Systems, methods, and kits for identifying a risk of disease

Abstract

Disclosed herein are methods, systems, and kits relating to identifying a subject as having or being at an increased risk of having a disease. The methods may include processing a biological sample obtained from an oral cavity of the subject at an oral point of care location. The methods may include generating a report identifying the subject as having or being at an increased risk of having the disease. The report may be generated remotely.

Description

Docket No.: 68417-702.601SYSTEMS, METHODS, AND KITS FOR IDENTIFYING A RISK OF DISEASECROSS-REFERENCE

[0001] This application claims the benefit of United Kingdom Patent Application No.2413648.3, filed September 17, 2024, and United Kingdom Patent Application No. 2509153.9, filed June 10, 2025, each of which are incorporated by reference herein in their entirety.BACKGROUND

[0002] Biological samples such as tissue samples or bodily fluids may be used to diagnose a disease such as cancer. Biological tissue samples may be collected from a subject for routine testing. Routine testing tools may screen subjects for at least one disease by processing the biological tissue samples. Routine testing tools may also help monitor a disease in a subject. Processing the biological tissue samples may include sequencing the genetic code from the samples, detecting biomarkers in the samples, or other methods to gain information from the samples that may be used in obtaining a result from the testing. Routine testing may provide information about a disease or condition a subject may have before recognizable symptoms of the disease or condition appear. Routine testing may provide monitoring information about the status, response, progression, or regression of a disease or condition of a subject.INCORPORATION BY REFERENCE

[0003] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. To the extent publications and patents or patent applications incorporated by reference contradict the disclosure contained in the specification, the specification is intended to supersede and / or take precedence over any such contradictory material.SUMMARY

[0004] Applicant has recognized that screening tools for early detection and monitoring of diseases such as cancer may be essential for reducing patient burden on providers and the healthcare system. Despite the importance of screening tools for detection and monitoring, Applicant has recognized various non-limiting issues concerning screening tools, for example accessibility issues, accuracy issues, ease of use issues, and result interpretation issues.Docket No.: 68417-702.601

[0005] Applicant has recognized that accessibility of screening tools for diseases such as cancer may be limited to medical healthcare providers such as medical doctors and nurses. Applicant has recognized that limited accessibility to screening tools may result from a requirement to provide a tissue or other biological sample in a controlled and regulated environment, such as a medical healthcare provider location, for example a medical clinic, doctor’s office, or hospital. Applicant has recognized that issues such as limited healthcare provider availability and insufficient healthcare insurance coverage are often significant hurdles preventing widespread use of screening tools.

[0006] Applicant has also recognized various non-limiting issues concerning using screening tools for monitoring. Accessibility issues recognized by Applicant may prevent a subject from consistently using a screening tool, as Applicant has recognized. Applicant has also recognized that high costs of multiple screening tools for monitoring periodically may also inhibit the ability of subjects to use screening tools accurately and consistently.

[0007] Applicant has also recognized non-limiting issues concerning accuracy of screening tools, for example at-home use screening tools may be inaccurate and limited in diagnostic breadth due to, for example, lack of expert sampling and expert result interpretation. Applicant has recognized that screening tools utilizing biological samples obtained from an oral cavity of a patient at an oral point of care location have the potential to facilitate widespread availability of screening tools with expert sampling and expert result interpretation. Applicant has also recognized that screening tools utilized at an oral point of care and not limited to sample collection use by healthcare professionals only may lower costs and increase accessibility for subjects at the oral point of care. Applicant has recognized that greater access and consistency of use for screening tools at the oral point of care may increase the accuracy and use instances of the screening tools, as well as allow the screening tools to more accurately monitor a subject’s disease or condition over time.

[0008] In an aspect, described herein are methods of point of care testing to identify a disease in a patient, said method comprising: obtaining a saliva sample from the patient; extracting a deoxyribonucleic acid (DNA) sample from the saliva sample; enriching the DNA sample with a reagent set, wherein the reagent set comprises a panel of biomarkers, thereby generating a plurality of amplicons or derivatives thereof, wherein the panel of biomarkers comprises at least one biomarker selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAP1; sequencing the plurality of amplicons or derivatives thereof to detect the panel of biomarkers; and treating the disease in the patient by identifying the disease in the patient.Docket No.: 68417-702.601

[0009] In some embodiments, the panel of biomarkers comprises at least two biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAP1. In some embodiments, the panel of biomarkers comprises at least three biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT In some embodiments, the panel of biomarkers comprises at least four biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT In some embodiments, the panel of biomarkers comprises at least five biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT In some embodiments, the panel of biomarkers comprises at least six biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT In some embodiments, the panel of biomarkers comprises at least seven biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT In some embodiments, the panel of biomarkers comprises at least eight biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT In some embodiments, the panel of biomarkers comprises at least nine biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT In some embodiments, the panel of biomarkers comprises at least ten biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT In some embodiments, the panel of biomarkers comprises at least eleven biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT In some embodiments, the panel of biomarkers comprises at least twelve biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT In some embodiments, the panel of biomarkers comprises TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT In some embodiments, the method further comprises identifying the disease utilizing the panel of biomarkers. In some embodiments, the disease comprises cancer. In some embodiments, the cancer is an oral cancer. In some embodiments, the oral cancer comprises a cancer of the oral cavity. In some embodiments, the oral cancer comprises one or more of: squamous cell carcinoma (SCC), salivary gland tumors, lymphoma, melanoma, sarcoma, or odontogenic tumors, or any combination thereof. In some embodiments, the disease comprises an oral disease. In some embodiments, the oral disease is periodontal disease. In some embodiments, the oral disease comprises one or more of caries, halitosis, gingivitis, periodontitis, stomatitis, oral thrush, oralDocket No.: 68417-702.601 herpes, salivary gland infections, dental abscess, dental erosion, dental fluorosis, dry mouth syndrome, hand, foot, and mouth disease, oral leukoplakia, oral lichen planus, candidiasis, canker sores, oral cysts, jaw disorders, mumps, oral papilloma, pemphigus vulgaris, pericoronitis, tooth decay, toothache, trench mouth, Vincent’s angina, oral ulcers, or any combination thereof. In some embodiments, the treatment comprises a treatment capable of treating the disease. In some embodiments, the treatment comprises one or more of: a chemotherapy, radiation, an immunotherapy, or a surgery. In some embodiments, the treatment comprises a treatment capable of treating the cancer. In some embodiments, the treatment comprises one or more of: a chemotherapy, radiation, an immunotherapy, or a surgery. In some embodiments, the treatment comprises a treatment capable of treating the oral cancer. In some embodiments, the treatment comprises one or more of: a chemotherapy, radiation, an immunotherapy, or a surgery. In some embodiments, identifying the disease further comprises measuring an amount of the detected panel of biomarkers, measuring a frequency of the detected panel of biomarkers, measuring a concentration of the detected panel of biomarkers, generating a comparative analysis of the detected panel of biomarkers, or mapping patterns of the detected panel of biomarkers, or any combination thereof. In some embodiments, mapping patterns of the detected panel of biomarkers comprises mapping DNA methylation patterns of the detected panel of biomarkers to the enriched . In some embodiments, mapping patterns of the detected panel of biomarkers comprises mapping DNA fusion patterns. In some embodiments, mapping patterns of the detected panel of biomarkers comprises mapping amounts of the one or more biomarkers of the detected panel of biomarkers over areas of the DNA sample or a derivative thereof. In some embodiments, the method further comprises utilizing a machine learning model to identify the disease in the patient based at least in part on the detected panel of biomarkers. In some embodiments, the method further comprises generating a prediction of the patient’s response to the treatment of the disease based at least in part on the detected panel of biomarkers. In some embodiments, the disease comprises a cancer. In some embodiments, the treatment of the disease comprises a treatment responsive to the cancer. In some embodiments, the treatment comprises one or more of: a chemotherapy, radiation, an immunotherapy, or a surgery. In some embodiments, the cancer is an oral cancer. In some embodiments, the method further comprises utilizing the machine learning model to select one or more treatment options based at least in part on the prediction of the patient’s response to the treatment of the disease. In some embodiments, the treatment of the disease comprises a plurality of treatment options. In some embodiments, the method further comprises utilizing the machine learning model to select a subset of the plurality of treatment options. In some embodiments, the selected subset comprisesDocket No.: 68417-702.601 one or more treatments with high efficacy, high safety, low toxicity, low reactivity, low crossreactivity, or any combination thereof.

[0010] In another aspect, described herein are methods of point of care testing to identify a disease in a patient, said method comprising: obtaining a saliva sample from the patient; extracting a DNA sample from the saliva sample; administering a reagent set to the DNA sample, wherein the reagent set comprises a panel of biomarkers, and generating a plurality of amplicons or derivatives thereof; sequencing the plurality of amplicons to generate a report comprising the presence or absence of one or more biomarkers of the panel of biomarkers; wherein the report identifies the disease in the patient. In some embodiments, the panel of biomarkers comprises at least one biomarker selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAP1.

[0011] In yet another aspect, described herein are methods of point of care testing to identify a disease in a patient, said method comprising: obtaining a saliva sample from the patient; extracting a DNA sample from the saliva sample; enriching the DNA sample with a reagent set, wherein the reagent set comprises a panel of biomarkers, thereby generating a plurality of amplicons or derivatives thereof, wherein the panel of biomarkers comprises at least one biomarker selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAP1; sequencing the plurality of amplicons or derivatives thereof to detect the panel of biomarkers, thereby identifying the disease in the patient. In some embodiments, the panel of biomarkers comprises at least two biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAP1.

