[0063] Accordingly, the present disclosure provides a method for determining whether a neuroendocrine cancer in a subject is stable or progressing, comprising: (a) determining expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 36 biomarkers are: AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF2; (b) determining the expression of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OA, and other genes, including AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2 ... The expression levels of Z2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC were normalized to the expression levels of housekeeping genes, AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8 (c) obtaining a normalized expression level of each of D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC; (d) inputting each normalized expression level of step (b) into an algorithm to generate a score; and (d) comparing the score to a predetermined cutoff value.(e) determining that the neuroendocrine cancer is progressing if the score is greater than a predetermined cutoff value, or determining that the neuroendocrine cancer is stable if the score is equal to or less than a predetermined cutoff value.

[0064] In some embodiments of the foregoing methods, the predetermined cutoff value may be 50% on a scale of 0 to 100%.

[0065] Further, the disclosure provides a method for determining the completeness of surgery to remove a neuroendocrine cancer in a subject, comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject after surgery, wherein the 36 biomarkers are AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, (b) determining the expression of genes including PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, ZXDC, and housekeeping genes; and (c) determining the expression of genes including AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF The expression levels of each of 21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC were normalized to the expression levels of housekeeping genes, thereby determining the expression levels of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, M and (c) identifying the neuroendocrine cancer as not having been completely eliminated or identifying the neuroendocrine cancer as having been completely eliminated based on the normalized expression levels of step (b).In some aspects, identifying the neuroendocrine cancer as not being completely eliminated or identifying the neuroendocrine cancer as being completely eliminated based on the normalized expression level in step (b) can include comparing the normalized expression level to a corresponding predetermined cutoff value and identifying the neuroendocrine cancer as not being completely eliminated or identifying the neuroendocrine cancer as being completely eliminated based on a relationship (e.g., greater than, greater than or equal to, less than, less than or equal to, or equal) between the normalized expression level and the corresponding predetermined cutoff value.

[0066] Accordingly, the present disclosure provides a method for determining the completeness of surgery to remove a neuroendocrine cancer in a subject, comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject after surgery, wherein the 36 biomarkers are AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, P (b) determining the expression of genes including LD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, ZXDC, and housekeeping genes; and (c) determining the expression of genes including AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK. The expression levels of each of the genes PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC were normalized to the expression levels of housekeeping genes AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, L (c) obtaining a normalized expression level of each of EO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC; (d) inputting each normalized expression level of step (b) into an algorithm to generate a score; and (d) identifying the neuroendocrine cancer as not completely eliminated based on the score;or identifying the neuroendocrine cancer as having been completely removed. In some embodiments, identifying the neuroendocrine cancer as having not been completely removed or as having been completely removed based on the score can include comparing the score to a corresponding predetermined cutoff value, and identifying the neuroendocrine cancer as having not been completely removed or as having been completely removed based on the relationship between the score and the predetermined cutoff value (e.g., greater than, greater than or equal to, less than, less than or equal to, or equal).

[0067] Accordingly, the present disclosure provides a method for determining the completeness of surgery to remove a neuroendocrine cancer in a subject, comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject after surgery, the 36 biomarkers being AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD (b) determining the expression of genes including AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, and AKAP8L, and (c) determining the expression of genes including AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, and AKAP8L, and (d) determining the expression of genes including AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, and AKAP8L. The expression levels of each of the following genes were normalized to those of the housekeeping genes: AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC. (c) obtaining a normalized expression level of each of KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC; (d) inputting each normalized expression level of step (b) into an algorithm to generate a score; and (d) comparing the score with a predetermined cutoff value.(e) identifying the neuroendocrine cancer as not having been completely removed if the score is equal to or greater than the predetermined cutoff value, or identifying the neuroendocrine cancer as having been completely removed if the score is less than the predetermined cutoff value.

[0068] Accordingly, the present disclosure provides a method for determining the completeness of surgery to remove a neuroendocrine cancer in a subject, comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject after surgery, the 36 biomarkers being AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, P (b) determining the expression of genes including KD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, ZXDC, and housekeeping genes; and (c) determining the expression of genes including AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OA. The expression levels of Z2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC were normalized to the expression levels of housekeeping genes, AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8 (c) obtaining a normalized expression level of each of D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC; (d) inputting each normalized expression level of step (b) into an algorithm to generate a score; and (d) comparing the score to a predetermined cutoff value.(e) identifying the neuroendocrine cancer as not having been completely removed if the score is greater than a predetermined cutoff value, or identifying the neuroendocrine cancer as having been completely removed if the score is equal to or less than a predetermined cutoff value.

[0069] In some embodiments of the foregoing methods, the predetermined cutoff value may be 50% on a scale of 0 to 100%.

