Method for detecting neuroendocrine cancer in saliva

Abstract

The present invention relates to a method for detecting neuroendocrine cancer in saliva, a method for determining the completeness of a procedure, a method for determining whether neuroendocrine cancer is stable or progresses, and a method for assessing the response to neuroendocrine cancer therapy.

Description

[0001] Related Applications

[0002] This application claims priority and the benefit of U.S. Provisional Application No. 63 / 377,808, filed Sep. 30, 2022, the content of which is incorporated herein by reference in its entirety.

[0003] Reference to Electronic Sequence Listing

[0004] The content of the electronic sequence listing (LBIO-007_001WO_SeqList_ST26.xml; size: 207,502 bytes; and creation date: Sep. 27, 2023) is incorporated herein by reference in its entirety. Background Art

[0005] Neuroendocrine carcinoma, also known as neuroendocrine neoplasm (NEN) or neuroendocrine tumor (NET), is a tumor derived from specialized cells of the neuroendocrine system in the body. These cells have the characteristics of both endocrine cells that produce hormones and nerve cells. They are found throughout the organs of the body, including the gastrointestinal (GI) tract, pancreas, and lungs, but can also occur in other locations such as the adrenal gland (pheochromocytoma) or central nervous system (paraganglioma) or pituitary gland. Over the past three decades, the incidence and prevalence of NET / NEN have increased by 100 to 600% in the United States, without a significant increase in survival. Symptoms of neuroendocrine carcinoma include flushing and sweating of the skin, wheezing, coughing and shortness of breath, diarrhea, coughing, sweating, weight gain, pain from cancer that has spread to the bones or other areas, and significant changes in heart rate and blood pressure.

[0006] The heterogeneity and complexity of these tumors have made diagnosis, treatment, and classification difficult. These neoplasms lack several mutations commonly associated with other cancers, and microsatellite instability is largely absent. Individual histopathological subtypes, as determined based on tissue resources such as biopsies, can be associated with different clinical behaviors, but there is no clear, generally accepted molecular pathological classification or prediction scheme, hindering diagnosis, staging, treatment evaluation, and follow-up.

[0007] Existing diagnostic and prognostic methods for tumors include imaging (e.g., CT or MRI), histology, measurement of circulating hormones and proteins such as chromogranin A, and detection of some gene products. Available methods are, for example, limited by low sensitivity and / or specificity, inability to detect early disease, and continued exposure to the radiation risk associated with imaging protocols. Tumors are often diagnosed only after they have metastasized and are often untreatable. Additionally, follow-up is difficult, especially in patients with residual disease burden.

[0008] Molecular genetic information is used to understand the biology of neuroendocrine carcinoma, but there is an incomplete understanding of the molecular mechanisms underlying the pathogenesis and the absence of molecular-based biomarkers that can be used to predict sensitivity to therapeutic agents. Thus, the development of diagnostic methods that more accurately define disease states, identify sensitivity to therapies, and ultimately can be used to better monitor disease progression is crucial.

[0009] Surveillance remains the cornerstone of monitoring neuroendocrine carcinoma and detecting recurrence at an early stage. After potentially curative resection, monitoring can be carried out by measuring blood biomarkers and / or imaging such as CT to detect asymptomatic metastatic disease at an early stage.

[0010] The current biomarker for monitoring is chromogranin A (CgA). The sensitivity and specificity of this biomarker are poor, and other hormone biomarkers specific for the primary tumor may be used. In any case, detecting residual disease remains difficult, and the protocols usually cause significant patient / physician concern.

[0011] Although used to identify the timing of imaging, histologic grading has similarly been shown to have low sensitivity and specificity for predicting recurrence.

[0012] Saliva is an important compartment for testing, which allows the evaluation of biomarkers for viral, bacterial, and fungal parasite infections, as well as markers for measuring systemic and non-systemic diseases. Human RNA obtained from cell-free saliva has been evaluated using sequencing and PCR techniques. Cell-free RNA from healthy individuals contains more than 3000 mRNA species. RNA generally enters the oral cavity as a component of gingival crevicular fluid through secretion (from the parotid, submandibular, and sublingual glands) and from exfoliated oral epithelial cells. RNA can be derived from acinar cells or through circulation.

[0013] Saliva has been identified as a compartment for testing for other cancers such as head and neck tumors. Generally, viral DNA (HPV) is isolated and amplified. This is used to provide a diagnosis of the disease. Recently, tumor RNA has been detected in saliva. For example, a 4-gene RNA-based biomarker has been developed for the diagnosis of oral cancer. The source of the RNA may be from the salivary gland itself or may be secondary to cells secreted into the oral cavity such as lymphocytes. It is also known that the salivary gland is vascularized and filters blood products. This suggests that blood may also be a source of RNA detectable in saliva. Summary of the Invention

[0014] The present disclosure provides a method for identifying the presence or absence of neuroendocrine carcinoma in a subject in need thereof, the method comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 36 biomarkers comprise 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 a housekeeping gene; (b) normalizing 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 against the expression level of the housekeeping gene, thereby obtaining the normalized 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; (c) inputting each of the normalized expression levels from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) identifying the presence of neuroendocrine carcinoma in the subject when the score is greater than or equal to the predetermined cut-off value, or determining the absence of neuroendocrine carcinoma in the subject when the score is less than the predetermined cut-off value. In some aspects, the predetermined cut-off value is 26% on a scale of 0 - 100%.

[0015] The present disclosure provides a method for determining whether a neuroendocrine carcinoma in a subject is stable or progressive, the method comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 36 biomarkers comprise 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 a housekeeping gene; (b) normalizing 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 against the expression level of the housekeeping gene, thereby obtaining the normalized 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; (c) inputting each of the normalized expression levels from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) determining that the neuroendocrine carcinoma is progressive when the score is greater than or equal to the predetermined cut-off value, or determining that the neuroendocrine carcinoma is stable when the score is less than the predetermined cut-off value. In some aspects, the predetermined cut-off value is 50% on a scale of 0 to 100%.

[0016] The present disclosure provides a method for determining the completeness of a surgery for excising a neuroendocrine carcinoma in a subject, the method comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject after surgery, wherein the 36 biomarkers comprise 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 a housekeeping gene; (b) normalizing 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 against the expression level of the housekeeping gene, thereby obtaining the normalized 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; (c) inputting each of the normalized expression levels from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) when the score is greater than or equal to the predetermined cut-off value, identifying that the neuroendocrine carcinoma has not been completely resected, or when the score is less than the predetermined cut-off value, identifying that the neuroendocrine carcinoma has been completely resected. In some aspects, the predetermined cut-off value is 50% on a scale of 0 to 100%.