[0012] In some embodiments, the panel of biomarkers comprises at least three biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAP1. In some embodiments, the panel of biomarkers comprises at least four biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAP1. In some embodiments, the panel of biomarkers comprises at least five biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT In some embodiments, the panel of biomarkers comprises at least six biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT In some embodiments, the panel of biomarkers comprises at least seven biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT In some embodiments, the panel of biomarkers comprises at least eight biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3,Docket No.: 68417-702.601CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT In some embodiments, the panel of biomarkers comprises at least nine biomarkers selected from TP53, N0TCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and AD API. In some embodiments, the panel of biomarkers comprises at least ten biomarkers selected from TP53, N0TCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and AD API. In some embodiments, the panel of biomarkers comprises at least eleven biomarkers selected from TP53, N0TCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAP1. In some embodiments, the panel of biomarkers comprises at least twelve biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAP1. In some embodiments, the panel of biomarkers comprises TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and AD API. In some embodiments, the disease comprises cancer. In some embodiments, the cancer is an oral cancer. In some embodiments, the oral cancer comprises a cancer of the oral cavity. In some embodiments, the oral cancer comprises one or more of: squamous cell carcinoma (SCC), salivary gland tumors, lymphoma, melanoma, sarcoma, or odontogenic tumors, or any combination thereof. In some embodiments, the disease comprises an oral disease. In some embodiments, the oral disease is periodontal disease. In some embodiments, the oral disease comprises one or more of caries, halitosis, gingivitis, periodontitis, stomatitis, oral thrush, oral herpes, salivary gland infections, dental abscess, dental erosion, dental fluorosis, dry mouth syndrome, hand, foot, and mouth disease, oral leukoplakia, oral lichen planus, candidiasis, canker sores, oral cysts, jaw disorders, mumps, oral papilloma, pemphigus vulgaris, pericoronitis, tooth decay, toothache, trench mouth, Vincent’s angina, oral ulcers, or any combination thereof. In some embodiments, the method further comprises determining treatment responsive to the disease and capable of treating the disease. In some embodiments, the treatment comprises one or more of: a chemotherapy, radiation, an immunotherapy, or a surgery. In some embodiments, the treatment comprises a treatment responsive to cancer and capable of treating the cancer. In some embodiments, the treatment capable of treating the cancer comprises one or more of: a chemotherapy, radiation, an immunotherapy, or a surgery. In some embodiments, the treatment comprises a treatment responsive to an oral cancer and capable of treating the oral cancer. In some embodiments, the treatment capable of treating the oral cancer comprises one or more of: a chemotherapy, radiation, an immunotherapy, or a surgery. In some embodiments, identifying the disease further comprises measuring an amount of the detected panel of biomarkers, measuring a frequency of the detected panel of biomarkers, measuring a concentration of the detected panel of biomarkers, generating a comparative analysis of the detected panel of biomarkers, or mapping patterns ofDocket No.: 68417-702.601 the detected panel of biomarkers, or any combination thereof. In some embodiments, mapping patterns of the detected panel of biomarkers comprises mapping DNA methylation patterns of the detected panel of biomarkers to the enriched . In some embodiments, mapping patterns of the detected panel of biomarkers comprises mapping DNA fusion patterns. In some embodiments, mapping patterns of the detected panel of biomarkers comprises mapping amounts of the one or more biomarkers of the detected panel of biomarkers over areas of the DNA sample or a derivative thereof. In some embodiments, the method further comprises utilizing a machine learning model to identify the disease in the patient based at least in part on the detected panel of biomarkers.

[0013] In yet another aspect, described herein are methods of identifying a subject as having or being at an increased risk of having a disease, comprising processing a biological sample obtained from an oral cavity of the subject at an oral point of care location to generate a report identifying the subject as having or being at an increased risk of having the disease at an accuracy of at least 60%, wherein the biological sample is processed at a location that is remote from the oral point of care location.

[0014] In yet another aspect, described herein are methods of generating a disease risk profile for a subject, comprising: (a) obtaining a biological sample from an oral cavity of a subject, wherein the biological sample is obtained at an oral point of care location; (b) depositing the biological sample at the oral point of care location in a sample collection tube comprising a sample preservation reagent or a sample processing reagent, or both; (c) transferring the sample collection tube comprising the biological sample sealed therein to a remote sample processing location, wherein the remote sample processing location processes the biological sample or a derivative thereof to detect a panel of biomarkers of the disease risk profile for the subject, wherein the panel of biomarkers comprises at least one biomarker selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAP1; and (d) transmitting to the oral point of care location or a healthcare professional location the generated disease risk profile for the subject, wherein the disease risk profile identifies a risk of the subject for having or developing the disease.

[0015] In some embodiments, the disease risk profile identifies the risk at an accuracy of at least 60%. In some embodiments, the disease risk profile comprises one or more of a diagnostic risk prediction, a development risk prediction, a severity risk prediction, or an accuracy prediction of the subject for having the disease. In some embodiments, the diagnostic prediction comprises one or more of a binary indication for the subject having the disease, a risk value for the subject having the disease, or a risk level for the subject having the disease. In some embodiments, the development risk prediction comprises one or more of a binary indication for the subjectDocket No.: 68417-702.601 developing the disease, a risk value for the subject developing the disease, or a risk level for the subject developing the disease. In some embodiments, the severity risk prediction comprises one or more of a binary indication for the subject having a severe state of the disease, a binary indication for the subject developing a severe state of the disease, a risk value for the subject having a severe state of the disease, a risk value for the subject developing a severe state of the disease, a value quantifying predicted severity of the disease, or a categorization of predicted severity of the disease. In some embodiments, the risk level comprises one or more of a zero level, low level, medium level, high level, or very high level. In some embodiments, the accuracy prediction comprises a value predicting the accuracy of one or more of the diagnostic risk prediction, the development risk prediction, or the severity risk prediction. In some embodiments, the disease comprises cancer. In some embodiments, the cancer is an oral cancer. In some embodiments, the disease comprises an oral disease. In some embodiments, the oral disease is periodontal disease. In some embodiments, the oral disease comprises one or more of caries, halitosis, gingivitis, periodontitis, stomatitis, oral thrush, oral herpes, salivary gland infections, dental abscess, dental erosion, dental fluorosis, dry mouth syndrome, hand, foot, and mouth disease, oral leukoplakia, oral lichen planus, candidiasis, canker sores, oral cysts, jaw disorders, mumps, oral papilloma, pemphigus vulgaris, pericoronitis, tooth decay, toothache, trench mouth, Vincent’s angina, oral ulcers, or any combination thereof. In some embodiments, the biological sample comprises a saliva sample. In some embodiments, the biological sample comprises an oral tissue sample. In some embodiments, the oral tissue sample comprises one or more of an oral swab sample, an oral rinse sample, an oral brush biopsy sample, an oral mucosal tissue biopsy sample, a tongue scraping sample, a dental plaque sample, a gingival tissue sample, a gingival crevicular fluid sample, a jawbone sample, a tooth fragment sample, a dental pulp sample, a periapical sample, a tooth root tissue sample, a periodontal pocket sample, a buccal cells sample, or a periodontal ligament sample, or any combination thereof. In some embodiments, the oral point of care location comprises a dental practice, an orthodontic practice, an endodontic practice, an oral surgeon practice, a maxillofacial surgeon practice, a periodontal practice, or a prosthodontia practice. In some embodiments, the derivative of the biological sample comprises one or more of microbial cells, DNA, methylated DNA, ctDNA, RNA, mRNA, miRNA, IncRNA, sRNA, siRNA, snRNA, sncRNA, circulating tumor cell RNA, proteins, enzymes, lipids, hormones, metabolites, cytokines, antigens, antibodies, exosomes, extracellular vesicles, or cancer antigens, or any combination thereof. In some embodiments, detecting the panel of biomarkers further comprises measuring an amount of the detected panel of biomarkers, measuring a frequency of the detected panel of biomarkers, measuring a concentration of the detected panel of biomarkers, generating a comparative analysis of theDocket No.: 68417-702.601 detected panel of biomarkers, or mapping patterns of the detected panel of biomarkers, or any combination thereof. In some embodiments, mapping patterns of the detected panel of biomarkers comprises mapping DNA methylation patterns or DNA fusion patterns of the detected panel of biomarkers to the biological sample. In some embodiments, mapping patterns of the detected panel of biomarkers comprises mapping amounts of the detected panel of biomarkers over areas of the biological sample or the derivative thereof. In some embodiments, the method further comprises storing the detected panel of biomarkers in a database of the remote sample processing location. In some embodiments, the method further comprises generating as part of the disease risk profile of the subject a prediction of the subject’s response to a treatment. In some embodiments, the method further comprises generating the disease risk profile for the subject based at least in part on the detected panel of biomarkers of the subject in comparison to a plurality of stored reference detected panels of biomarkers. In some embodiments, the stored reference detected panels of biomarkers comprise previously detected panels of biomarkers stored on a database.

[0016] In yet another aspect, provided herein are methods of point of care testing to identify a disease in a patient, said methods comprising: enriching a deoxyribonucleic acid (DNA) sample derived from the patient with a reagent set, wherein the reagent set comprises a panel of biomarkers, thereby generating a plurality of amplicons or derivatives thereof, wherein the panel of biomarkers comprises at least one biomarker selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAP1; and sequencing the plurality of amplicons or derivatives thereof to detect the panel of biomarkers, thereby identifying the disease.

[0017] Disclosed herein in some embodiments are methods, systems, and tools for predictively diagnosing diseases or conditions, predictively monitoring diseases or conditions, and predicting treatment efficacy of diseases or conditions. Disclosed herein in one embodiment is a method of identifying a subject as having or being at an increased risk of having a disease, comprising processing a biological sample obtained from an oral cavity of the subject at an oral point of care location to generate a report identifying the subject as having or being at an increased risk of having the disease at an accuracy of at least 60%, wherein the biological sample is processed at a location that is remote from the oral point of care location.

[0018] Disclosed herein in another embodiment is a method of generating a disease risk profile for a subject, comprising: (a) obtaining a biological sample from an oral cavity of a subject, wherein the biological sample is obtained at an oral point of care location; (b) depositing the biological sample at the oral point of care location in a sample collection tube comprising a sample preservation reagent or a sample processing reagent, or both; (c) transferring the sampleDocket No.: 68417-702.601 collection tube comprising the biological sample sealed therein to a remote sample processing location, wherein the remote sample processing location processes the biological sample or a derivative thereof to generate the disease risk profile for the subject; and (d) transmitting to the oral point of care location or a healthcare professional location the generated disease risk profile for the subject, wherein the disease risk profile identifies a risk of the subject for having or developing the disease.