[0070] The response of a patient with neuroendocrine cancer to treatment can also be evaluated by comparing the scores determined by the same algorithm at different time points during treatment. For example, the first time point can be before or after administering treatment to the subject, and the second time point is after the first time point and after administering treatment to the subject. The first score is generated at the first time point, and the second score is generated at the second time point. If the second score is reduced compared to the first score, the subject is considered to be responding to treatment. In some embodiments, the second score is reduced compared to the first score if the second score is at least 5% lower than the first score, for example, at least 10% lower than the first score, at least 15% lower than the first score, at least 25% lower than the first score, at least 40% lower than the first score, at least 50% lower than the first score, at least 75% lower than the first score, or at least 90% lower than the first score. If the second score is not significantly decreased or is increased compared to the first score, the subject is considered to be not responding to the treatment.

[0071] The disclosure also provides a method of assessing the response of a subject having a neuroendocrine cancer to an anti-neuroendocrine cancer therapy, comprising: (a) determining, at a first time point, (i) the expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 38 biomarkers are: AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, P (ii) determining the expression of genes including ANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, ZXDC, and housekeeping genes; and (iii) determining the expression of genes including AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, The expression levels of each of MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC were normalized to the expression levels of housekeeping genes, AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357, and FLJ10357. / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC; and (b) at a second time point (the second time point is after the first time point and after administration of an anti-neuroendocrine therapy to the subject),(i) determining the expression levels of at least 36 biomarkers in a test sample from a subject; and (ii) determining the expression levels of at least 36 biomarkers, including AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, and RAF. The expression levels of each of RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC were normalized to the expression levels of housekeeping genes, AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, and GLT8 (c) obtaining a normalized expression level of each of D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC; (c) comparing the normalized expression levels of step (a)(ii) and step (b)(ii); and (d) identifying the subject as responsive to the anti-neuroendocrine cancer therapy if the normalized expression level of step (b)(ii) is decreased compared to the expression level of step (a)(ii), or identifying the subject as not responsive to the anti-neuroendocrine cancer therapy if the normalized expression level of step (b)(ii) is not decreased compared to the normalized expression level of step (a)(ii).

[0072] The present disclosure also provides a method of assessing the response of a subject having a neuroendocrine cancer to an anti-neuroendocrine cancer therapy, comprising: (a) determining, at a first time point, (i) the expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 38 biomarkers are AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF2, and / or .... (ii) determining the expression of genes including AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ, and PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, ZXDC, and housekeeping genes; and The expression levels of each of the following genes were normalized to those of the housekeeping genes: AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, IFN-γ ... (iii) obtaining a normalized expression level of each of LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC; (ii) inputting the normalized expression level of each of steps (a) and (ii) into an algorithm to generate a first score; and (b) obtaining a normalized expression level of each of LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC at a second time point, the second time point being after the first time point, and(ii) determining the expression levels of at least 36 biomarkers in a test sample from the subject, including AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, and PK The expression levels of each of D1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC were normalized to the expression levels of housekeeping genes, thereby AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FL obtaining normalized expression levels of each of J10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC; and (iii) The method includes inputting each normalized expression level of steps (b)(ii) into an algorithm to generate a second score; (c) comparing the first score with the second score; and (d) identifying the subject as responsive to the anti-neuroendocrine cancer therapy if the second score is reduced compared to the first score, or identifying the subject as not responsive to the anti-neuroendocrine cancer therapy if the second score is not reduced compared to the first score.

[0073] General Methods and Definitions

[0074] The following general methods and definitions are applicable to any of the above methods.

[0075] In some embodiments, the test sample can include saliva.

[0076] Exemplary housekeeping genes include, but are not limited to, ATG4B, RHOA, TOX4, TPT1, and TXNIP. In some embodiments, the housekeeping gene is RHOA.

[0077] Each of the biomarkers disclosed herein may have one or more transcript variants, and the methods disclosed herein can measure the expression level of any one of the transcript variants of each biomarker.

[0078] In some embodiments, determining the expression levels of the at least 36 biomarkers in the subject's test sample can include contacting the test sample with a plurality of agents specific for detecting expression of the at least 36 biomarkers.

[0079] Thus, the present invention provides the use of a plurality of agents for detecting the expression of at least 36 biomarkers in the manufacture of a kit for identifying the presence or absence of neuroendocrine cancer by the methods described herein.

[0080] The present invention also provides the use of a plurality of agents for detecting the expression of at least 36 biomarkers in the manufacture of a kit for identifying the risk that a subject has neuroendocrine cancer by the methods described herein.

[0081] The present invention also provides the use of a plurality of agents for detecting expression of at least 36 biomarkers in the manufacture of a kit for determining whether a neuroendocrine cancer in a subject is stable or progressing by the methods described herein.

[0082] The present invention also provides the use of multiple agents for detecting the expression of at least 36 biomarkers in the manufacture of a kit for determining the completeness of surgery to remove a neuroendocrine cancer in a subject by the methods described herein.

[0083] The present invention also provides the use of a plurality of agents for detecting the expression of at least 36 biomarkers in the manufacture of a kit for assessing the response of a subject with neuroendocrine cancer to anti-neuroendocrine cancer therapy by the methods described herein.

[0084] The expression level can be measured in a variety of ways, including, but not limited to, measuring the mRNA encoded by the selected gene, measuring the amount of the protein encoded by the selected gene, measuring the activity of the protein encoded by the selected gene, or any combination thereof.