[0017] The present disclosure provides a method for assessing the response of a subject with neuroendocrine cancer to an anti-neuroendocrine cancer therapy, the method comprising: (a) at a first time point: (i) determining the expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 38 biomarkers comprise 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 a housekeeping gene; (ii) normalizing 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 against the expression level of the housekeeping gene, thereby obtaining the normalized 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; and (iii) inputting each normalized expression level from step (a)(ii) into an algorithm to generate a first score; (b) at a second time point, wherein the second time point is after the first time point and after administering an anti-neuroendocrine therapy to the subject: (i) determining the expression levels of at least 36 biomarkers in a test sample from the subject;(ii) Normalize 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 against the expression level of a housekeeping gene to obtain the normalized 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; and (iii) input each normalized expression level from step (b)(ii) into an algorithm to generate a second score; (c) compare the first score and the second score; and (d) identify a subject as responsive to anti-neuroendocrine cancer therapy when the second score is decreased compared to the first score, or identify a subject as non-responsive to anti-neuroendocrine cancer therapy when the second score is not decreased compared to the first score. In some aspects, a subject is identified as responsive to anti-neuroendocrine cancer therapy when the second score is at least 5% lower than the first score.;

[0018] In some aspects of the foregoing method, the housekeeping gene is selected from ATG4B, RHOA, TOX4, TPT1, and TXNIP.

[0019] In some aspects of the foregoing method, the housekeeping gene is RHOA.

[0020] In some aspects of the foregoing method, the method has a sensitivity of at least 90%.

[0021] In some aspects of the foregoing method, the method has a specificity of at least 90%.

[0022] In some aspects of the foregoing method, at least one of the at least 36 biomarkers is RNA, cDNA, or protein.

[0023] In some aspects of the foregoing method, when the biomarker is RNA, the RNA is reverse transcribed to produce cDNA, and the expression level of the produced cDNA is detected.

[0024] In some aspects of the foregoing method, the expression level of the biomarker is detected by forming a complex between the biomarker and a labeled probe or primer.

[0025] In some aspects of the foregoing method, when the biomarker is a protein, the protein is detected by forming a complex between the protein and a labeled antibody. In some aspects, the label is a fluorescent label.

[0026] In some aspects of the foregoing method, 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 aspects, the label is a fluorescent label. In some aspects, the complex between the RNA or cDNA and the labeled nucleic acid probe or primer is a hybridization complex.

[0027] In some aspects of the foregoing method, a first predetermined cut-off value is derived from a plurality of reference samples obtained from subjects who do not have or have not been diagnosed with a neoplastic disease. In some aspects, the neoplastic disease is neuroendocrine carcinoma.

[0028] In some aspects of the foregoing method, the algorithm is XGB, RF, glmnet, cforest, CART, treebag, knn, nnet, SVM - radial, SVM - linear, NB or mlp. In some aspects, the algorithm is RF, preferably where the RF algorithm is a grid search optimized random forest.

[0029] In some aspects of the foregoing method, the expression levels or normalized expression levels of at least 36 biomarkers obtained from a plurality of reference samples from subjects who do not have neuroendocrine carcinoma, and the expression levels or normalized expression levels of at least 36 biomarkers from a plurality of reference samples from subjects who have neuroendocrine carcinoma are used to train a machine learning algorithm.

[0030] In some aspects of the foregoing method, the method further includes treating a subject identified as having neuroendocrine carcinoma with at least one anti - neuroendocrine carcinoma therapy.

[0031] In some aspects of the foregoing method, the anti - neuroendocrine carcinoma therapy comprises active surveillance, surgery, cryotherapy, chemotherapy, targeted therapy, radiotherapy or any combination thereof.

[0032] In some aspects of the foregoing method, the targeted therapy comprises somatostatin analogue therapy, everolimus, sunitinib, immunotherapy or any combination thereof.

[0033] In some aspects of the foregoing method, the chemotherapy comprises capecitabine, temozolomide, or any combination thereof.

[0034] In some aspects of the foregoing method, the radiotherapy comprises peptide receptor radionuclide therapy (PRRT).

[0035] In some aspects of the foregoing method, the first time point is before administering the therapy to the subject.

[0036] In some aspects of the foregoing method, the first time point is after administering the therapy to the subject.

[0037] In some aspects of the foregoing method, the test sample is saliva.

[0038] In some aspects of the foregoing method, the test sample is saliva collected into a container with stable fluid. Description of the Drawings

[0039] Figure 1 is a graph showing the relationship between gene expression in blood and saliva.

[0040] Figure 2A and Figure 2B is an X-Y scatter plot showing the concordance between Ct values ( Figure 2A ) in blood and saliva and the normalized gene expression ( Figure 2B ) in blood and saliva. The red line is the linear correlation. The vertical and horizontal lines are the SEM and SD from the mean of 36 target genes, respectively.

[0041] Figure 3 is a graph showing the relationship between the normalized gene expression in tumor samples and saliva. The red line is the linear correlation. The vertical and horizontal lines are the SEM and SD from the mean of 36 target genes, respectively.

[0042] Figure 4 is a graph showing gene expression in age / sex-matched controls: n = 30 and neuroendocrine cancer cases: n = 15). The expression levels were significantly (p < 0.05) elevated in 28 genes and significantly decreased in 8 target genes.

[0043] Figure 5A , Figure 5B and Figure 5C is a graph showing the manifestation of 36 putative marker genes identified by a random forest algorithm in a derived cohort of n = 274 control samples and 76 cancer samples. ( Figure 5A ) expression is normalized against ATG4B. ( Figure 5B ) expression is normalized against RHOA. ( Figure 5C)Expression is normalized against TXNIP.

[0044] Figure 6 Is a graph showing the NET saliva scores in an independent cohort of controls (n = 108) and neuroendocrine carcinomas (n = 30). The levels were significantly elevated in NET (57 ± 15) compared to controls (15 ± 11) (p < 0.0001).

[0045] Figure 7 Is a graph showing the receiver operating characteristic analysis of the test partition in an independent cohort. The AUROC was 0.98. The Youden J index was 0.88. The Z statistic was highly significant (54.9; p < 0.0001).

[0046] Figure 8 Is a graph showing the metrics of the assay for determining neuroendocrine carcinoma. The sensitivity was 100% and the specificity was 88%.

[0047] Figure 9 Is a graph showing the effect of surgery on the NET saliva score. The levels were elevated prior to surgery (66 ± 10%). Surgery reduced the levels to 30 ± 12% (p < 0.0001), not different from control levels.

[0048] Figure 10A -B is a spider graph showing the effect of treatment on the NET saliva score. The levels were elevated prior to treatment (63 ± 44%). In those patients who responded to therapy, the levels decreased by -40 ± 31% and -51 ± 25% at two follow-up time points (p < 0.0001). In those patients who progressed despite therapy, the levels increased by +7 ± 16% and +32 ± 30% (p < 0.05). ( Figure 10A )Spider graph of all patients. ( Figure 10B )Spider graphs of individual responders (blue) and progressors (red). DETAILED DESCRIPTION

[0049] Details of the invention are set forth in the accompanying specification below. Although illustrative methods and materials are described now, methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention. Other features, objects, and advantages of the present invention will be apparent from the specification and claims. In the specification and the appended claims, the singular forms also include the plural unless the context clearly dictates otherwise. 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, unless otherwise defined. All patents and publications cited in this specification are incorporated herein by reference in their entirety.