[0019] In some embodiments, the disease risk profile identifies the risk at an accuracy of at least 60%. In some embodiments, the disease risk profile comprises one or more of a diagnostic risk prediction, a development risk prediction, a severity risk prediction, or an accuracy prediction of the subject for having the disease. In some embodiments, the diagnostic prediction comprises one or more of a binary indication for the subject having the disease, a risk value for the subject having the disease, or a risk level for the subject having the disease. In some embodiments, the development risk prediction comprises one or more of a binary indication for the subject developing the disease, a risk value for the subject developing the disease, or a risk level for the subject developing the disease. In some embodiments, the severity risk prediction comprises one or more of a binary indication for the subject having a severe state of the disease, a binary indication for the subject developing a severe state of the disease, a risk value for the subject having a severe state of the disease, a risk value for the subject developing a severe state of the disease, a value quantifying predicted severity of the disease, or a categorization of predicted severity of the disease. In some embodiments, the risk level comprises one or more of a zero level, low level, medium level, high level, or very high level. In some embodiments, the accuracy prediction comprises a value predicting the accuracy of one or more of the diagnostic risk prediction, the development risk prediction, or the severity risk prediction.

[0020] In some embodiments, the disease comprises cancer. In some embodiments, the cancer is an oral cancer. In some embodiments, the cancer is a non-oral cancer. In some embodiments, In some embodiments, the non-oral cancer comprises one or more of: acute lymphoblastic leukemia, acute myeloid leukemia, adenoid cystic carcinoma, adrenocortical carcinoma, anal cancer, appendix cancer, astrocytoma, basal cell carcinoma, bile duct cancer, bladder cancer, bone cancer, brain stem glioma, brain tumor, breast cancer, bronchial tumors, Burkitt lymphoma, carcinoid tumors, cervical cancer, cholangiocarcinoma, chondrosarcoma, chronic lymphocytic leukemia, chronic myeloid leukemia, colon cancer, colorectal cancer, craniopharyngioma, cutaneous T-cell lymphoma, ductal carcinoma in situ, endometrial cancer, esophageal cancer, Ewing family of tumors, eyelid carcinoma, fallopian tube cancer, fibrosarcoma, gallbladder cancer, gastric cancer, gastrointestinal carcinoid tumors, gastrointestinal stromal tumor, germ cell tumor, glioblastoma, glioma, hairy cell leukemia, head and neck cancer, HodgkinDocket No.: 68417-702.601 lymphoma, hypopharyngeal cancer, inflammatory breast cancer, invasive ductal carcinoma, kidney cancer, laryngeal cancer, lip cancer, liver cancer, lung cancer, lymphoma, malignant fibrous histiocytoma, malignant melanoma, medulloblastoma, Merkel cell carcinoma, mesothelioma, metastatic squamous neck cancer, mouth cancer, multiple myeloma, mycosis fungoides, nasal cavity and paranasal sinus cancer, nasopharyngeal cancer, neuroblastoma, nonHodgkin lymphoma, ocular melanoma, oropharyngeal cancer, ovarian cancer, pancreatic cancer, papillary thyroid cancer, parathyroid cancer, penile cancer, pituitary tumor, prostate cancer, rectal cancer, renal cell carcinoma, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcoma, sebaceous carcinoma, skin cancer, small cell lung cancer, small intestine cancer, soft tissue sarcoma, squamous cell carcinoma, stomach cancer, testicular cancer, throat cancer, thymoma and thymic carcinoma, thyroid cancer, uterine cancer, uterine sarcoma, vaginal cancer, vulvar cancer, Waldenstrom macroglobulinemia, or Wilms tumor, or any combination thereof. In some embodiments, the oral disease is periodontal disease. In some embodiments, the oral disease comprises one or more of caries, halitosis, gingivitis, periodontitis, stomatitis, oral thrush, oral herpes, salivary gland infections, dental abscess, dental erosion, dental fluorosis, dry mouth syndrome, hand, foot, and mouth disease, oral leukoplakia, oral lichen planus, candidiasis, canker sores, oral cysts, jaw disorders, mumps, oral papilloma, pemphigus vulgaris, pericoronitis, tooth decay, toothache, trench mouth, Vincent’s angina, oral ulcers, or any combination thereof. In some embodiments, the disease comprises an infectious disease. In some embodiments, the disease comprises a non-oral disease. In some embodiments, the disease comprises multiple diseases or conditions.

[0021] In some embodiments, the biological sample comprises a saliva sample. In some embodiments, the biological sample comprises an oral tissue sample. In some embodiments, the oral tissue sample comprises one or more of an oral swab sample, an oral rinse sample, an oral brush biopsy sample, an oral mucosal tissue biopsy sample, a tongue scraping sample, a dental plaque sample, a gingival tissue sample, a gingival crevicular fluid sample, a jawbone sample, a tooth fragment sample, a dental pulp sample, a periapical sample, a tooth root tissue sample, a periodontal pocket sample, a buccal cells sample, or a periodontal ligament sample, or any combination thereof.

[0022] In some embodiments, the oral point of care location comprises a dental practice, an orthodontic practice, an endodontic practice, an oral surgeon practice, a maxillofacial surgeon practice, a periodontal practice, or a prosthodontia practice.

[0023] In some embodiments, the sample collection tube is part of a kit. In some embodiments, the kit comprises a diagnostic kit. In some embodiments, the sample preservation reagent or the sample processing reagent, or both, are compatible with one or more subtypes of the biologicalDocket No.: 68417-702.601 samples. In some embodiments, the sample preservation agent comprises a formulan-free preservation agent. In some embodiments, transferring the sample collection tube to the remote sample processing location comprises placing the sample collection tube in an outer packaging at the oral point of care location. In some embodiments, transferring the sample collection tube to the remote sample processing location further comprises shipping the sample collection tube in the outer packaging from the oral point of care location to the remote sample processing location.

[0024] In some embodiments, the derivative of the biological sample comprises one or more of microbial cells, DNA, methylated DNA, ctDNA, RNA, mRNA, miRNA, IncRNA, sRNA, siRNA, snRNA, sncRNA, circulating tumor cell RNA, proteins, enzymes, lipids, hormones, metabolites, cytokines, antigens, antibodies, exosomes, extracellular vesicles, or cancer antigens, or any combination thereof. In some embodiments, processing the biological sample or the derivative thereof at the remote sample processing location comprises generating a biomarker profile of the subject. In some embodiments, generating the biomarker profile of the subject comprises detecting one or more biomarkers in the biological sample or the derivative thereof. In some embodiments, generating the biomarker profile of the subject further comprises measuring an amount of the detected biomarkers, measuring a frequency of the detected biomarkers, measuring a concentration of the detected biomarkers, generating a comparative analysis of the detected biomarkers, or mapping patterns of the detected biomarkers, or any combination thereof. In some embodiments, mapping patterns of the detected biomarkers comprises mapping DNA methylation patterns. In some embodiments, mapping patterns of the detected biomarkers comprises mapping RNA fusion patterns. In some embodiments, mapping patterns of the detected biomarkers comprises mapping amounts of the detected biomarkers over areas of the biological sample or the derivative thereof. In some embodiments, generation of the biomarker profile of the subject further comprises utilizing a machine learning model to predict one or more characteristics of the subject based on the biomarker detection.

[0025] In some embodiments, the method further comprises storing the biomarker profile of the subject in a database of the remote sample processing location. In some embodiments, the method further comprises generating as part of the disease risk profile of the subject a prediction of the subject’s response to a treatment. In some embodiments, the method further comprises generating as part of the disease risk profile of the subject a prediction of the subject’s response to a subset of treatments of a plurality of treatments. In some embodiments, the subset of treatments is selected based on the disease risk profile of the subject.

[0026] In some embodiments, the method further comprises utilizing a machine learning model to select an appropriate subset of treatments based on the disease risk profile of the subject andDocket No.: 68417-702.601 disease risk profiles of others. In some embodiments, the method further comprises utilizing a machine learning model to predict the response of the subject to the selected subset of treatments. In some embodiments, the machine learning model is configured to predict the response of the subject to the selected subset of treatments based the disease risk profile of the subject. In some embodiments, the method further comprises generating as part of the disease risk profile of the subject a prediction of an optimized treatment for the subject from a plurality of treatments. In some embodiments, the optimized treatment comprises one or more treatments with high efficacy, high safety, low toxicity, low reactivity, low cross-reactivity, or another positive characteristic. In some embodiments, the optimized treatment is selected from a plurality of treatments based on the disease risk profile of the subject.

[0027] In some embodiments, the method further comprises comparing the disease risk profile of the subject to disease risk profiles of others to select the optimized treatment. In some embodiments, the method further comprises utilizing a machine learning model to predict the optimized treatment based on the disease risk profile of the subject. In some embodiments, the method further comprises generating as part of the disease risk profile of the subject a prediction of disease progression for the subject. In some embodiments, the method further comprises utilizing a machine learning model to predict the disease progression for the subject based on the disease risk profile of the subject. In some embodiments, the method further comprises generating the disease risk profile for the subject based at least in part on the biomarker profile of the subj ect.

[0028] In some embodiments, the method further comprises generating the disease risk profile for the subject based at least in part on the biomarker profile of the subject in comparison to a plurality of stored biomarker profiles. In some embodiments, the plurality of stored biomarker profiles comprise previously generated biomarker profiles stored on a database.

[0029] In some embodiments, the method further comprises generating the disease risk profile for the subject based at least in part on the biomarker profile of the subject in comparison to a subset of the plurality of stored biomarker profiles. In some embodiments, the subset of the plurality of stored biomarker profiles is selected based on similarity to one or more components of the biomarker profile of the subject. In some embodiments, transmitting to the oral point of care location or the healthcare professional location the generated disease risk profile for the subject comprises transmitting data comprising the disease risk profile over a network from the remote sample processing location to the oral point of care location or the healthcare professional location. In some embodiments, the data is stored in the database of the remote sample processing location. In some embodiments, transmitting the data to the oral point of care location or the healthcare professional location further comprises transmitting the data to aDocket No.: 68417-702.601 database at the oral point of care location or a database at the healthcare professional location. In some embodiments, transmitting to the oral point of care location or the healthcare professional location the generated disease risk profile for the subject comprises transmitting data comprising the disease risk profile to a secure user interface at the oral point of care location or at the healthcare professional location.

[0030] In some embodiments, the method further comprises displaying the generated disease risk profile, or a subset of data thereof, on a display at the oral point of care location or at the healthcare professional location.