[0085] Biomarkers can be RNA, cDNA, or proteins. If the biomarker is RNA, the RNA is reverse transcribed to generate cDNA (such as by RT-PCR), and the expression level of the generated cDNA is detected. The expression level of the biomarker can be detected by forming a complex between the biomarker and a labeled probe or primer. If the biomarker is RNA or cDNA, the RNA or cDNA is detected by forming a complex between the RNA or cDNA and a labeled nucleic acid probe or primer. The complex between the RNA or cDNA and the labeled nucleic acid probe or primer can be a hybridization complex.

[0086] As will be understood by those skilled in the art, gene expression can be detected by microarray analysis. Differential gene expression can also be identified or confirmed using microarray technology. Thus, expression profile biomarkers can be measured in either fresh or fixed tissue using microarray technology. In this method, the polynucleotide sequences of interest (including cDNA and oligonucleotides) are arranged or arrayed on a microchip substrate. The arrayed sequences are then hybridized with specific DNA probes from cells or tissues of interest. The source of mRNA is typically total RNA isolated from biological samples, and differential expression can be determined using corresponding normal tissues or cell lines.

[0087] In some embodiments of microarray technology, PCR-amplified cDNA clone inserts are applied to a substrate in a high-density array. In some embodiments, at least 10,000 nucleotide sequences are applied to the substrate. Microarrayed genes, each 10,000 elements long, are suitable for hybridization under stringent conditions. Fluorescently labeled cDNA probes may be generated by incorporating fluorescent nucleotides through reverse transcription of RNA extracted from tissues of interest. Labeled cDNA probes applied to the chip specifically hybridize to each DNA spot on the array. After stringent washing to remove nonspecifically bound probes, the microarray chip is scanned by a device such as a confocal laser microscope or another detection method such as a CCD camera. Quantifying hybridization of each arrayed element allows assessment of the abundance of the corresponding mRNA. In dual-color fluorescence, separately labeled cDNA probes generated from two RNA sources are hybridized pairwise to the array. Thus, the relative abundance of transcripts from the two sources corresponding to each specified gene is determined simultaneously. Microarray analysis can be performed on commercially available instruments according to the manufacturer's protocols.

[0088] In some embodiments, biomarkers (i.e., NET saliva transcripts and / or housekeeping genes) can be detected in saliva samples using RNA sequencing. As will be understood by those skilled in the art, the first step in gene expression profiling by RNA sequencing is to extract RNA from saliva samples, followed by reverse transcription of the RNA template into cDNA to generate an RNA library. Sequencing adapters are added. The cDNA is then sequenced using a sequencing platform. The data is analyzed and expressed as transcripts per million.

[0089] In some embodiments, biomarkers (i.e., NET salivary transcripts and / or housekeeping genes) can be detected in saliva samples using qRT-PCR. As will be understood by those skilled in the art, the first step in gene expression profiling by RT-PCR is to extract RNA from a biological sample, followed by reverse transcription of the RNA template into cDNA and amplification via a PCR reaction. The reverse transcription step is generally primed using specific primers, random hexamers, or oligo-dT primers, depending on the purpose of expression profiling. Two commonly used reverse transcriptases are avian myeloblastosis virus reverse transcriptase (AMV-RT) and Moloney murine leukemia virus reverse transcriptase (MLV-RT).

[0090] In some embodiments where the biomarker is a protein, the protein can be detected by forming a complex between the protein and a labeled antibody. The label can be any label, such as a fluorescent label, a chemiluminescent label, or a radioactive label. Exemplary protein detection methods include, but are not limited to, enzyme immunoassay (EIA), radioimmunoassay (RIA), Western blot analysis, and enzyme-linked immunosorbent assay (ELISA). For example, biomarkers can be detected by ELISA, in which a biomarker antibody is bound to a solid phase and an enzyme-antibody conjugate is used to detect and / or quantify the biomarker present in a sample. Alternatively, a Western blot assay can be used, in which solubilized and separated biomarkers are bound to nitrocellulose paper. The combination of a highly specific and stable liquid conjugate with a sensitive chromogenic substrate allows for rapid and accurate identification of samples.

[0091] In some embodiments, the methods described herein can have a specificity, sensitivity, and / or accuracy of at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

[0092] In some aspects, the methods described herein can have a specificity of at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% (e.g., specificity for identifying the presence or absence of neuroendocrine cancer, specificity for identifying whether neuroendocrine cancer is stable or progressing, specificity for identifying the completeness of surgery in a subject with neuroendocrine cancer, or specificity for assessing the response of a subject with neuroendocrine cancer to an anti-neuroendocrine cancer therapy).