[0050] The present disclosure describes methods for quantitatively (scoring) salivary neuroendocrine cancer molecular markers with high sensitivity and specificity for purposes including but not limited to: detecting NET / NEN, determining whether a NET / NEN is stable or progressive, determining the completeness of surgery, assessing a subject's response to neuroendocrine cancer therapy, treating NET / NEN in a subject, or any combination 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 level of a housekeeping gene, are elevated in subjects with neuroendocrine cancer compared to healthy subjects.

[0051] As described herein, measurement of the expression levels of the aforementioned circulating neuroendocrine cancer transcripts (collectively referred to as "NET salivary transcripts") in a saliva sample from a subject can be used to diagnose neuroendocrine cancer. In a non-limiting example, the expression levels of NET salivary transcripts, as measured from a saliva sample from a subject, can be input into an algorithm to generate a score (referred to herein as the "NET saliva score"), which can be used to diagnose the presence of NET / NEN in a subject. Additionally, following administration of one or more anti-neuroendocrine cancer therapies (such as surgery and chemotherapy), a decrease in a subject's NET saliva score can be used to determine the responsiveness of the subject to the one or more therapies, optionally in combination with standard clinical evaluation and imaging.

[0052] Accordingly, the present disclosure provides a method for identifying the presence or absence of neuroendocrine carcinoma in a subject in need thereof, the method comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 36 biomarkers comprise 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 a housekeeping gene; (b) normalizing 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 against the expression level of the housekeeping gene, thereby obtaining the normalized 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; and (c) identifying the presence or absence of neuroendocrine carcinoma in the subject based on the normalized expression levels from step (b).In some aspects, determining the presence or absence of a neuroendocrine carcinoma in a subject based on the normalized expression levels from step (b) can include comparing the normalized expression levels to corresponding predetermined cut-off values and determining the presence or absence of a neuroendocrine carcinoma in the subject 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 levels and the corresponding predetermined cut-off values.

[0053] The present disclosure provides a method for identifying the presence or absence of neuroendocrine carcinoma in a subject in need thereof, the method comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 36 biomarkers comprise 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 a housekeeping gene; (b) normalizing 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 against the expression level of the housekeeping gene, thereby obtaining the normalized 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; (c) inputting each normalized expression level from step (b) into an algorithm to generate a score; and (d) identifying the presence or absence of neuroendocrine carcinoma in the subject based on the score. In some aspects, identifying the presence or absence of neuroendocrine carcinoma in the subject based on the score may comprise comparing the score with a predetermined cut-off value and identifying the presence or absence of neuroendocrine carcinoma in the subject 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 score and the predetermined cut-off value.

[0054] Accordingly, the present disclosure provides a method for identifying the presence or absence of neuroendocrine carcinoma in a subject in need thereof, the method comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 36 biomarkers comprise 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 a housekeeping gene; (b) normalizing 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 against the expression level of the housekeeping gene, thereby obtaining the normalized 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; (c) inputting each of the normalized expression levels from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) identifying the presence of neuroendocrine carcinoma in the subject when the score is greater than or equal to the predetermined cut-off value, or determining the absence of neuroendocrine carcinoma in the subject when the score is less than the predetermined cut-off value.

[0055] Accordingly, the present disclosure provides a method for identifying the presence or absence of neuroendocrine carcinoma in a subject in need thereof, the method comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 36 biomarkers comprise 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 a housekeeping gene; (b) normalizing 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 against the expression level of the housekeeping gene, thereby obtaining the normalized 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; (c) inputting each of the normalized expression levels from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) identifying the presence of neuroendocrine carcinoma in the subject when the score is greater than the predetermined cut-off value, or determining the absence of neuroendocrine carcinoma in the subject when the score is less than or equal to the predetermined cut-off value.

[0056] In some aspects of the foregoing method, the predetermined truncation value can be 26% on a scale of 0 - 100%.

[0057] Accordingly, the present disclosure provides a method for assessing the risk that a subject has neuroendocrine cancer, the method comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 36 biomarkers comprise 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 a housekeeping gene; (b) normalizing 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 against the expression level of the housekeeping gene, thereby obtaining the normalized 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; and (c) assessing the risk that the subject has neuroendocrine cancer based on the normalized expression levels from step (b). In some aspects, assessing the risk that the subject has neuroendocrine cancer based on the normalized expression levels from step (b) can include comparing the normalized expression levels with corresponding predetermined cut-off values, and assessing the risk that the subject has 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 levels and the corresponding predetermined cut-off values.

[0058] The present disclosure provides a method for assessing the risk that a subject has neuroendocrine cancer, the method comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 36 biomarkers comprise 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 a housekeeping gene; (b) normalizing 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 against the expression level of the housekeeping gene, thereby obtaining the normalized 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; and (c) inputting each of the normalized expression levels from step (b) into an algorithm to generate a score; and (d) assessing the risk that the subject has neuroendocrine cancer based on the score. In some aspects, assessing the risk that the subject has neuroendocrine cancer based on the score may comprise comparing the score with a predetermined cut-off value and assessing the risk that the subject has 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 score and the predetermined cut-off value.

[0059] Accordingly, the present disclosure provides a method for assessing the risk that a subject has neuroendocrine cancer, the method comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 36 biomarkers comprise 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 a housekeeping gene; (b) normalizing 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 against the expression level of the housekeeping gene, thereby obtaining the normalized 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; (c) inputting each of the normalized expression levels from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) when the score is greater than or equal to the predetermined cut-off value, assessing that the subject is at high risk of having neuroendocrine cancer, or when the score is less than the predetermined cut-off value, determining that the subject is at low risk of having neuroendocrine cancer.

[0060] Accordingly, the present disclosure provides a method for assessing the risk that a subject has neuroendocrine cancer, the method comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 36 biomarkers comprise 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 a housekeeping gene; (b) normalizing 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 against the expression level of the housekeeping gene, thereby obtaining the normalized 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; (c) inputting each normalized expression level from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) when the score is greater than the predetermined cut-off value, assessing that the subject is at high risk of having neuroendocrine cancer, or when the score is less than or equal to the predetermined cut-off value, determining that the subject is at low risk of having neuroendocrine cancer.

[0061] In some aspects of the foregoing method, the predetermined truncation value can be 26% on a scale of 0 - 100%.

[0062] The present disclosure provides a method for determining whether a neuroendocrine carcinoma in a subject is stable or progressive, the method comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 36 biomarkers comprise 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 a housekeeping gene; (b) normalizing 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 against the expression level of the housekeeping gene, thereby obtaining the normalized 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; and (c) determining whether the neuroendocrine carcinoma in the subject is stable or progressive based on the normalized expression levels from step (b).In some aspects, determining whether a neuroendocrine carcinoma in a subject is stable or progressive based on the normalized expression levels from step (b) includes comparing the normalized expression levels to corresponding predetermined cut-off values and determining whether the neuroendocrine carcinoma in the subject is stable or progressive 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 levels and the corresponding predetermined cut-off values.