[0031] Disclosed herein in yet another embodiment is a method of remotely generating a transmissible disease risk profile for a subject comprising: (a) receiving a sample collection tube comprising a biological sample or derivative thereof sealed therein and a sample preservation reagent or a sample processing reagent, or both, from a remote oral point of care location, wherein the biological sample originates from an oral cavity of a subject; (b) processing the biological sample or the derivative thereof to generate the disease risk profile for the subject; (c) transmitting to the oral point of care location or a healthcare professional location the generated disease risk profile for the subject, wherein the generated disease risk profile identifies a risk of the subject for having or developing the disease.

[0032] In some embodiments, the disease risk profile identifies the risk at an accuracy of at least 60%. In some embodiments, the disease risk profile comprises one or more of a diagnostic risk prediction, a development risk prediction, a severity risk prediction, or an accuracy prediction of the subject for having the disease. In some embodiments, the diagnostic prediction comprises one or more of a binary indication for the subject having the disease, a risk value for the subject having the disease, or a risk level for the subject having the disease. In some embodiments, the development risk prediction comprises one or more of a binary indication for the subject developing the disease, a risk value for the subject developing the disease, or a risk level for the subject developing the disease. In some embodiments, the severity risk prediction comprises one or more of a binary indication for the subject having a severe state of the disease, a binary indication for the subject developing a severe state of the disease, a risk value for the subject having a severe state of the disease, a risk value for the subject developing a severe state of the disease, a value quantifying predicted severity of the disease, or a categorization of predicted severity of the disease. In some embodiments, the risk level comprises one or more of a zero level, low level, medium level, high level, or very high level.

[0033] In some embodiments, the accuracy prediction comprises a value predicting the accuracy of one or more of the diagnostic risk prediction, the development risk prediction, or the severity risk prediction.Docket No.: 68417-702.601

[0034] In some embodiments, the disease comprises cancer. In some embodiments, the cancer is an oral cancer. In some embodiments, the cancer is a non-oral cancer. In some embodiments, the non-oral cancer comprises one or more of: acute lymphoblastic leukemia, acute myeloid leukemia, adenoid cystic carcinoma, adrenocortical carcinoma, anal cancer, appendix cancer, astrocytoma, basal cell carcinoma, bile duct cancer, bladder cancer, bone cancer, brain stem glioma, brain tumor, breast cancer, bronchial tumors, Burkitt lymphoma, carcinoid tumors, cervical cancer, cholangiocarcinoma, chondrosarcoma, chronic lymphocytic leukemia, chronic myeloid leukemia, colon cancer, colorectal cancer, craniopharyngioma, cutaneous T-cell lymphoma, ductal carcinoma in situ, endometrial cancer, esophageal cancer, Ewing family of tumors, eyelid carcinoma, fallopian tube cancer, fibrosarcoma, gallbladder cancer, gastric cancer, gastrointestinal carcinoid tumors, gastrointestinal stromal tumor, germ cell tumor, glioblastoma, glioma, hairy cell leukemia, head and neck cancer, Hodgkin lymphoma, hypopharyngeal cancer, inflammatory breast cancer, invasive ductal carcinoma, kidney cancer, laryngeal cancer, lip cancer, liver cancer, lung cancer, lymphoma, malignant fibrous histiocytoma, malignant melanoma, medulloblastoma, Merkel cell carcinoma, mesothelioma, metastatic squamous neck cancer, mouth cancer, multiple myeloma, mycosis fungoides, nasal cavity and paranasal sinus cancer, nasopharyngeal cancer, neuroblastoma, non-Hodgkin lymphoma, ocular melanoma, oropharyngeal cancer, ovarian cancer, pancreatic cancer, papillary thyroid cancer, parathyroid cancer, penile cancer, pituitary tumor, prostate cancer, rectal cancer, renal cell carcinoma, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcoma, sebaceous carcinoma, skin cancer, small cell lung cancer, small intestine cancer, soft tissue sarcoma, squamous cell carcinoma, stomach cancer, testicular cancer, throat cancer, thymoma and thymic carcinoma, thyroid cancer, uterine cancer, uterine sarcoma, vaginal cancer, vulvar cancer, Waldenstrom macroglobulinemia, or Wilms tumor, or any combination thereof.

[0035] In some embodiments, the disease comprises an oral disease. In some embodiments, the oral disease is periodontal disease. In some embodiments, the oral disease comprises one or more of caries, halitosis, gingivitis, periodontitis, stomatitis, oral thrush, oral herpes, salivary gland infections, dental abscess, dental erosion, dental fluorosis, dry mouth syndrome, hand, foot, and mouth disease, oral leukoplakia, oral lichen planus, candidiasis, canker sores, oral cysts, jaw disorders, mumps, oral papilloma, pemphigus vulgaris, pericoronitis, tooth decay, toothache, trench mouth, Vincent’s angina, oral ulcers, or any combination thereof. In some embodiments, the disease comprises an infectious disease. In some embodiments, the disease comprises a non- oral disease. In some embodiments, the disease comprises multiple diseases or conditions.

[0036] In some embodiments, the biological sample comprises a saliva sample. In some embodiments, the biological sample comprises an oral tissue sample. In some embodiments, theDocket No.: 68417-702.601 oral tissue sample comprises one or more of an oral swab sample, an oral rinse sample, an oral brush biopsy sample, an oral mucosal tissue biopsy sample, a tongue scraping sample, a dental plaque sample, a gingival tissue sample, a gingival crevicular fluid sample, a jawbone sample, a tooth fragment sample, a dental pulp sample, a periapical sample, a tooth root tissue sample, a periodontal pocket sample, a buccal cells sample, or a periodontal ligament sample, or any combination thereof. In some embodiments, the oral point of care location comprises a dental practice, an orthodontic practice, an endodontic practice, an oral surgeon practice, a maxillofacial surgeon practice, a periodontal practice, or a prosthodontia practice.

[0037] In some embodiments, the method is performed at a remote sample processing location. In some embodiments, the remote sample processing location comprises a laboratory location, a sequencing location, a testing location, a tissue processing location, a tissue storage location, or any combination thereof. In some embodiments, the remote sample processing location comprises a plurality of remote processing locations in communication with each other. In some embodiments, the sample collection tube is part of a kit. In some embodiments, the kit comprises a diagnostic kit. In some embodiments, the sample preservation reagent or the sample processing reagent, or both, are compatible with one or more subtypes of the biological samples. In some embodiments, the sample preservation agent comprises a formulan-free preservation agent. In some embodiments, receiving the sample collection tube comprises receiving the sample collection tube in an outer packaging. In some embodiments, the outer packaging is placed around the sample collection tube at the oral point of care location.

[0038] In some embodiments, the method further comprises removing the outer packaging prior to processing. In some embodiments, receiving the sample collection tube further comprises receiving a shipment comprising the sample collection tube from the oral point of care location. In some embodiments, the derivative of the biological sample comprises one or more of microbial cells, DNA, methylated DNA, ctDNA, RNA, mRNA, miRNA, IncRNA, sRNA, siRNA, snRNA, sncRNA, circulating tumor cell RNA, proteins, enzymes, lipids, hormones, metabolites, cytokines, antigens, antibodies, exosomes, extracellular vesicles, or cancer antigens, or any combination thereof. In some embodiments, processing the biological sample further comprises generating a biomarker profile of the subject.

[0039] In some embodiments, generating the biomarker profile of the subject comprises detecting one or more biomarkers in the biological sample or the derivative thereof. In some embodiments, generating the biomarker profile of the subject further comprises measuring an amount of the detected biomarkers, measuring a frequency of the detected biomarkers, measuring a concentration of the detected biomarkers, generating a comparative analysis of the detected biomarkers, or mapping patterns of the detected biomarkers, or any combinationDocket No.: 68417-702.601 thereof. In some embodiments, mapping patterns of the detected biomarkers comprises mapping DNA methylation patterns. In some embodiments, mapping patterns of the detected biomarkers comprises mapping RNA fusion patterns. In some embodiments, mapping patterns of the detected biomarkers comprises mapping amounts of the detected biomarkers over areas of the biological sample or the derivative thereof. In some embodiments, generation of the biomarker profile of the subject further comprises utilizing a machine learning model to predict one or more characteristics of the subject based on the biomarker detection. In some embodiments, the method further comprises storing the biomarker profile of the subject in a database.

[0040] In some embodiments, the method further comprises generating as part of the disease risk profile of the subject a prediction of the subject’s response to a treatment. In some embodiments, the method further comprises generating as part of the disease risk profile of the subject a prediction of the subject’s response to a subset of treatments of a plurality of treatments. In some embodiments, the subset of treatments is selected based on the disease risk profile of the subject. In some embodiments, the method further comprises utilizing a machine learning model to select an appropriate subset of treatments based on the disease risk profile of the subject and disease risk profiles of others. In some embodiments, the method further comprises utilizing a machine learning model to predict the response of the subject to the selected subset of treatments. In some embodiments, the machine learning model is configured to predict the response of the subject to the selected subset of treatments based the disease risk profile of the subject and disease risk profiles of others.

[0041] In some embodiments, the method further comprises generating as part of the disease risk profile of the subject a prediction of an optimized treatment for the subject from a plurality of treatments. In some embodiments, the optimized treatment comprises one or more treatments most likely to be effective against the disease of the disease risk profile for the subject. In some embodiments, the optimized treatment is selected from a plurality of treatments based on the disease risk profile of the subject.

[0042] In some embodiments, the method further comprises comparing the disease risk profile of the subject to disease risk profiles of others to select the optimized treatment. In some embodiments, the method further comprises utilizing a machine learning model to predict the optimized treatment based on the disease risk profile of the subject. In some embodiments, the method further comprises generating as part of the disease risk profile of the subject a prediction of disease progression for the subject. In some embodiments, the method further comprises utilizing a machine learning model to predict the disease progression for the subject based on the disease risk profile of the subject. In some embodiments, the method further comprises generating the disease risk profile for the subject based at least in part on the biomarker profile ofDocket No.: 68417-702.601 the subject. In some embodiments, the method further comprises generating the disease risk profile for the subject based at least in part on the biomarker profile of the subject in comparison to a plurality of stored biomarker profiles. In some embodiments, the plurality of stored biomarker profiles comprise previously generated biomarker profiles stored on a database.