[0093] In some aspects, the methods described herein can have a sensitivity (e.g., sensitivity for identifying the presence or absence of neuroendocrine cancer, sensitivity for identifying whether neuroendocrine cancer is stable or progressing, sensitivity for identifying the completeness of surgery in a subject with neuroendocrine cancer, or sensitivity for assessing the response of a subject with neuroendocrine cancer to an anti-neuroendocrine cancer therapy) of at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

[0094] In some aspects, the methods described herein can have an accuracy (e.g., accuracy of identifying the presence or absence of neuroendocrine cancer, accuracy of identifying whether neuroendocrine cancer is stable or progressing, accuracy of identifying the completeness of surgery in a subject with neuroendocrine cancer, or accuracy of assessing the response of a subject with neuroendocrine cancer to an anti-neuroendocrine cancer therapy) of at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

[0095] Any algorithm that can generate a score for a sample by evaluating where the sample value falls within a generated predictive model using different techniques, such as decision trees, can be used in the methods disclosed herein. The algorithm analyzes the data (i.e., expression levels) and then assigns a score. In some aspects, the algorithm can be a machine learning algorithm. Exemplary algorithms that can be used in the methods disclosed herein include, but are not limited to, XGB, random forest (RF), glmnet, cforest, CART, treebag, knn, nnet, SVM-radial, SVM-linear, NB, and mlp. In some aspects, the algorithm can be random forest. In some aspects, the algorithm can be XGB (also known as XGBoost). XGB is an implementation of gradient boosted decision trees designed for speed and performance. In some aspects, the algorithm can be random forest. In some aspects, the random forest algorithm can be a grid search optimized random forest algorithm. Random forest is an implementation of ensemble learning methods for classification, regression, and other tasks that operate by building multiple decision trees during training.

[0096] In some embodiments of the methods of the present disclosure, a machine learning algorithm can be trained using a) expression levels or normalized expression levels of at least 36 biomarkers in at least one biological sample (e.g., saliva) from at least one subject without neuroendocrine cancer, and b) expression levels or normalized expression levels of at least 36 biomarkers in at least one biological sample (e.g., saliva) from at least one subject with neuroendocrine cancer. That is, in some embodiments, the machine learning algorithm is trained using expression levels or normalized expression levels of at least 36 biomarkers obtained from multiple reference samples obtained from subjects without neuroendocrine cancer, and expression levels or normalized expression levels of at least 36 biomarkers from multiple reference samples obtained from subjects with neuroendocrine cancer.

[0097] In some embodiments, one or more predetermined cutoff values ​​can be derived from a plurality of reference samples obtained from subjects who do not have or have not been diagnosed with a neoplastic disease. The plurality of reference samples can be about 2 to about 500 samples, about 2 to about 200 samples, about 10 to about 100 samples, or about 20 to about 80 samples.

[0098] In some embodiments, determining the predetermined cutoff value can include inputting the normalized expression level of NET saliva transcripts from each reference sample into the same algorithm as used in the above-mentioned method, thereby generating multiple scores from multiple reference samples.Then, by taking the arithmetic mean of these scores, the predetermined cutoff value can be determined.In some embodiments, the reference sample can comprise saliva.In some embodiments, the reference sample is the same type as the test sample.

[0099] In some embodiments of the methods of the present disclosure, at least one receiver operating characteristic (ROC) curve can be used to calculate and / or select the predetermined cutoff value. In some embodiments of the methods of the present disclosure, as will be understood by one of skill in the art, any method known in the art can be used to calculate and / or select the predetermined cutoff value to have any of the characteristics described herein (e.g., a particular sensitivity, specificity, accuracy, or any combination thereof).

[0100] In some aspects, the methods described herein can further include treating the subject with an anti-neuroendocrine cancer therapy.

[0101] Thus, in some embodiments, the methods described herein further comprise treating a subject identified as having a neuroendocrine cancer with an anti-neuroendocrine cancer therapy. In some embodiments, the methods described herein further comprise treating a subject identified as having an advanced neuroendocrine cancer with at least one anti-neuroendocrine cancer therapy. In some embodiments, the methods described herein further comprise treating a subject identified as having a high-risk neuroendocrine cancer with at least one anti-neuroendocrine cancer therapy. In some embodiments, the methods described herein further comprise treating a subject whose neuroendocrine cancer has not been completely removed by surgery with at least one anti-neuroendocrine cancer therapy.

[0102] In some embodiments, the described methods further include treating subjects identified as non-responsive to an anti-neuroendocrine cancer therapy with a different anti-neuroendocrine cancer therapy. In some embodiments, the described methods further include continuing to treat subjects identified as responsive to an anti-neuroendocrine cancer therapy with the same anti-neuroendocrine cancer therapy.

[0103] In some aspects, the anti-neuroendocrine cancer therapy can include active surveillance, surgery, cryotherapy, chemotherapy, targeted therapy, radiation therapy, or any combination thereof. The anti-neuroendocrine cancer therapy can include any therapeutic known in the art to be effective in treating neuroendocrine cancer.

[0104] As will be appreciated by those skilled in the art, active surveillance can include a doctor's visit with chromogranin A blood testing and imaging scans approximately every six months. Active surveillance can also occur every two years. 68 Imaging may include Ga-PET-SSA-CT scanning.