[0063] The present disclosure provides a method for determining whether a neuroendocrine carcinoma in a subject is stable or progressive, the method comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 36 biomarkers comprise 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 a housekeeping gene; (b) normalizing 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 against the expression level of the housekeeping gene, thereby obtaining the normalized 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; and (c) inputting each of the normalized expression levels from step (b) into an algorithm to generate a score; and (d) determining whether the neuroendocrine carcinoma in the subject is stable or progressive based on the score. In some aspects, determining whether the neuroendocrine carcinoma in the subject is stable or progressive based on the score comprises comparing the score with a corresponding predetermined cut-off value and determining whether the neuroendocrine carcinoma in the subject is stable or progressive 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 score and the predetermined cut-off value.

[0064] Accordingly, the present disclosure provides a method for determining whether a neuroendocrine carcinoma in a subject is stable or progressive, the method comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 36 biomarkers comprise 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 a housekeeping gene; (b) normalizing 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 against the expression level of the housekeeping gene, thereby obtaining the normalized 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; (c) inputting each of the normalized expression levels from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) determining that the neuroendocrine carcinoma is progressive when the score is greater than or equal to the predetermined cut-off value, or determining that the neuroendocrine carcinoma is stable when the score is less than the predetermined cut-off value.

[0065] Accordingly, the present disclosure provides a method for determining whether a neuroendocrine carcinoma in a subject is stable or progressive, the method comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 36 biomarkers comprise 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 a housekeeping gene; (b) normalizing 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 against the expression level of the housekeeping gene, thereby obtaining the normalized 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; (c) inputting each of the normalized expression levels from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) determining that the neuroendocrine carcinoma is progressive when the score is greater than the predetermined cut-off value, or determining that the neuroendocrine carcinoma is stable when the score is less than or equal to the predetermined cut-off value.

[0066] In some aspects of the foregoing method, the predetermined cut-off value can be 50% on a scale of 0 to 100%.

[0067] Additionally, the present disclosure provides a method for determining the completeness of a surgery for excising a neuroendocrine carcinoma in a subject, the method comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject after surgery, wherein the 36 biomarkers include 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 a housekeeping gene; (b) 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 each against the expression level of the housekeeping gene, thereby obtaining the normalized 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 each; and (c) identifying that the neuroendocrine carcinoma has not been completely resected or identifying that the neuroendocrine carcinoma has been completely resected based on the normalized expression levels from step (b).In some aspects, identifying that a neuroendocrine carcinoma has not been completely resected or identifying that a neuroendocrine carcinoma has been completely resected based on the normalized expression levels from step (b) can include comparing the normalized expression levels to corresponding predetermined cut-off values and identifying that a neuroendocrine carcinoma has not been completely resected or identifying that a neuroendocrine carcinoma has been completely resected 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 levels and the corresponding predetermined cut-off values.

[0068] Accordingly, the present disclosure provides a method for determining the completeness of a surgery for excising neuroendocrine cancer in a subject, the method comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject after surgery, wherein the 36 biomarkers include 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 a housekeeping gene; (b) normalizing 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 against the expression level of the housekeeping gene, thereby obtaining the normalized 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; (c) inputting each normalized expression level from step (b) into an algorithm to generate a score; and (d) identifying that the neuroendocrine cancer has not been completely resected or identifying that the neuroendocrine cancer has been completely resected based on the score.In some aspects, determining that a neuroendocrine carcinoma has not been completely resected or determining that a neuroendocrine carcinoma has been completely resected based on a score may include comparing the score to a corresponding predetermined cut-off value and determining that a neuroendocrine carcinoma has not been completely resected or determining that a neuroendocrine carcinoma has been completely resected based on the relationship between the score and the predetermined cut-off value (such as greater than, greater than or equal to, less than, less than or equal to, or equal to).

[0069] Accordingly, the present disclosure provides a method for determining the completeness of a surgery for excising a neuroendocrine carcinoma in a subject, the method comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject after surgery, wherein the 36 biomarkers comprise 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 a housekeeping gene; (b) normalizing 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 against the expression level of the housekeeping gene, thereby obtaining the normalized 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; (c) inputting each of the normalized expression levels from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) when the score is greater than or equal to the predetermined cut-off value, identifying that the neuroendocrine carcinoma has not been completely resected, or when the score is less than the predetermined cut-off value, identifying that the neuroendocrine carcinoma has been completely resected.

[0070] Accordingly, the present disclosure provides a method for determining the completeness of a surgery for excising a neuroendocrine carcinoma in a subject, the method comprising: (a) determining the expression levels of at least 36 biomarkers in a test sample from the subject after surgery, wherein the 36 biomarkers comprise 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 a housekeeping gene; (b) normalizing 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 against the expression level of the housekeeping gene, thereby obtaining the normalized 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; (c) inputting each normalized expression level from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) when the score is greater than the predetermined cut-off value, identifying that the neuroendocrine carcinoma has not been completely resected, or when the score is less than or equal to the predetermined cut-off value, identifying that the neuroendocrine carcinoma has been completely resected.

[0071] In some aspects of the foregoing method, the predetermined cut-off value can be 50% on a 0-100% scale.

[0072] The response of a subject with neuroendocrine cancer to a therapy can also be evaluated by comparing scores determined by the same algorithm at different time points of treatment. For example, the first time point can be before or after the therapy is administered to the subject; the second time point can be after the first time point and after the therapy is administered to the subject. A first score is generated at the first time point, and a second score is generated at the second time point. When the second score is reduced compared to the first score, the subject is considered to respond to the therapy. In some aspects, the second score is reduced compared to the first score when the second score is at least 5% lower than the first score, e.g., 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. When the second score is not significantly reduced or has increased compared to the first score, the subject is considered not to respond to the therapy.