[0043] In some embodiments, the method further comprises generating the disease risk profile for the subject based at least in part on the biomarker profile of the subject in comparison to a subset of the plurality of stored biomarker profiles. In some embodiments, the subset of the plurality of stored biomarker profiles is selected based on similarity to one or more components of the biomarker profile of the subject. In some embodiments, the method further comprises storing the disease risk profile for the subject in a database.

[0044] In some embodiments, transmitting to the oral point of care location or the healthcare professional location the generated disease risk profile for the subject comprises transmitting data comprising the disease risk profile over a network to the oral point of care location or the healthcare professional location. In some embodiments, transmitting the data to the oral point of care location or the healthcare professional location further comprises transmitting the data to a database at the oral point of care location or a database at the healthcare professional location. In some embodiments, transmitting to the oral point of care location or the healthcare professional location the generated disease risk profile for the subject comprises transmitting data comprising the disease risk profile to a secure user interface at the oral point of care location or at the healthcare professional location.

[0045] In yet another embodiment, the method comprises a method of point of care testing to identify a disease in a patient, said method comprising: obtaining a saliva sample from the patient; extracting a DNA sample from the saliva sample; administering a reagent set to the DNA sample, wherein the reagent set comprises a panel of biomarkers, and generating a plurality of amplicons or derivatives thereof; sequencing the plurality of amplicons to generate a report comprising the presence or absence of one or more biomarkers of the panel of biomarkers; wherein the report identifies the disease in the patient.

[0046] In some embodiments, the DNA sample comprises at least 50 ng of DNA derived from the saliva sample.

[0047] In some embodiments, the panel of biomarkers comprises at least one biomarker selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, AD API, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0048] In some embodiments, the panel of biomarkers comprises at least two biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1,Docket No.: 68417-702.601AD API, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, F0XL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0049] In some embodiments, the panel of biomarkers comprises at least three biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, AD API, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0050] In some embodiments, the panel of biomarkers comprises at least four biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, AD API, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0051] In some embodiments, the panel of biomarkers comprises at least five biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, AD API, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0052] In some embodiments, the panel of biomarkers comprises at least six biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, AD API, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0053] In some embodiments, the panel of biomarkers comprises at least seven biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, AD API, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0054] In some embodiments, the panel of biomarkers comprises at least eight biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, AD API, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0055] In some embodiments, the panel of biomarkers comprises at least nine biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, AD API, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0056] In some embodiments, the panel of biomarkers comprises at least ten biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, AD API, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.Docket No.: 68417-702.601

[0057] In some embodiments, the panel of biomarkers comprises at least eleven biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, AD API, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0058] In some embodiments, the panel of biomarkers comprises at least twelve biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, AD API, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0059] In some embodiments, the panel of biomarkers comprises. TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, ADAP1, HRAS, KRAS, and FGFR3.

[0060] In some embodiments, the panel of biomarkers comprises AKT1, BRAF, EGFR, ERBB2, FOXL2, GNA11, GNAQ, GNAS, HRAS, IDH1, IDH2, KIT, KRAS, MET, NRAS, PDGFRA, PIK3CA, RET, TERT, and TP53.

[0061] In some embodiments, the panel of biomarkers comprises AKT1, BRAF, EGFR, ERBB2, FOXL2, GNA11, GNAQ, KIT, KRAS, MET, NRAS, PDGFRA, PIK3CA, RET, and TP53.

[0062] In some embodiments, the sequencing comprises a limit of detection (“LoD”) of 0.05% for variant allele frequency (“VAF”).BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The novel features of the disclosure are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present disclosure will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the disclosure are utilized, and the accompanying drawings of which:

[0064] FIG. 1 illustrates background information concerning frequency of oral cancer location by percentage in the oral cavity.

[0065] FIG. 2 illustrates a non-limiting example of a process flow diagram depicting an example method of generating a risk disease profile remotely from an oral point of care.

[0066] FIG. 3 illustrates a non-limiting example of a list of biomarkers of DNA alteration in cancer, for example squamous cell carcinoma.

[0067] FIG. 4 illustrates a non-limiting example of a process flow diagram depicting an example conventional method of obtaining a diagnostic result from conventional visual and tactile examination without sample processing.Docket No.: 68417-702.601

[0068] FIG. 5 illustrates a non-limiting example of a process flow diagram depicting an example method of monitoring using diagnostic screening tools on samples taken at an oral point of care and remotely processed.

[0069] FIG. 6 illustrates a non-limiting example of various biomarkers of DNA alteration in cancer, for example oral carcinoma, along with percentage of a sample population with mutation of the respective biomarker.

[0070] FIG. 7 illustrates a non-limiting example of a process flow diagram depicting an example method of sampling at an oral point of care, transferring the sample to a remote sample processing location for processing, and receiving a disease risk profile at the oral point of care or at a healthcare professional location.

[0071] FIG. 8 illustrates a non-limiting example of a process flow diagram depicting an example method of receiving a biological sample from a remote point of care location, processing the sample, generating a disease risk profile, and transmitting the disease risk profile to the oral point of care location or to a healthcare professional location.

[0072] FIG. 9 illustrates a non-limiting example of a process flow diagram depicting an example method of generating a disease risk profile and predicting various characteristics of treatments or disease of a subject based on detected biomarker data.

[0073] FIG. 10 illustrates a non-limiting example of a computing device; in this case, a device with one or more processors, memory, storage, and a network interface, per one or more embodiments herein.

[0074] FIG. 11 illustrates a non-limiting example of a process flow diagram depicting a sample collection to sample analysis results reporting process.DETAILED DESCRIPTION

[0075] While preferable embodiments of the invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention.Terms and Definitions

[0076] Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.Docket No.: 68417-702.601

[0077] As used herein, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. Any reference to “or” herein is intended to encompass “and / or” unless otherwise stated.

[0078] As used herein, the term “about” in some cases refers to an amount that is approximately the stated amount, in some cases near the stated amount by 10%, 5%, or 1%, including increments therein, and in some cases, in reference to a percentage, refers to an amount that is greater or less the stated percentage by 10%, 5%, or 1%, including increments therein.

[0079] As used herein, the phrases “at least one,” “one or more,” and “and / or” are open-ended expressions that are both conjunctive and disjunctive in operation. For example, each of the expressions “at least one of A, B and C,” “at least one of A, B, or C,” “one or more of A, B, and C”, “one or more of A, B, or C” and “A, B, and / or C” means A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B and C together. As used herein, the phrase “at most three” may mean less than one, one, two, or three.

[0080] Reference throughout this specification to “some embodiments,” “further embodiments,” or “a particular embodiment,” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrase “in some embodiments,” or “in further embodiments,” or “in a particular embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0081] The terms "subject," "individual," and "patient" may be used interchangeably and refer to humans, as well as non-human mammals (e.g., non-human primates, canines, equines, felines, porcines, bovines, ungulates, lagomorphs, rodents, and the like). In various embodiments, the subject may be a human (e.g., adult male, adult female, adolescent male, adolescent female, male child, female child) under the care of a physician or other health worker in a hospital, as an outpatient, or other clinical context. In certain embodiments, the subject may not be under the care or prescription of a physician or other health worker. In some embodiments, the subject may be under the care of a dental professional.

[0082] As used herein, “treatment” or “treating” refers to an approach for obtaining beneficial or desired results with respect to a disease, disorder, or medical condition including, but not limited to, a therapeutic benefit and / or a prophylactic benefit. In certain embodiments, treatment or treating involves administering a therapeutic to a subject. A therapeutic benefit may include the eradication or amelioration of the underlying disorder being treated. Also, a therapeutic benefit may be achieved with the eradication or amelioration of one or more of the physiologicalDocket No.: 68417-702.601 symptoms associated with the underlying disorder, such as observing an improvement in the subject, notwithstanding that the subject may still be afflicted with the underlying disorder.

[0083] As used herein, the term “specificity” refers to the ability of a method to correctly reject the presence of a disease in a subject (e.g., the specificity of a method can be described as the ability of the method to identify the true negative rate or probability of correctly determining that a condition does not exist in a subject. For example, when used in reference to any of the variety of methods described herein that can detect the presence of cancer in a subject, a high specificity means that the method correctly identifies the absence of cancer in the subject a large percentage of the time (e.g., the method does not incorrectly identify the presence of cancer in the subject a large percentage of the time). For example, a method described herein that correctly detects the absence of cancer in a subject 95% of the time the method is performed is said to have a specificity of 95%. In some embodiments, a method described herein that can detect the absence of cancer in a subject provides a specificity of at least 90% (e.g., at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.5%, 99.9%, 99.99%, or higher).

[0084] As used herein, the term “sensitivity” refers to the ability of a method to correctly identify or diagnose the presence of a disease in a subject (e.g., the sensitivity of a method can be described as the ability of the method to identify the true positive rate or probability of detecting a condition in a subject). For example, when used in reference to any of the variety of methods described herein that can detect the presence of a cancer in a subject, a high sensitivity means that the method correctly identifies the presence of cancer in the subject a large percentage of the time. For example, a method described herein that correctly detects the presence of cancer in a subject 95% of the time the method is performed is said to have a sensitivity of 95%. In some embodiments, a method described herein that can detect the presence of cancer in a subject provides a sensitivity of at least 80% (e.g., at least 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.5%, 99.7%, 99.9%, or more than 99.9%). In some embodiments, methods provided herein that include detecting the presence of one or more biomarkers of a panel of biomarkers.Identifying a Disease in a Patient

[0085] In an aspect, described herein may be methods of point of care testing to identify a disease in a patient, said method comprising: obtaining a saliva sample from the patient; extracting a deoxyribonucleic acid (DNA) sample from the saliva sample; enriching the DNA sample with a reagent set, wherein the reagent set may comprise a panel of biomarkers, thereby generating a plurality of amplicons or derivatives thereof, wherein the panel of biomarkers may comprise at least one biomarker selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA,Docket No.: 68417-702.601FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAP1; sequencing the plurality of amplicons or derivatives thereof to detect the panel of biomarkers; and treating the disease in the patient by identifying the disease in the patient.