[0105] As will be appreciated by those skilled in the art, surgery for neuroendocrine cancer patients can include complete resection (R0 "curative" surgery).

[0106] As will be understood by those skilled in the art, cryotherapy (also called cryosurgery or cryoablation) uses very low temperatures to freeze and kill neuroendocrine cancer cells, typically in the liver.

[0107] As will be appreciated by those skilled in the art, chemotherapy can include streptozotocin, doxorubicin, 5-FU, dacarbazine, temozolomide, capecitabine, and oxaliplatin, or any combination thereof.

[0108] As will be appreciated by one of skill in the art, targeted therapies can include somatostatin analogs, everolimus, sunibinib, and immunotherapy, or any combination thereof.

[0109] As will be appreciated by one of skill in the art, radiation therapy can include peptide receptor radionuclide therapy (PRRT).

[0110] As will be appreciated by those skilled in the art, when neuroendocrine cancer grows outside the primary tumor site, the primary goal of treatment is to prevent or slow the spread of cancer to the liver or bone. Treatments directed at the liver and / or bone include the use of radiation therapy or radiopharmaceuticals (e.g., indium-111, lutetium-177).

[0111] Sequence information for neuroendocrine cancer biomarkers and housekeeping genes is provided in Table 1. Table 1 provides representative sequences for each of the neuroendocrine cancer biomarkers and housekeeping genes discussed herein. One skilled in the art will understand that in addition to the specific sequences provided in Table 1, other isoforms and variants of variants can be measured to obtain expression levels of biomarkers or housekeeping genes in the methods of the present invention.

[0112] [Table 1-1] [Table 1-2]

[0113] In this disclosure, the articles "a" and "an" refer to one or to more than one (i.e., to at least one) of the grammatical object of the article. For example, "an element" means one element or more than one element.

[0114] In this disclosure, the term "and / or" is used to mean either "and" or "or," unless expressly stated otherwise.

[0115] As used herein, the terms "polynucleotide" and "nucleic acid molecule" are used interchangeably to refer to a polymeric form of nucleotides, either ribonucleotides or deoxynucleotides, or modified forms of either type of nucleotide, of at least 10 bases or base pairs in length, and are meant to include single-stranded and double-stranded forms of DNA. As used herein, nucleic acid molecules or nucleic acid sequences that function as probes in microarray analysis preferably comprise strands of nucleotides, more preferably DNA and / or RNA strands. In some embodiments, nucleic acid molecules or nucleic acid sequences include other types of nucleic acid structures, including, but not limited to, DNA / RNA helices, peptide nucleic acids (PNAs), locked nucleic acids (LNAs), and / or ribozymes. Thus, as used herein, the term "nucleic acid molecule" also includes strands comprising non-natural nucleotides, modified nucleotides, and / or non-nucleotide building blocks that exhibit the same function as natural nucleotides.

[0116] As used herein, the terms "hybridize," "hybridizing," "hybridizes," and the like, when used in the context of polynucleotides, are meant to refer to hybridization under conventional hybridization conditions, e.g., 50% formamide / 6X SSC / 0.1% SDS / 100 μg / ml ssDNA (where the hybridization temperature is 37°C or higher and the wash temperature in 0.1X SSC / 0.1% SDS is 55°C or higher), preferably stringent hybridization conditions.

[0117] As used herein, the term "normalization" or "normalizer" refers to expressing a differential value relative to a standard value to adjust for effects resulting from technical variations due to sample handling, sample preparation, and measurement methodology, rather than biological variations in biomarker concentration in a sample. For example, when measuring the expression of a differentially expressed protein, the absolute value of the protein's expression can be expressed as the absolute value of the expression of a standard protein whose expression is substantially constant.

[0118] The terms "diagnosis" and "diagnostics" also include the terms "prognosis" and "prognostics," respectively, as well as the application of such procedures across two or more time points to monitor diagnosis and / or prognosis over time and statistical modeling thereon. Furthermore, the term diagnosis includes a. prediction (determining whether a patient is likely to develop malignant disease (hyperproliferative / invasive)), b. prognosis (predicting whether a patient will have a good or bad outcome at a preselected time point in the future), c. therapy selection, d. therapeutic drug monitoring, and e. recurrence monitoring.

[0119] "Accuracy" refers to the closeness of agreement of a measured or calculated quantity (the value reported by a test) with its actual value (or true value). Clinical accuracy is related to the proportion of true results (true positive (TP) or true negative (TN)) and misclassified results (false positive (FP) or false negative (FN)) and may be expressed as sensitivity, specificity, positive predictive value (PPV) or negative predictive value (NPV), or likelihood, odds ratio, or other measure.

[0120] As used herein, the term "biological sample" refers to any sample of biological origin that may contain one or more biomarkers. Examples of biological samples include bodily fluids such as saliva or lavage, or any other specimen used in the detection of disease.

[0121] The term "subject" as used herein refers to a mammal, preferably a human. In some embodiments, the subject has at least one symptom of neuroendocrine cancer. In some embodiments, the subject has a predisposition or family history to developing neuroendocrine cancer. The subject may have previously been diagnosed with neuroendocrine cancer and is being tested for cancer recurrence.