[0073] The present disclosure also provides a method for assessing the response of a subject with neuroendocrine cancer to an anti-neuroendocrine cancer therapy, the method comprising: (a) at a first time point: (i) determining the expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 38 biomarkers comprise 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 a housekeeping gene; (ii) normalizing 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 against the expression level of the housekeeping gene, thereby obtaining the normalized 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; (b) at a second time point, wherein the second time point is after the first time point and after administering the anti-neuroendocrine therapy to the subject: (i) determining the expression levels of at least 36 biomarkers in a test sample from the subject;(ii) Normalize 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 against the expression level of a housekeeping gene, thereby obtaining the normalized 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; (c) Compare the normalized expression levels from step (a)(ii) and step (b)(ii); and (d) When the normalized expression level from step (b)(ii) is reduced compared to the expression level from step (a)(ii), identify that the subject responds to the anti-neuroendocrine cancer therapy, or when the normalized expression level from step (b)(ii) is not reduced compared to the normalized expression level from step (a)(ii), identify that the subject does not respond to the anti-neuroendocrine cancer therapy.;

[0074] The present disclosure also provides a method for assessing the response of a subject with neuroendocrine cancer to an anti-neuroendocrine cancer therapy, the method comprising: (a) at a first time point: (i) determining the expression levels of at least 36 biomarkers in a test sample from the subject, wherein the at least 38 biomarkers comprise 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 a housekeeping gene; (ii) normalizing 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 against the expression level of the housekeeping gene, thereby obtaining the normalized 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; and (iii) inputting each of the normalized expression levels from step (a)(ii) into an algorithm to generate a first score; (b) at a second time point, wherein the second time point is after the first time point and after administering an anti-neuroendocrine therapy to the subject: (i) determining the expression levels of at least 36 biomarkers in a test sample from the subject;(ii) Normalize 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 against the expression level of a housekeeping gene, thereby obtaining the normalized 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; and (iii) input each of the normalized expression levels from step (b)(ii) into an algorithm to generate a second score; (c) compare the first score and the second score; and (d) identify that the subject responds to anti-neuroendocrine cancer therapy when the second score is decreased compared to the first score, or identify that the subject does not respond to anti-neuroendocrine cancer therapy when the second score is not decreased compared to the first score.;

[0075] General methods and definitions

[0076] The following general methods and definitions may be applied to any of the foregoing methods.

[0077] In some aspects, the test sample may comprise saliva.

[0078] Exemplary housekeeping genes include, but are not limited to, ATG4B, RHOA, TOX4, TPT1, and TXNIP. In some aspects, the housekeeping gene is RHOA.

[0079] Each of the biomarkers disclosed herein may have one or more transcript variants. The methods disclosed herein may measure the expression level of any one of the transcript variants of each biomarker.

[0080] In some aspects, determining the expression levels of at least 36 biomarkers in a test sample from a subject can include contacting the test sample with a plurality of reagents that specifically detect the expression of at least 36 biomarkers.

[0081] Accordingly, the present disclosure provides the use of a plurality of reagents that detect the expression of at least 36 biomarkers in the manufacture of a kit for identifying the presence or absence of neuroendocrine carcinoma by the methods described herein.

[0082] The present disclosure also provides the use of a plurality of reagents that detect the expression of at least 36 biomarkers in the manufacture of a kit for identifying the risk that a subject has neuroendocrine carcinoma by the methods described herein.

[0083] The present disclosure also provides the use of a plurality of reagents that detect the expression of at least 36 biomarkers in the manufacture of a kit for determining whether neuroendocrine carcinoma in a subject is stable or progressive by the methods described herein.

[0084] The present disclosure also provides the use of a plurality of reagents that detect the expression of at least 36 biomarkers in the manufacture of a kit for determining the completeness of a surgery to resect neuroendocrine carcinoma in a subject by the methods described herein.

[0085] The present disclosure also provides the use of a plurality of reagents that detect the expression of at least 36 biomarkers in the manufacture of a kit for evaluating the response of a subject with neuroendocrine carcinoma to anti - neuroendocrine carcinoma therapy by the methods described herein.

[0086] The expression levels can be measured in a variety of ways, including but not limited to measuring the mRNA encoded by a selected gene; measuring the amount of protein encoded by a selected gene; measuring the activity of the protein encoded by a selected gene; or any combination thereof.

[0087] The biomarker can be RNA, cDNA or protein. When the biomarker is RNA, the RNA can be reverse - transcribed to produce cDNA (e.g., by RT - PCR), and the expression level of the resulting 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. When the biomarker is RNA or cDNA, the RNA or cDNA can be 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.

[0088] As those skilled in the art will appreciate, gene expression can be detected through microarray analysis. Differential gene expression can also be identified or confirmed using microarray technology. Thus, expression profile biomarkers can be measured in fresh or fixed tissue using microarray technology. In this method, polynucleotide sequences of interest (including cDNA and oligonucleotides) are plated or arrayed on a microchip substrate. The arrayed sequences are then hybridized to specific DNA probes from the cells or tissues of interest. The source of mRNA is typically total RNA isolated from a biological sample, and a corresponding normal tissue or cell line may be used to determine differential expression.

[0089] In some aspects of microarray technology, the inserts of PCR-amplified cDNA clones are applied to a substrate in a dense array. In some aspects, at least 10,000 nucleotide sequences are applied to the substrate. Microarray genes fixed to the microchip at 10,000 elements each are suitable for hybridization under stringent conditions. Fluorescently labeled cDNA probes may be generated by incorporating fluorescent nucleotides via reverse transcription of RNA extracted from the tissue of interest. The labeled cDNA probes applied to the chip specifically hybridize to each DNA spot on the array. After stringent washing to remove non-specifically 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. Quantification of hybridization of each array element allows for the assessment of the corresponding mRNA abundance. Using two-color fluorescence, separately labeled cDNA probes generated from two RNA sources are hybridized to the array in pairs. Thus, the relative abundances of transcripts from two sources corresponding to each designated gene are determined simultaneously. Microarray analysis can be performed using commercially available equipment following the manufacturer's protocol.

[0090] In some aspects, biomarkers (i.e., NET salivary transcripts and / or housekeeping genes) can be detected in saliva samples using RNAseq. As those skilled in the art will appreciate, the first step in gene expression profiling by RNAseq is to extract RNA from a saliva sample, 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 are analyzed and represented as transcripts per million.

[0091] In some aspects, biomarkers (i.e., NET salivary transcripts and / or housekeeping genes) can be detected in saliva samples using qRT-PCR. As those skilled in the art would appreciate, 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 by PCR reaction. Depending on the goal of the profiling, the reverse transcription reaction step is generally primed using specific primers, random hexamers, or oligo dT primers. Two commonly used reverse transcriptases are avian myeloblastosis virus reverse transcriptase (AMV-RT) and Moloney murine leukemia virus reverse transcriptase (MLV-RT).

[0092] In some aspects, 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, chemiluminescent label, radioactive label, etc. Exemplary methods for protein detection include, but are not limited to, enzyme immunoassay (EIA), radioimmunoassay (RIA), Western blot analysis, and enzyme-linked immunosorbent assay (ELISA). For example, the biomarker can be detected in ELISA, where the biomarker antibody is bound to a solid phase and an enzyme-antibody conjugate is used to detect and / or quantify the biomarker present in the sample. Alternatively, a Western blot assay can be used, where the solubilized and separated biomarker is bound to nitrocellulose paper. The combination of highly specific, stable liquid conjugates with sensitive chromogenic substrates allows for rapid and accurate sample identification.

[0093] In some aspects, 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%.

[0094] 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 carcinoma, specificity for identifying whether neuroendocrine carcinoma is stable or progressive, specificity for identifying the completeness of surgery in a subject with neuroendocrine carcinoma, or specificity for assessing the response of a subject with neuroendocrine carcinoma to anti-neuroendocrine carcinoma therapy).