[0086] The panel of biomarkers may comprise at least two biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT The panel of biomarkers may comprise at least three biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT The panel of biomarkers may comprise at least four biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT The panel of biomarkers may comprise at least five biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT The panel of biomarkers may comprise at least six biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT The panel of biomarkers may comprise at least seven biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT The panel of biomarkers may comprise at least eight biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT The panel of biomarkers may comprise at least nine biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT The panel of biomarkers may comprise at least ten biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT The panel of biomarkers may comprise at least eleven biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT The panel of biomarkers may comprise at least twelve biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT The panel of biomarkers may comprise TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT The method may further comprise identifying the disease utilizing the panel of biomarkers. The disease may comprise cancer. The cancer may be an oral cancer. The oral cancer may comprise a cancer of the oral cavity. The oral cancer may comprise one or more of: squamous cell carcinoma (SCC), salivary gland tumors, lymphoma, melanoma, sarcoma, or odontogenic tumors, or any combination thereof. The disease may comprise an oral disease. The oral disease may be periodontal disease. The oral disease may comprise one or more of caries, halitosis, gingivitis, periodontitis, stomatitis, oral thrush, oral herpes, salivary gland infections, dental abscess, dental erosion, dental fluorosis, dry mouth syndrome, hand, foot, and mouthDocket No.: 68417-702.601 disease, oral leukoplakia, oral lichen planus, candidiasis, canker sores, oral cysts, jaw disorders, mumps, oral papilloma, pemphigus vulgaris, pericoronitis, tooth decay, toothache, trench mouth, Vincent’s angina, oral ulcers, or any combination thereof.

[0087] The treatment may comprise a treatment capable of treating the disease. The treatment may comprise a kinase inhibitor, an immune checkpoint inhibitor (e.g., a PD-1, a PD-L1, and / or a CTLA-4 immune checkpoint inhibitor), a chemotherapeutic agent, adoptive T cell therapy (e.g., chimeric antigen receptors and / or T cells having wild-type or modified T cell receptors), an antibody, a bispecific antibody or fragments thereof (e.g., BiTEs), chemotherapy, adjuvant chemotherapy, neoadjuvant chemotherapy, cytotoxic therapy, hormone therapy, immunotherapy, a monoclonal antibody, radiation therapy, signal transduction inhibitors, surgery (e.g., surgical resection), a targeted therapy such as administration of kinase inhibitors (e.g., kinase inhibitors that target a particular genetic lesion, such as a translocation or mutation), or any combination of thereof. Such therapeutic interventions may be administered alone or in combination. In some embodiments of any of the methods described herein, the one or more therapeutic interventions are administered sequentially or simultaneously to the subject after the cancer cell has been detected. In some embodiments, the therapeutic intervention may be administered at a time when the subject has an early-stage cancer, and wherein the therapeutic intervention is more effective that if the therapeutic intervention were to be administered to a subject at a later time. In some embodiments, a therapeutic intervention may reduce the severity of the cancer, reduce a symptom of the cancer, and / or to reduce the number of cancer cells present within the subject.

[0088] Also provided herein are treatments comprising one or more therapeutic interventions utilized alone or in combination, such as various surgeries, targeted interventions, chemotherapy treatments, and radiation treatments as described in PCT Publication No. WO 2018 / 204657, incorporated by reference in its entirety herein.

[0089] The treatment may comprise a treatment capable of treating the disease. The treatment may comprise one or more of: a chemotherapy, radiation, an immunotherapy, or a surgery. The treatment may comprise one or more of: intensity modulated radiation therapy (IMRT), proton therapy, stereotactic body radiation therapy (SBRT), stereotactic radiosurgery (SRS), or any combination thereof. The treatment may comprise one or more targeted therapies. The treatment may comprise one or more immune checkpoint inhibitors. The treatment may comprise administering one or more cytotoxic chemotherapy agents. The treatment may comprise a treatment capable of treating the cancer. The treatment may comprise one or more of: a chemotherapy, radiation, an immunotherapy, or a surgery. The treatment may comprise one or more of: intensity modulated radiation therapy (IMRT), proton therapy, stereotactic body radiation therapy (SBRT), stereotactic radiosurgery (SRS), or any combination thereof. TheDocket No.: 68417-702.601 treatment may comprise one or more targeted therapies. The treatment may comprise one or more immune checkpoint inhibitors. The treatment may comprise administering one or more cytotoxic chemotherapy agents. The treatment may comprise a treatment capable of treating the oral cancer. The treatment may comprise one or more of: a chemotherapy, radiation, an immunotherapy, or a surgery. The treatment may comprise one or more of: intensity modulated radiation therapy (IMRT), proton therapy, stereotactic body radiation therapy (SBRT), stereotactic radiosurgery (SRS), or any combination thereof. The treatment may comprise one or more targeted therapies. The treatment may comprise one or more immune checkpoint inhibitors. The treatment may comprise administering one or more cytotoxic chemotherapy agents. In some embodiments, identifying the disease may further comprise measuring an amount of the detected panel of biomarkers, measuring a frequency of the detected panel of biomarkers, measuring a concentration of the detected panel of biomarkers, generating a comparative analysis of the detected panel of biomarkers, or mapping patterns of the detected panel of biomarkers, or any combination thereof. In some embodiments, mapping patterns of the detected panel of biomarkers may comprise mapping DNA methylation patterns of the detected panel of biomarkers to the enriched . In some embodiments, mapping patterns of the detected panel of biomarkers may comprise mapping DNA fusion patterns. In some embodiments, mapping patterns of the detected panel of biomarkers may comprise mapping amounts of the one or more biomarkers of the detected panel of biomarkers over areas of the DNA sample or a derivative thereof. The method may further comprise utilizing a machine learning model to identify the disease in the patient based at least in part on the detected panel of biomarkers. The method may further comprise generating a prediction of the patient’s response to the treatment of the disease based at least in part on the detected panel of biomarkers. The disease may comprise a cancer. The treatment of the disease may comprise a treatment responsive to the cancer. The treatment may comprise one or more of: a chemotherapy, radiation, an immunotherapy, or a surgery. The treatment may comprise one or more of: intensity modulated radiation therapy (IMRT), proton therapy, stereotactic body radiation therapy (SBRT), stereotactic radiosurgery (SRS), or any combination thereof. The treatment may comprise one or more targeted therapies. The treatment may comprise one or more immune checkpoint inhibitors. The treatment may comprise administering one or more cytotoxic chemotherapy agents. The cancer may be an oral cancer. The method may further comprise utilizing the machine learning model to select one or more treatment options based at least in part on the prediction of the patient’s response to the treatment of the disease. The treatment of the disease may comprise a plurality of treatment options. The method may further comprise utilizing the machine learning model to select a subset of the plurality of treatment options. The selected subset may comprise one or more treatments withDocket No.: 68417-702.601 high efficacy, high safety, low toxicity, low reactivity, low cross-reactivity, or any combination thereof.

[0090] In another aspect, described herein may be methods of point of care testing to identify a disease in a patient, said method comprising: obtaining a saliva sample from the patient; extracting a DNA sample from the saliva sample; administering a reagent set to the DNA sample, wherein the reagent set may comprise a panel of biomarkers, and generating a plurality of amplicons or derivatives thereof; sequencing the plurality of amplicons to generate a report may comprise the presence or absence of one or more biomarkers of the panel of biomarkers; wherein the report identifies the disease in the patient. The disease may be identified in the patient by the method with an accuracy of at least about 70%. The disease may be identified in the patient by the method with an accuracy of at least about 75%., 80%, 85%, 90%, 95%, 06%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or more than about 99.9%. The disease may be identified in the patient by the method with a sensitivity of at least about 70%. The disease may be identified in the patient by the method with a sensitivity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%. The sensitivity may be determined by determining a value or percentage of correct identifications of disease of one or more reference subjects with similar characteristics to the patient. The disease may be identified in the patient by the method with a specificity of at least about 70%. The disease may be identified in the patient by the method with a specificity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%.

[0091] The panel of biomarkers may comprise at least one biomarker selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT

[0092] In yet another aspect, described herein may be methods of point of care testing to identify a disease in a patient, said method comprising: obtaining a saliva sample from the patient; extracting a DNA sample from the saliva sample; enriching the DNA sample with a reagent set, wherein the reagent set may comprise a panel of biomarkers, thereby generating a plurality of amplicons or derivatives thereof, wherein the panel of biomarkers may comprise at least one biomarker selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAP1; sequencing the plurality of amplicons or derivatives thereof to detect the panel of biomarkers, thereby identifying the disease in the patient. The panel of biomarkers may be detected by the method with an accuracy of at least about 70%. The panel of biomarkers may be detected by the method with an accuracy of at least about 75%., 80%, 85%, 90%, 95%, 06%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or more than about 99.9%. The panel of biomarkers may be detected by the method with a sensitivity of atDocket No.: 68417-702.601 least about 70%. The panel of biomarkers may be detected by the method with a sensitivity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%. The sensitivity may be determined by determining a value or percentage of correct identifications of a panel of biomarkers of one or more reference subjects with similar characteristics to the patient. The panel of biomarkers may be detected by the method with a specificity of at least about 70%. The panel of biomarkers may be detected by the method with a specificity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%. The disease may be identified in the patient by the method with an accuracy of at least about 70%. The disease may be identified in the patient by the method with an accuracy of at least about 75%., 80%, 85%, 90%, 95%, 06%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or more than about 99.9%. The disease may be identified in the patient by the method with a sensitivity of at least about 70%. The disease may be identified in the patient by the method with a sensitivity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%. The sensitivity may be determined by determining a value or percentage of correct identifications of disease of one or more reference subjects with similar characteristics to the patient. The disease may be identified in the patient by the method with a specificity of at least about 70%. The disease may be identified in the patient by the method with a specificity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%. The panel of biomarkers may comprise at least two biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT

[0093] The panel of biomarkers may comprise at least three biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT The panel of biomarkers may comprise at least four biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT The panel of biomarkers may comprise at least five biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT The panel of biomarkers may comprise at least six biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT The panel of biomarkers may comprise at least seven biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT The panel of biomarkers may comprise at least eight biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT The panel of biomarkers may comprise at least nine biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT The panel of biomarkers may comprise at least tenDocket No.: 68417-702.601 biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAP1. The panel of biomarkers may comprise at least eleven biomarkers selected from TP53, N0TCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT The panel of biomarkers may comprise at least twelve biomarkers selected from TP53, N0TCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT The panel of biomarkers may comprise TP53, N0TCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT The disease may comprise cancer. The cancer may be an oral cancer. The oral cancer may comprise a cancer of the oral cavity. The oral cancer may comprise one or more of: squamous cell carcinoma (SCC), salivary gland tumors, lymphoma, melanoma, sarcoma, or odontogenic tumors, or any combination thereof. The disease may comprise an oral disease. The oral disease may be periodontal disease. The oral disease may comprise one or more of caries, halitosis, gingivitis, periodontitis, stomatitis, oral thrush, oral herpes, salivary gland infections, dental abscess, dental erosion, dental fluorosis, dry mouth syndrome, hand, foot, and mouth disease, oral leukoplakia, oral lichen planus, candidiasis, canker sores, oral cysts, jaw disorders, mumps, oral papilloma, pemphigus vulgaris, pericoronitis, tooth decay, toothache, trench mouth, Vincent’s angina, oral ulcers, or any combination thereof. The method may further comprise determining treatment responsive to the disease and capable of treating the disease. The treatment responsive to the disease and capable of treating the disease may be determined by the method with an accuracy of at least about 70%. The treatment responsive to the disease and capable of treating the disease may be determined by the method with an accuracy of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%. The accuracy may comprise a value such as a percentage determined by evaluating a treatment efficacy for the treatment relating to a reference cohort of one or more diseases corresponding to the disease of the patient. The accuracy may comprise a value such as a percentage determined by evaluating treatment efficacy of the treatment in the patient or a plurality of reference subjects having a disease of the patient. The treatment may comprise one or more of: a chemotherapy, radiation, an immunotherapy, or a surgery. The treatment may comprise one or more of: intensity modulated radiation therapy (IMRT), proton therapy, stereotactic body radiation therapy (SBRT), stereotactic radiosurgery (SRS), or any combination thereof. The treatment may comprise one or more targeted therapies. The treatment may comprise one or more immune checkpoint inhibitors. The treatment may comprise administering one or more cytotoxic chemotherapy agents. The treatment may comprise a treatment responsive to cancer and capable of treating the cancer. The treatment capable of treating the cancer may comprise one or more of: a chemotherapy, radiation, an immunotherapy, or a surgery. The treatment may comprise aDocket No.: 68417-702.601 treatment responsive to an oral cancer and capable of treating the oral cancer. The treatment may comprise one or more of: intensity modulated radiation therapy (IMRT), proton therapy, stereotactic body radiation therapy (SBRT), stereotactic radiosurgery (SRS), or any combination thereof. The treatment may comprise one or more targeted therapies. The treatment may comprise one or more immune checkpoint inhibitors. The treatment may comprise administering one or more cytotoxic chemotherapy agents. The treatment capable of treating the oral cancer may comprise one or more of: a chemotherapy, radiation, an immunotherapy, or a surgery. The treatment may comprise one or more of: intensity modulated radiation therapy (IMRT), proton therapy, stereotactic body radiation therapy (SBRT), stereotactic radiosurgery (SRS), or any combination thereof. The treatment may comprise one or more targeted therapies. The treatment may comprise one or more immune checkpoint inhibitors. The treatment may comprise administering one or more cytotoxic chemotherapy agents. In some embodiments, identifying the disease may further comprise measuring an amount of the detected panel of biomarkers, measuring a frequency of the detected panel of biomarkers, measuring a concentration of the detected panel of biomarkers, generating a comparative analysis of the detected panel of biomarkers, or mapping patterns of the detected panel of biomarkers, or any combination thereof. In some embodiments, mapping patterns of the detected panel of biomarkers may comprise mapping DNA methylation patterns of the detected panel of biomarkers to the enriched. In some embodiments, mapping patterns of the detected panel of biomarkers may comprise mapping DNA fusion patterns. In some embodiments, mapping patterns of the detected panel of biomarkers may comprise mapping amounts of the one or more biomarkers of the detected panel of biomarkers over areas of the DNA sample or a derivative thereof. The method may further comprise utilizing a machine learning model to identify the disease in the patient based at least in part on the detected panel of biomarkers. The panel of biomarkers may be detected by the method with an accuracy of at least about 70%. The panel of biomarkers may be detected by the method with an accuracy of at least about 75%., 80%, 85%, 90%, 95%, 06%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or more than about 99.9%. The panel of biomarkers may be detected by the method with a sensitivity of at least about 70%. The panel of biomarkers may be detected by the method with a sensitivity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%. The sensitivity may be determined by determining a value or percentage of correct identifications of a panel of biomarkers of one or more reference subjects with similar characteristics to the patient. The panel of biomarkers may be detected by the method with a specificity of at least about 70%. The panel of biomarkers may be detected by the method with a specificity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%. The disease may beDocket No.: 68417-702.601 identified in the patient by the method with an accuracy of at least about 70%. The disease may be identified in the patient by the method with an accuracy of at least about 75%., 80%, 85%, 90%, 95%, 06%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or more than about 99.9%. The disease may be identified in the patient by the method with a sensitivity of at least about 70%. The disease may be identified in the patient by the method with a sensitivity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%. The sensitivity may be determined by determining a value or percentage of correct identifications of disease of one or more reference subjects with similar characteristics to the patient. The disease may be identified in the patient by the method with a specificity of at least about 70%. The disease may be identified in the patient by the method with a specificity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%.

[0094] In yet another aspect, described herein may be methods of identifying a subject as having or being at an increased risk of having a disease, may comprise processing a biological sample obtained from an oral cavity of the subject at an oral point of care location to generate a report identifying the subject as having or being at an increased risk of having the disease at an accuracy of at least 60%, wherein the biological sample may be processed at a location that may be remote from the oral point of care location. The identification of the subject as having or being at increased risk of having a disease may be determined by the method with an accuracy of at least about 70%. The identification of the subject as having or being at increased risk of having a disease may be determined by the method with an accuracy of at least about 75%., 80%, 85%, 90%, 95%, 06%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or more than about 99.9%. The identification of the subject as having or being at increased risk of having a disease may be determined by the method with a sensitivity of at least about 70%. The identification of the subject as having or being at increased risk of having a disease may be determined by the method with a sensitivity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%. The sensitivity may be determined by determining a risk of disease or a presence of a disease of one or more reference subjects with similar characteristics to the patient as compared to a confirmed increased risk or confirmed presence of the disease in the one or more reference subjects. The identification of the subject as having or being at increased risk of having a disease may be determined by the method with a specificity of at least about 70%. The identification of the subject as having or being at increased risk of having a disease may be determined by the method with a specificity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%.

[0095] In yet another aspect, described herein may be methods of generating a disease risk profile for a subject, comprising: (a) obtaining a biological sample from an oral cavity of aDocket No.: 68417-702.601 subject, wherein the biological sample may be obtained at an oral point of care location; (b) depositing the biological sample at the oral point of care location in a sample collection tube may comprise a sample preservation reagent or a sample processing reagent, or both; (c) transferring the sample collection tube may comprise the biological sample sealed therein to a remote sample processing location, wherein the remote sample processing location processes the biological sample or a derivative thereof to detect a panel of biomarkers of the disease risk profile for the subject, wherein the panel of biomarkers may comprise at least one biomarker selected from TP53, N0TCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAP1; and (d) transmitting to the oral point of care location or a healthcare professional location the generated disease risk profile for the subject, wherein the disease risk profile identifies a risk of the subject for having or developing the disease.

[0096] In some embodiments, methods and materials provided herein provide high sensitivity in the detection or diagnosis of cancer (e.g., a high frequency or incidence of correctly identifying a subject as having cancer). The cancer may be an oral cancer. In some embodiments, methods and materials provided herein provide a sensitivity of at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, at least about 99.5%, at least about 99.9%, at least about 99.99%, or more than 99.99%. In some embodiments, methods and materials provided herein provide high sensitivity in detecting a single type of cancer. In some embodiments, methods and materials provided herein provide high sensitivity in detecting oral cancers. Any of a variety of cancer types can be detected using methods and materials provided herein. In some embodiments, cancers that can be detected using methods and materials provided herein include oral cancer. In some embodiments, cancers that can be detected using methods and materials provided herein include liver cancer, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer, or breast cancer.

[0097] The disease risk profile identifies the risk at an accuracy of at least 60%. The disease risk profile may comprise one or more of a diagnostic risk prediction, a development risk prediction, a severity risk prediction, or an accuracy prediction of the subject for having the disease. The diagnostic prediction may comprise one or more of a binary indication for the subject having the disease, a risk value for the subject having the disease, or a risk level for the subject having the disease. The development risk prediction may comprise one or more of a binary indication for the subject developing the disease, a risk value for the subject developing the disease, or a risk level for the subject developing the disease. The severity risk prediction may comprise one or more of a binary indication for the subject having a severe state of the disease, a binaryDocket No.: 68417-702.601 indication for the subject developing a severe state of the disease, a risk value for the subject having a severe state of the disease, a risk value for the subject developing a severe state of the disease, a value quantifying predicted severity of the disease, or a categorization of predicted severity of the disease. The risk level may comprise one or more of a zero level, low level, medium level, high level, or very high level. The accuracy prediction may comprise a value predicting the accuracy of one or more of the diagnostic risk prediction, the development risk prediction, or the severity risk prediction. The disease may comprise cancer. The cancer may be an oral cancer. The disease may comprise an oral disease. The oral disease may be periodontal disease. The oral disease may comprise one or more of caries, halitosis, gingivitis, periodontitis, stomatitis, oral thrush, oral herpes, salivary gland infections, dental abscess, dental erosion, dental fluorosis, dry mouth syndrome, hand, foot, and mouth disease, oral leukoplakia, oral lichen planus, candidiasis, canker sores, oral cysts, jaw disorders, mumps, oral papilloma, pemphigus vulgaris, pericoronitis, tooth decay, toothache, trench mouth, Vincent’s angina, oral ulcers, or any combination thereof. The biological sample may comprise a saliva sample. The biological sample may comprise an oral tissue sample. The oral tissue sample may comprise one or more of an oral swab sample, an oral rinse sample, an oral brush biopsy sample, an oral mucosal tissue biopsy sample, a tongue scraping sample, a dental plaque sample, a gingival tissue sample, a gingival crevicular fluid sample, a jawbone sample, a tooth fragment sample, a dental pulp sample, a periapical sample, a tooth root tissue sample, a periodontal pocket sample, a buccal cells sample, or a periodontal ligament sample, or any combination thereof. The oral point of care location may comprise a dental practice, an orthodontic practice, an endodontic practice, an oral surgeon practice, a maxillofacial surgeon practice, a periodontal practice, or a prosthodontia practice. The derivative of the biological sample may comprise one or more of microbial cells, DNA, methylated DNA, ctDNA, RNA, mRNA, miRNA, IncRNA, sRNA, siRNA, snRNA, sncRNA, circulating tumor cell RNA, proteins, enzymes, lipids, hormones, metabolites, cytokines, antigens, antibodies, exosomes, extracellular vesicles, or cancer antigens, or any combination thereof. In some embodiments, detecting the panel of biomarkers may further comprise measuring an amount of the detected panel of biomarkers, measuring a frequency of the detected panel of biomarkers, measuring a concentration of the detected panel of biomarkers, generating a comparative analysis of the detected panel of biomarkers, or mapping patterns of the detected panel of biomarkers, or any combination thereof. In some embodiments, mapping patterns of the detected panel of biomarkers may comprise mapping DNA methylation patterns or DNA fusion patterns of the detected panel of biomarkers to the biological sample. In some embodiments, mapping patterns of the detected panel of biomarkers may comprise mapping amounts of the detected panel of biomarkers over areas of the biologicalDocket No.: 68417-702.601 sample or the derivative thereof. The method may further comprise storing the detected panel of biomarkers in a database of the remote sample processing location. The method may further comprise generating as part of the disease risk profile of the subject a prediction of the subject’s response to a treatment. The method may further comprise generating the disease risk profile for the subject based at least in part on the detected panel of biomarkers of the subject in comparison to a plurality of stored reference detected panels of biomarkers. The stored reference detected panels of biomarkers may comprise previously detected panels of biomarkers stored on a database.