[0122] "Treating" or treatment of a disease or condition refers to carrying out a protocol or treatment plan that may include administering one or more therapeutic agents to a patient in an attempt to alleviate the signs or symptoms of the disease, or the recurrence of the disease. Desirable effects of treatment include slowing the rate of disease progression, improvement or mitigation of disease symptoms, and remission, increased survival, improved quality of life, or improved prognosis. Furthermore, "treating" or "treatment" specifically includes protocols or treatment plans that provide only a marginal benefit to the patient, but do not require complete alleviation of signs or symptoms, and a cure is not required.

[0123] As used herein, "prevent," "preventing," and the like refer to halting the onset of a disease, condition, disorder, or one or more symptoms or complications thereof.

[0124] The level of a biomarker may change due to treatment of a disease. The change in biomarker level may be measured according to the present disclosure. The change in biomarker level may be used to monitor disease progression or therapy.

[0125] "Altered," "changed," or "significantly different" refers to a detectable change or difference from reasonably comparable states, profiles, measurements, etc. Such changes may be total or none at all. They may be incremental and need not be linear. They may be orders of magnitude. Changes may be an increase or decrease of 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, 100%, or more, or any value between 0% and 100%. Alternatively, the change may be 1-fold, 1.5-fold, 2-fold, 3-fold, 4-fold, 5-fold, or more, or any value between 1-fold and 5-fold. The change may be statistically significant at a p-value of 0.1, 0.05, 0.001, or 0.0001.

[0126] The term "stable disease" refers to a diagnosis of the presence of neuroendocrine cancer, but the neuroendocrine cancer has been treated and remains stable, i.e., not progressing, as determined by imaging data and / or best clinical decision making.

[0127] The term "progressive disease" refers to a diagnosis of the presence of a highly aggressive state of neuroendocrine cancer, i.e., a neuroendocrine cancer that is untreated and not stable, or that has been treated and has not responded to treatment, or that has been treated and still has active disease, as determined by imaging data and / or best clinical judgment.

[0128] The term "neoplastic disease" refers to an abnormal growth of cells or tissue that is benign (non-cancerous) or malignant (cancerous). For example, a neoplastic disease can be a neuroendocrine cancer.

[0129] The term "neoplastic tissue" refers to a mass of abnormally proliferating cells.

[0130] The term "non-neoplastic tissue" refers to a mass of normally growing cells.

[0131] As used herein, the term "about" when used in conjunction with a numerical value and / or range generally refers to a numerical value and / or range that is close to the stated numerical value and / or range. In some cases, the term "about" can mean within ±10% of the stated numerical value. For example, in some cases, "about 100 units" can mean within 100 ±10% (e.g., 90 to 110). [Example]

[0132] The present disclosure is further illustrated by the following examples, which should not be construed as limiting the disclosure in scope or spirit to the specific procedures described herein. It should be understood that these examples are provided to illustrate particular embodiments and are not intended to limit the scope of the disclosure. It should further be understood that various other embodiments, modifications, and equivalents thereof, which may be suggested to those skilled in the art, may be employed without departing from the spirit of the present disclosure and / or the scope of the appended claims.

[0133] Example 1. Obtaining a 36-marker gene panel

[0134] The NET salivary transcript panel was derived from an evaluation of gene expression in matched blood and saliva samples from 44 patients with neuroendocrine cancer, including expression of biomarkers previously identified in blood samples from neuroendocrine cancer patients (see US2014-0066328A1, US2016-0076106A1, and US2019-0160189A1). While 49 (96%) of the previously identified genes were detectable, only 36 of these were detectable in >60% of the saliva samples (Figure 1). These 36 genes were highly correlated both in terms of measured values ​​(Ct values) and when expressed as normalized values. The correlation between blood and salivary Ct values ​​was r = 0.52 (p = 0.0011, Figure 2A), and for normalized values, the Pearson r value was 0.51 (p = 0.0014, Figure 2B).

[0135] These genes were shown to be highly expressed in tumor tissues of neuroendocrine cancers and were significantly correlated with gene expression in saliva (r=0.89, p<0.0001), identifying the potential use of saliva to effectively function as a liquid biopsy (Figure 3).

[0136] Evaluation of transcripts in a preliminary dataset of saliva samples from age- (mean 72 years) and gender- (8 males: 7 females)-matched patients with neuroendocrine cancer (n = 15) and normal saliva (n = 30) confirmed the expression of 36 genes as markers of neuroendocrine cancer (Figure 4). These data indicate that the candidate target transcripts are produced by neoplastically transformed neuroendocrine cells and are detectable in saliva.