[0095] In some aspects, the methods described herein can have a sensitivity of at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% (e.g., the sensitivity for identifying the presence or absence of neuroendocrine carcinoma, the sensitivity for identifying whether neuroendocrine carcinoma is stable or progressive, the sensitivity for identifying the completeness of surgery in a subject with neuroendocrine carcinoma, or the sensitivity for assessing the response of a subject with neuroendocrine carcinoma to anti-neuroendocrine carcinoma therapy).

[0096] In some aspects, the methods described herein can have an accuracy of at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% (e.g., the accuracy for identifying the presence or absence of neuroendocrine carcinoma, the accuracy for identifying whether neuroendocrine carcinoma is stable or progressive, the accuracy for identifying the completeness of surgery in a subject with neuroendocrine carcinoma, or the accuracy for assessing the response of a subject with neuroendocrine carcinoma to anti-neuroendocrine carcinoma therapy).

[0097] Any algorithm that generates a score for a sample by evaluating where the sample values fall on a prediction model generated using different techniques such as decision trees can be used in the methods disclosed herein. The algorithm analyzes 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 can 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 an ensemble learning method for classification, regression, and other tasks that operates by constructing a large number of decision trees during training.

[0098] In some aspects of the methods of the present disclosure, a machine learning algorithm can be trained using: a) the 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 not having neuroendocrine cancer; and b) the 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 having neuroendocrine cancer. That is, in some aspects, the expression levels or normalized expression levels of at least 36 biomarkers obtained from multiple reference samples from subjects not having neuroendocrine cancer, and the expression levels or normalized expression levels of at least 36 biomarkers from multiple reference samples from subjects having neuroendocrine cancer are used to train a machine learning algorithm.

[0099] In some aspects, one or more predetermined cut-off values can be derived from multiple reference samples obtained from subjects not having or not diagnosed with a neoplastic disease. The multiple reference samples can be about 2 to about 500 samples, about 2 to about 200 samples, about 10 to about 100, or about 20 to about 80 samples.

[0100] In some aspects, determining a predetermined cut-off value can include inputting the normalized expression levels of NET saliva transcripts from each reference sample into the same algorithm used in the above method, thereby generating multiple scores from the multiple reference samples. Then the predetermined cut-off value can be determined by obtaining the arithmetic mean of these scores. In some aspects, the reference samples can comprise saliva. In some aspects, the reference samples are of the same type as the test samples.

[0101] In some aspects of the methods of the present disclosure, a predetermined cut-off value can be calculated and / or selected using at least one receiver operating characteristic (ROC) curve. In some aspects of the methods of the present disclosure, a predetermined cut-off value can be calculated and / or selected using any method known in the art to have any of the characteristics described herein (e.g., a particular sensitivity, specificity, accuracy, or any combination thereof), as would be understood by a person skilled in the art.

[0102] In some aspects, the methods described herein can further comprise treating a subject with an anti-neuroendocrine cancer therapy.

[0103] Accordingly, in some aspects, the methods described herein further comprise treating a subject identified as having neuroendocrine cancer with an anti-neuroendocrine cancer therapy. In some aspects, the methods described herein further comprise treating a subject identified as having progressive neuroendocrine cancer with at least one anti-neuroendocrine cancer therapy. In some aspects, the methods described herein further comprise treating a subject identified as being at high risk of having neuroendocrine cancer with at least one anti-neuroendocrine cancer therapy. In some aspects, the methods described herein further comprise treating a subject in whom neuroendocrine cancer has not been completely resected surgically with at least one anti-neuroendocrine cancer therapy.

[0104] In some aspects, the methods further comprise treating a subject identified as not responding to an anti-neuroendocrine cancer therapy with a different anti-neuroendocrine cancer therapy. In some aspects, the methods further comprise continuing to treat a subject identified as responding to an anti-neuroendocrine cancer therapy with the same anti-neuroendocrine cancer therapy.

[0105] In some aspects, the anti-neuroendocrine cancer therapy can comprise active surveillance, surgery, cryotherapy, chemotherapy, targeted therapy, radiotherapy, or any combination thereof. The anti-neuroendocrine cancer therapy can comprise any therapeutic agent known in the art to be effective in treating neuroendocrine cancer.

[0106] As would be appreciated by one of skill in the art, active surveillance can include physician visits with chromogranin A blood tests and imaging scans approximately every 6 months. Active surveillance can also include imaging with 68 Ga-PET-SSA-CT scans, which may be done every two years.

[0107] As would be appreciated by one of skill in the art, surgery for patients with neuroendocrine cancer can include complete resection (R0 "curative" surgery).

[0108] As would be appreciated by one of skill in the art, cryotherapy (also known as cryosurgery or cryoablation) is the use of extremely low temperatures to freeze and kill neuroendocrine cancer cells, typically in the liver.

[0109] As would be appreciated by one of skill in the art, chemotherapy can comprise streptozocin, doxorubicin, 5-FU, dacarbazine, temozolomide, capecitabine, and oxaliplatin, or any combination thereof.

[0110] As would be appreciated by one of skill in the art, targeted therapy can comprise somatostatin analogs, everolimus, sunitinib, and immunotherapy, or any combination thereof.

[0111] As would be appreciated by one of skill in the art, radiotherapy can comprise peptide receptor radionuclide therapy (PRRT).

[0112] As will be appreciated by those skilled in the art, if a neuroendocrine carcinoma has grown outside the primary tumor site, preventing or slowing the spread of the cancer to the liver or bone is a major goal of treatment. Liver and / or bone-directed therapies can include radiotherapy or the use of radiopharmaceuticals (e.g., indium-111, lutetium-177).

[0113] Sequence information for neuroendocrine carcinoma biomarkers and housekeeping genes is shown in Table 1. Table 1 shows representative sequences for each neuroendocrine carcinoma biomarker and housekeeping gene discussed herein. Those skilled in the art will appreciate that in addition to the specific sequences shown in Table 1, other isoforms and variants of the variants can also be measured in the methods of the present disclosure to obtain the expression levels of the biomarkers or housekeeping genes.

[0114] Table 1. Neuroendocrine Carcinoma Biomarker / Housekeeping Gene Sequence Information

[0115]

[0116]

[0117] As used in this disclosure, the articles "a" and "an" are used to refer to one or more / one or more (i.e., at least one / one) grammatical objects of the article. For example, "element" means one element or more than one element.

[0118] Unless otherwise specified, the term "and / or" as used in this disclosure is intended to mean "and" or "or".

[0119] As used herein, the terms "polynucleotide" and "nucleic acid molecule" are used interchangeably to mean a polymeric form of nucleotides, ribonucleotides or deoxyribonucleotides or modified forms of either class of nucleotides, having a length of at least 10 bases or base pairs, and are intended to include single-stranded and double-stranded forms of DNA. As used herein, a nucleic acid molecule or nucleic acid sequence that serves as a probe in microarray analysis preferably comprises a nucleotide chain, more preferably DNA and / or RNA. In some aspects, the nucleic acid molecule or nucleic acid sequence comprises 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 encompasses chains containing non-natural nucleotides, modified nucleotides, and / or non-nucleotide building blocks that exhibit the same function as natural nucleotides.

[0120] As used herein, the terms "hybridize", "hybridizing", "hybridizes", etc. when used in the context of polynucleotides are intended to refer to conventional hybridization conditions, such as hybridization in 50% formamide / 6XSSC / 0.1% SDS / 100 μg / ml ssDNA, where the temperature for hybridization is above 37 degrees Celsius and the temperature for washing in 0.1XSSC / 0.1% SDS is above 55 degrees Celsius, and preferably refers to stringent hybridization conditions.

[0121] As used herein, the term "normalization" or "normalizer" refers to a difference value expressed according to a standard value to adjust for effects arising from technical variations in sample handling, sample preparation, and measurement methods, rather than biological variations in the concentration of biomarkers in the sample. For example, when measuring the expression of differentially expressed proteins, the absolute value of protein expression can be expressed according to the absolute value of a standard protein that is substantially constant in expression.

[0122] The terms "diagnosis" and "diagnostics" also respectively encompass the terms "prognosis" and "prognostics", as well as the application of such procedures at two or more time points to monitor diagnosis and / or prognosis over time, and statistical modeling based thereon. In addition, the term diagnosis includes: a. prediction (determining whether a patient is likely to develop an invasive disease (hyperplasia / invasion)); b. prognosis (predicting whether a patient is likely to have a better or worse outcome at a preselected future time); c. therapy selection; d. therapeutic drug monitoring; and e. recurrence monitoring.

[0123] "Accuracy" refers to the degree of agreement between a measured or calculated quantity (the test reported value) and its actual (or true) value. Clinical accuracy relates to the proportion of true results (true positive (TP) or true negative (TN)) relative to misclassified results (false positive (FP) or false negative (FN)), and may be stated as sensitivity, specificity, positive predictive value (PPV), or negative predictive value (NPV), or likelihood ratios, odds ratios, and other measures.

[0124] As used herein, the term "biological sample" refers to any sample of biological origin that potentially contains one or more biomarkers. Examples of biological samples include body fluids such as saliva or lavage fluid or any other sample used for detecting diseases.

[0125] As used herein, the term "subject" refers to a mammal, preferably a human. In some aspects, the subject has at least one symptom of neuroendocrine cancer. In some aspects, the subject has a predisposition or family history of developing neuroendocrine cancer. The subject may also have been previously diagnosed with neuroendocrine cancer and is being tested for cancer recurrence.

[0126] "Treating" or "treatment" of a disease or condition refers to implementing a protocol or treatment plan, which may include administering to a patient one or more therapeutic agents in an effort to alleviate the signs or symptoms of the disease or recurrence of the disease. Desired effects of treatment include reducing the rate of disease progression, improving or alleviating the disease state, and remission, increased survival, improved quality of life or improved prognosis. Additionally, "treating" or "treatment" does not require complete alleviation of signs or symptoms, does not require a cure, and specifically includes protocols or treatment plans that have only marginal effects on a patient.

[0127] As used herein, "prevent", "preventing", etc. describe preventing the onset of a disease, condition or disorder, or one or more of its symptoms or complications.

[0128] Biomarker levels may be altered due to treatment of a disease. Alterations in biomarker levels may be measured by the present disclosure. Alterations in biomarker levels may be used to monitor the progression of a disease or a therapy.

[0129] "Changed", "altered" or "significantly different" refers to a detectable change or difference from a reasonably comparable state, profile, measurement, etc. Such changes may be all or nothing. They may be incremental and need not be linear. They may be of an order of magnitude. The change 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, having a p-value of 0.1, 0.05, 0.001 or 0.0001.

[0130] The term "stable disease" refers to a diagnosis regarding the presence of neuroendocrine cancer, however, the neuroendocrine cancer has been treated and remains in a stable condition, i.e., has not progressed, as determined by imaging data and / or best clinical judgment.

[0131] The term "progressive disease" refers to a diagnosis of the presence of a highly active state with respect to neuroendocrine carcinoma, i.e., untreated and unstable, or treated and non-responsive to therapy, or treated and maintaining active disease, as determined by imaging data and / or best clinical judgment.

[0132] The term "neoplastic disease" refers to any abnormal growth of benign (non-cancerous) or malignant (cancerous) cells or tissues. For example, a neoplastic disease can be neuroendocrine carcinoma.

[0133] The term "neoplastic tissue" refers to a mass of abnormally growing cells.

[0134] The term "non-neoplastic tissue" refers to a mass of normally growing cells.

[0135] As used herein, when used in connection with a numerical value and / or range, the term "about" generally refers to those numerical values and / or ranges that are close to the stated numerical value and / or range. In some cases, the term "about" can mean within ±10% of the stated value. For example, in some cases, "about 100 [units]" can mean within ±10% of 100 (e.g., 90 to 110).

[0136] Examples

[0137] The present disclosure is further illustrated by the following examples, which are not to be construed as limiting the scope or spirit of the present disclosure to the specific procedures described herein. It should be understood that the examples are provided to illustrate certain embodiments and are thus not intended to limit the scope of the invention. It should be further understood that various other embodiments, modifications, and their equivalents that may be contemplated by 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.

[0138] Example 1. Derivation of the 36-Marker Gene Subject Group

[0139] The subject group of NET salivary transcripts was derived from an evaluation of gene expression in matched blood and saliva samples collected from 44 neuroendocrine carcinoma patients, including the expression of biomarkers previously identified in blood samples from neuroendocrine carcinoma patients (see US2014-0066328A1, US2016-0076106A1, and US2019-0160189 A1). Forty-nine (96%) of the previously identified genes were detectable, but only 36 of these were detectable in >60% of the saliva samples ( Figure 1 ). These 36 genes were highly correlated both by measurement (Ct value) and when expressed as normalized values. The correlation between blood and saliva Ct values was r = 0.52. (p = 0.0011, Figure 2A), and for the standardized values, the Pearson r value was 0.51 (p = 0.0014; Figure 2B ).

[0140] These genes were confirmed to be highly expressed in neuroendocrine carcinoma tumor tissues and there was a significant correlation with salivary gene expression (r = 0.89, p < 0.0001), identifying that saliva can be used to effectively act as a liquid biopsy ( Figure 3 ).

[0141] Evaluation of transcripts in a preliminary dataset of saliva samples from neuroendocrine carcinoma (n = 15) and normal saliva (n = 30) matched for age (mean 72 years) and sex (8M:7F) confirmed the expression of 36 genes as markers for neuroendocrine carcinoma ( Figure 4 ). These data confirmed that the candidate target transcripts were produced by neoplastic transformed neuroendocrine cells and were detectable in saliva.