[0137] We constructed an artificial intelligence model of neuroendocrine cancer disease using the normalized gene expression of these 36 markers (Table 2) in saliva from control samples (n = 274) and neuroendocrine cancer samples (n = 76). The dataset was randomly divided into training and test partitions for model creation and validation, respectively. Twelve algorithms (XGB, Random Forest (RF), glmnet, cforest, CART, treebag, knn, nnet, SVM-radial, SVM-linear, NB, and mlp) were evaluated. The best-performing algorithm (RF - "Random Forest") best predicted the training data. For the test set, RF generated probability scores for predicting samples. Each probability score reflects the algorithm's "certainty" that an unknown sample belongs to either the "control" or "neuroendocrine cancer" class. For example, unknown sample S1 could have the following probability vector: [control = 20%, NET = 80%]. This sample is considered a neuroendocrine cancer sample.

[0138] [Table 2-1] [Table 2-2] [Table 2-3] [Table 2-4]

[0139] The 36 marker genes identified by the random forest machine algorithm were visualized in a derivation cohort of n = 274 control samples and 76 cancer samples using the IVIS algorithm (Figure 5A-C).

[0140] Example 2. Clinical utility

[0141] The NET saliva score was significantly (p<0.001) higher in neuroendocrine carcinoma (57±14%) compared with controls (15±11%) (Figure 6). Data (receiver operating characteristic curve analysis and metrics) regarding the utility of the test for distinguishing patients with neuroendocrine carcinoma (n=30) from controls (n=108) in the validation study are included in Figure 7. The score demonstrated an area under the curve (AUROC) of 0.98. The metric had a sensitivity of 100% and a specificity of 88% (Figure 8). The Youden index J was 0.88, and the Z statistic for distinguishing controls was 54.9.

[0142] Specific evaluation of a neuroendocrine cancer cohort before and after surgery identified complete tumor removal, with the absence of evidence of disease associated with a significant decrease in NET saliva scores (p<0.0001) (Figure 9). Levels were not significantly different from controls. Evaluation of another cohort identified patients who received therapy and responded to the therapy as having significantly lower scores (p<0.001) (Figure 10), and were diagnosed with the disease. Therapies included targeted therapy and PRRT. Thus, this tool can accurately identify treatment responses in neuroendocrine cancer disease.

[0143] equivalent While the present invention has been described above in conjunction with specific embodiments, many alternatives, modifications, and other variations will be apparent to those skilled in the art, and all such alternatives, modifications, and variations are intended to fall within the spirit and scope of the present invention.

Claims

1. 1. A method for identifying the presence or absence of neuroendocrine cancer in a subject in need thereof, comprising: (a) determining expression levels of at least 36 biomarkers from a saliva sample of the subject, wherein the at least 36 biomarkers are AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1 , MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, ZXDC, and housekeeping genes; (b) The expression level of each of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC relative to the expression level of the housekeeping gene. and normalizing the expression levels of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC, respectively; (c) inputting each normalized expression level of step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cutoff value; (e) identifying the subject as having the neuroendocrine cancer if the score is equal to or greater than the predetermined cutoff value, or identifying the subject as not having the neuroendocrine cancer if the score is less than the predetermined cutoff value. A method comprising:

2. 2. The method of claim 1, wherein the predetermined cutoff value is 26% on a scale of 0 to 100%.

3. 1. A method for determining whether a neuroendocrine cancer in a subject is stable or progressing, comprising: (a) determining expression levels of at least 36 biomarkers from a saliva sample of the subject, wherein the at least 36 biomarkers are AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1 , MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, ZXDC, and housekeeping genes; (b) The expression level of each of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC relative to the expression level of the housekeeping gene. and normalizing the expression levels of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC, respectively; (c) inputting each normalized expression level of step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cutoff value; (e) determining that the neuroendocrine cancer is progressing if the score is equal to or greater than the predetermined cutoff value, or determining that the neuroendocrine cancer is stable if the score is less than the predetermined cutoff value. A method comprising:

4. 4. The method of claim 3, wherein the predetermined cutoff value is 50% on a scale of 0 to 100%.

5. 1. A method for determining the completeness of surgery to remove a neuroendocrine cancer in a subject, comprising: (a) determining expression levels of at least 36 biomarkers from a saliva sample of the subject after the surgery, wherein the at least 36 biomarkers are AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, L determining the nucleotide sequences of the nucleotides of interest, including EO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, ZXDC, and housekeeping genes; (b) The expression level of each of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC relative to the expression level of the housekeeping gene. and normalizing the expression levels of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC, respectively; (c) inputting each normalized expression level of step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cutoff value; (e) identifying the neuroendocrine cancer as not being completely removed if the score is equal to or greater than the predetermined cutoff value, or identifying the neuroendocrine cancer as being completely removed if the score is less than the predetermined cutoff value. A method comprising:

6. 6. The method of claim 5, wherein the predetermined cutoff value is 50% on a scale of 0 to 100%.