[0142] Using the standardized gene expression of these 36 markers in saliva from control (n = 274) and neuroendocrine carcinoma (n = 76) samples (Table 2), an artificial intelligence model for neuroendocrine carcinoma disease was constructed. The dataset was randomly split into a training partition and a test partition 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. In the test set, RF produced a probability score for the predicted samples. Each probability score reflected the “certainty” of the algorithm that an unknown sample belonged to the “control” or “neuroendocrine carcinoma” category. For example, an unknown sample S1 could have the following probability vector [control = 20%, NET = 80%]. This sample would be considered a neuroendocrine carcinoma sample.

[0143] Table 2: 36 Neuroendocrine Carcinoma Salivary Marker Genes Subject Groups (Excluding Housekeeping Genes)

[0144]

[0145]

[0146]

[0147] Using the IVIS algorithm, in a derived cohort of n = 274 control samples and 76 cancer samples, the 36 marker genes identified by the random forest machine algorithm were manifested ( Figure 5A -C).

[0148] Example 2. Clinical utility

[0149] Compared to controls (15 ± 11%), the NET saliva score was significantly elevated in neuroendocrine carcinomas (57 ± 14%) (p < 0.001)( Figure 6 ). Figure 7 Data on the utility of testing to distinguish patients with neuroendocrine carcinoma (n = 30) from controls (n = 108) (receiver operating curve analysis and metrics) were included. The score showed an area under the curve (AUROC) of 0.98. The metrics were: sensitivity: 100% and specificity: 88%( Figure 8 ). The Youden index J was 0.88 and the Z statistic for distinguishing controls was 54.9.

[0150] Specificity assessments before and after surgery in the neuroendocrine carcinoma cohort identified that complete resection of the tumor and no evidence of disease were associated with a significant decrease in the NET saliva score (p < 0.0001)( Figure 9 ). The levels were not significantly different from controls. Assessment of separate cohorts identified that patients who underwent and responded to therapy had significantly lower scores than those diagnosed with disease (p < 0.001) (Figure 10). Therapy included targeted therapy and PRRT. The tool can thus accurately identify treatment response in neuroendocrine carcinoma disease.

[0151] Equivalent schemes

[0152] Although the present invention has been described in connection with the specific embodiments set forth above, many alternatives, modifications, and other variations will be apparent to those of ordinary skill in the art. All such alternatives, modifications, and variations are intended to fall within the spirit and scope of the present invention.

Claims

1. A method for identifying the presence or absence of neuroendocrine carcinoma in a subject in need thereof, the method comprising: (a) determining the expression levels of at least 36 biomarkers from a saliva sample of the subject, wherein the at least 36 biomarkers comprise 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 a housekeeping gene; (b) normalizing 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 against the expression level of the housekeeping gene, thereby obtaining the normalized 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; (c) inputting each of the normalized expression levels from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) when the score is greater than or equal to the predetermined cut-off value, identifying the presence of neuroendocrine carcinoma in the subject, or when the score is less than the predetermined cut-off value, determining the absence of neuroendocrine carcinoma in the subject.

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

3. A method for determining whether a neuroendocrine carcinoma in a subject is stable or progressive, the method comprising: (a) determining the expression levels of at least 36 biomarkers from a saliva sample of the subject, wherein the at least 36 biomarkers comprise 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 a housekeeping gene; (b) normalizing 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 against the expression level of the housekeeping gene, thereby obtaining the normalized 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; (c) inputting each of the normalized expression levels from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) determining that the neuroendocrine carcinoma is progressive when the score is greater than or equal to the predetermined cut-off value, or determining that the neuroendocrine carcinoma is stable when the score is less than the predetermined cut-off value.

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

5. A method for determining the completeness of a surgery for removing neuroendocrine cancer in a subject, the method comprising: (a) determining the expression levels of at least 36 biomarkers in a saliva sample from the subject after surgery, wherein the 36 biomarkers comprise 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 a housekeeping gene; (b) normalizing 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 against the expression level of the housekeeping gene, thereby obtaining the normalized 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; (c) inputting each of the normalized expression levels from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) when the score is greater than or equal to the predetermined cut-off value, identifying that the neuroendocrine cancer has not been completely resected, or when the score is less than the predetermined cut-off value, identifying that the neuroendocrine cancer has been completely resected.

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

7. A method for assessing the response of a subject with neuroendocrine cancer to a neuroendocrine cancer therapy, the method 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 comprise 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 a housekeeping gene; (ii) Normalizing 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 against the expression level of the housekeeping gene, thereby obtaining the normalized 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; and (iii) Inputting each of the normalized expression levels from step (a)(ii) into an algorithm to generate a first score; (b) At a second time point, wherein the second time point is after the first time point and after administering the neuroendocrine therapy to the subject: (i) Determining the expression levels of at least 36 biomarkers from a saliva sample of the subject; (ii) Normalize 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 against the expression level of a housekeeping gene, thereby obtaining the normalized 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; and (iii) Input each of the normalized expression levels from step (b)(ii) into an algorithm to generate a second score; (c) Compare the first score and the second score; and (d) When the second score is reduced compared to the first score, identify the subject as responsive to the anti-neuroendocrine cancer therapy, or when the second score is not reduced compared to the first score, identify the subject as not responsive to the anti-neuroendocrine cancer therapy.

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

9. The method according to any one of the preceding claims, wherein the housekeeping gene is selected from ATG4B, RHOA, TOX4, TPT1, and TXNIP.

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

11. The method according to any one of the preceding claims, which has a sensitivity of at least 90%.

12. The method according to any one of the preceding claims, which has a specificity of at least 90%.

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

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

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

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

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

18. The method according to 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. The method according to claim 18, wherein the label is a fluorescent label.

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

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

22. The method according to claim 21, wherein the neoplastic disease is neuroendocrine carcinoma.

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

24. The method according to claim 22, wherein the algorithm is RF, preferably wherein the RF algorithm is a random forest optimized by grid search.

25. The method according to claim 24, wherein the machine learning algorithm is trained using the expression levels or normalized expression levels of at least 36 biomarkers obtained from a plurality of reference samples from subjects who do not have neuroendocrine carcinoma, and the expression levels or normalized expression levels of at least 36 biomarkers from a plurality of reference samples from subjects who have neuroendocrine carcinoma.

26. The method according to any one of the preceding claims, further comprising treating a subject identified as having neuroendocrine carcinoma with at least one anti-neuroendocrine carcinoma therapy.

27. The method according to any one of the preceding claims, wherein the anti-neuroendocrine carcinoma therapy comprises active surveillance, surgery, cryotherapy, chemotherapy, targeted therapy, radiotherapy or any combination thereof.

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

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

30. The method according to claim 27, wherein the radiotherapy comprises peptide receptor radionuclide therapy (PRRT).

31. The method according to any one of the preceding claims, wherein the first time point is before administering a therapy to the subject.

32. The method according to any one of the preceding claims, wherein the first time point is after administering a therapy to the subject.

33. The method according to any one of the preceding claims, wherein the saliva sample is saliva collected into a container with a stable fluid.

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