7. 1. A method for assessing the response of a subject having a neuroendocrine cancer to an anti-neuroendocrine cancer therapy, comprising: (a) at a first time point; (i) determining the expression levels of at least 36 biomarkers from a saliva sample of the subject, wherein the at least 38 biomarkers are AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1 , MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, ZXDC, and housekeeping genes; (ii) comparing the expression levels of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC with the expression levels of the housekeeping genes; and normalizing the expression levels of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC, respectively; (iii) inputting each normalized expression level of step (a)(ii) into an algorithm to generate a first score; (b) at a second time point, the second time point being after the first time point and after administration of the anti-neuroendocrine therapy to the subject; (i) determining the expression levels of the at least 36 biomarkers from a saliva sample of the subject; (ii) comparing the expression levels of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC with the expression levels of the housekeeping genes; and normalizing the expression levels of AKAP8L, APLP2, ARAF, BNIP3L, BRAF, CD59, COMMD9, CTGF, FAM131A, FLJ10357 / ARHGEF40, FZD7, GLT8D1, KRAS, LEO1, MORF4L2, NUDT3, OAZ2, PANK2, PHF21A, PKD1, PLD3, PNMA2, PQBP1, RAF1, RNF41, RSF1, RTN2, SMARCD3, SSTR1, SSTR3, SSTR4, TECPR2, TRMT112, VPS13C, WDFY3, and ZXDC, respectively; (iii) inputting each normalized expression level of step (b)(ii) into an algorithm to generate a second score; (c) comparing the first score with the second score; (d) identifying the subject as responsive to the anti-neuroendocrine cancer therapy if the second score is reduced compared to the first score, or identifying the subject as non-responsive to the anti-neuroendocrine cancer therapy if the second score is not reduced compared to the first score. A method comprising:

8. 8. The method of claim 7, wherein the subject is identified as responsive to the anti-neuroendocrine cancer therapy if the second score is at least 5% lower than the first score.

9. The method of any one of claims 1 to 8, wherein the housekeeping gene is selected from the group consisting of ATG4B, RHOA, TOX4, TPT1, and TXNIP.

10. The method of claim 2, wherein the housekeeping gene is RHOA.

11. The method of any one of claims 1 to 10, having a sensitivity of at least 90%.

12. The method of any one of claims 1 to 11, having a specificity of at least 90%.

13. The method of any one of claims 1 to 12, wherein at least one of the at least 36 biomarkers is RNA, cDNA, or protein.

14. 14. The method of claim 13, wherein when the biomarker is RNA, the RNA is reverse transcribed to produce cDNA, and the expression level of the produced cDNA is detected.

15. 15. The method of any one of claims 1 to 14, wherein the expression level of the biomarker is detected by forming a complex between the biomarker and a labeled probe or primer.

16. 14. The method of claim 13, wherein when the biomarker is a protein, the protein is detected by forming a complex between the protein and the labeled antibody.

17. 17. The method of claim 16, wherein the label is a fluorescent label.

18. 14. The method of claim 13, wherein when the biomarker is RNA or cDNA, the RNA or cDNA is detected by forming a complex between the RNA or cDNA and a labeled nucleic acid probe or primer.

19. 19. The method of claim 18, wherein the label is a fluorescent label.

20. 20. The method of claim 18 or 19, wherein the complex between the RNA or cDNA and the labeled nucleic acid probe or primer is a hybridization complex.

21. 21. The method of any one of claims 1 to 20, wherein the first predetermined cut-off value is obtained from a plurality of reference samples obtained from subjects who do not have or have not been diagnosed as having a neoplastic disease.

22. 22. The method of claim 21, wherein the neoplastic disease is a neuroendocrine cancer.

23. 23. The method of any one of claims 1 to 22, wherein the algorithm is XGB, RF, glmnet, cforest, CART, treebag, knn, nnet, SVM-radial, SVM-linear, NB, or mlp.

24. 23. The method of claim 22, wherein the algorithm is RF, preferably the RF algorithm is a grid search optimized random forest.

25. 25. The method of claim 24, wherein the machine learning algorithm is trained using expression levels or normalized expression levels of the at least 36 biomarkers from a plurality of reference samples obtained from subjects without neuroendocrine cancer and expression levels or normalized expression levels of the at least 36 biomarkers from a plurality of reference samples obtained from subjects with neuroendocrine cancer.

26. 26. The method of any one of claims 1 to 25, further comprising treating the subject identified as having neuroendocrine cancer with at least one anti-neuroendocrine cancer therapy.

27. 27. The method of any one of claims 1 to 26, wherein the anti-neuroendocrine cancer therapy comprises active surveillance, surgery, cryotherapy, chemotherapy, targeted therapy, radiation therapy, or any combination thereof.

28. 28. The method of claim 27, wherein the targeted therapy comprises somatostatin analog therapy, everolimus, sunitinib, immunotherapy, or any combination thereof.

29. 28. The method of claim 27, wherein the chemotherapy comprises capecitabine, temozolomide, or any combination thereof.

30. 28. The method of claim 27, wherein the radiation therapy comprises peptide receptor radionuclide therapy (PRRT).

31. The method of any one of claims 1 to 30, wherein the first time point is prior to administration of the therapy to the subject.

32. 32. The method of any one of claims 1 to 31, wherein the first time point is after administration of the therapy to the subject.

33. The method of any one of claims 1 to 32, wherein the saliva sample is self-collected saliva in a container containing a stabilising liquid.

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