[0098] In yet another aspect, provided herein are methods of point of care testing to identify a disease in a patient, said methods comprising: enriching a deoxyribonucleic acid (DNA) sample derived from the patient with a reagent set, wherein the reagent set comprises a panel of biomarkers, thereby generating a plurality of amplicons or derivatives thereof, wherein the panel of biomarkers comprises at least one biomarker selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAP1; and sequencing the plurality of amplicons or derivatives thereof to detect the panel of biomarkers, thereby identifying the disease.

[0099] Identifying a Subject Who Will or is Likely to Respond to a Treatment

[0100] Also provided herein are methods and materials for identifying a subject who will or is likely to respond to a treatment by detecting of one or more members (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more members) of one or more biomarker panels and / or the presence of aneuploidy in a sample obtained from the subject at an oral point of care. In some embodiments of identifying a subject who will or is likely to respond to a treatment, the presence of one or more members of one or more panels of biomarkers and / or the presence of aneuploidy are tested simultaneously (e.g., in one testing procedure, including embodiments in which the testing procedure itself may include multiple discrete test methods of systems). In some embodiments of identifying a subject who will or is likely to respond to a treatment, the presence of one or more members of one or more panels of biomarkers and / or the presence of aneuploidy are tested at a remote testing location. These can be tested in different procedures (e.g., in two or more different testing procedures conducted at two or more different time points at one or more remote testing locations, including embodiments in which the testing procedure itself may include multiple discrete test methods of systems). In some embodiments of identifying a subject who will or is likely to respond to a treatment that include either simultaneous or sequential testing (or both) at one or more remote testing locations of samples obtained at an oral point of care for the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy, the testing may be performed on a singleDocket No.: 68417-702.601 sample or may be performed on two or more different samples at a remote testing location (e.g., two or more different samples obtained from the same subject at an oral point of care).

[0101] Methods of Identifying a Subject as a Candidate for Further Diagnostic Testing

[0102] Also provided herein are methods and materials for identifying a subject as a candidate for further diagnostic testing by detecting of one or more members (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more members) of one or more classes of biomarkers and / or the presence of aneuploidy in a sample obtained from the subject at an oral point of care. In some embodiments of identifying a subject as a candidate for further diagnostic testing, the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy are tested simultaneously at a remote testing location (e.g., in one testing procedure, including embodiments in which the testing procedure itself may include multiple discrete test methods of systems at one or more remote testing locations). In some embodiments of identifying a subject as a candidate for further diagnostic testing, the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy are tested sequentially at one or more remote testing locations (e.g., in two or more different testing procedures conducted at two or more different time points, including embodiments in which the testing procedure itself may include multiple discrete test methods of systems). In some embodiments of identifying a subject as a candidate for further diagnostic testing that include either simultaneous or sequential testing (or both) at one or more remote testing locations for the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy, the testing may be performed on a single sample collected at an oral point of care or may be performed on two or more different samples collected at an oral point of care (e.g., two or more different samples obtained from the same subject at an oral point of care).Identifying a Subject Risk using Disease Risk Profile

[0103] A subject may be identified as having or being at an increased risk of having a disease. The subject may be identified as having or being at an increased risk of having a disease using a method for sample processing remotely from an oral point of care location. The method may comprise processing a biological sample. The biological sample may be obtained from an oral cavity of the subject. The biological sample may be obtained from an oral cavity of the subject at an oral point of care location. The method may comprise generating a report. The report may identify the subject as having one or more diseases. The report may identify the subject as being at an increased risk of having one or more diseases. The report may identify the subject as having one or more diseases at an accuracy of at least 60%. The report may identify the subjectDocket No.: 68417-702.601 as being at an increased risk of having one or more diseases at an accuracy of at least 60%. The report may identify the subject as having one or more diseases at an accuracy of at least 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%,78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%,94%, 95%, 96%, 97%, 98%, 99%, or 100%. The report may identify the subject as being at an increased risk of having one or more diseases at an accuracy of at least 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%,81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%,97%, 98%, 99%, or 100%. The biological sample may be processed at a location that is remote from the oral point of care location. The report may identify the subject as having one or more diseases at an accuracy of at least 60%. The report may identify the subject as being at an increased risk of having one or more diseases at an accuracy of at least 70%. The accuracy may comprise a sensitivity of identification of a cancer type of at least 70%. The sensitivity for identification of a cancer type may comprise at least 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%. The accuracy may comprise a specificity of identification of a cancer type of at least 60%. The specificity for identification of a cancer type may comprise at least 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%.Generating Disease Risk Profile from Sampling at Oral Point of Care Location

[0104] A disease risk profile for a subject may be generated by a method comprising obtaining a biological sample from an oral cavity of a subject. The biological sample may be obtained at an oral point of care location. The biological sample may be deposited from the subject at the oral point of care location. The biological sample may be deposited from the subject in a sample collection tube. The sample collection tube may comprise a sample preservation reagent. The sample collection tube may comprise a sample processing reagent. The sample collection tube may comprise both a sample preservation reagent and a sample processing reagent. The method may further comprise transferring the sample collection tube comprising the biological sample. The biological sample may be sealed in the sample collection tube. The sample collection tube may be transferred to a remote sample processing location. The remote sample processing location may process the biological sample. The remote sample processing location may process a derivative thereof the biological sample. Processing the biological sample or the derivativeDocket No.: 68417-702.601 thereof may generate the disease risk profile for the subject. The method may further comprise transmitting information to the oral point of care location. The information may comprise the generated disease risk profile for the subject. The method may further comprise transmitting information to a healthcare professional location. The information may comprise the generated disease risk profile for the subject. The method may further comprise transmitting information to both the oral point of care location and a healthcare professional location. The information may comprise the generated disease risk profile for the subject. The disease risk profile may identify a risk of the subject. The risk of the subject may be for having the disease. The risk of the subject may be for developing the disease. The risk of the subject may be for both having the disease and developing the disease.

[0105] The disease risk profile may identify the risk at an accuracy of at least 60%. The disease risk profile may identify the risk at an accuracy of at least 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%. The accuracy may comprise a sensitivity of identification of a cancer type of at least 60%. The sensitivity for identification of a cancer type may comprise at least 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%. The accuracy may comprise a specificity of identification of a cancer type of at least 60%. The specificity for identification of a cancer type may comprise at least 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%. The disease may be identified in the patient using the disease risk profile of the method with an accuracy of at least about 70%. The disease may be identified in the patient using the disease risk profile of the method with an accuracy of at least about 75%., 80%, 85%, 90%, 95%, 06%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or more than about 99.9%. The disease may be identified in the patient using the disease risk profile of the method with a sensitivity of at least about 70%. The disease may be identified in the patient using the disease risk profile of the method with a sensitivity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%. The sensitivity may be determined by determining a value or percentage of correct identifications of disease of one or more reference subjects with similar disease risk profile characteristics to the patient. The disease may be identified in the patient using the disease risk profile of the method with a specificity of at least about 70%. The disease may be identified in the patient using the disease risk profile of the method with a specificity of at least about 75%,Docket No.: 68417-702.60180%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%. The disease risk profile may comprise a diagnostic risk prediction of the subject for having the disease. The disease risk profile may comprise a development risk prediction of the subject for having the disease. The disease risk profile may comprise a severity risk prediction of the subject for having the disease. The disease risk profile may comprise an accuracy prediction of the subject for having the disease. The disease risk profile may comprise any combination of a diagnostic risk prediction of the subject for having the disease, a development risk prediction of the subject for having the disease, a severity risk prediction of the subject for having the disease, and an accuracy prediction of the subject for having the disease. The diagnostic prediction may comprise a binary indication for the subject having the disease. The diagnostic prediction may comprise a risk value for the subject having the disease. The diagnostic prediction may comprise a risk level for the subject having the disease. The diagnostic prediction may comprise any combination of a binary indication for the subject having the disease, a risk value for the subject having the disease, and a risk level for the subject having the disease.

[0106] The development risk prediction may comprise a binary indication for the subject developing the disease. The development risk prediction may comprise a risk value for the subject developing the disease. The development risk prediction may comprise a risk level for the subject developing the disease. The development risk prediction may comprise any combination of a binary indication for the subject developing the disease, a risk value for the subject developing the disease, and a risk level for the subject developing the disease. The accuracy may comprise a sensitivity of development risk of a cancer type of at least 60%. The sensitivity for identification of a cancer type may comprise at least 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%. The accuracy may comprise a specificity of development risk of a cancer type of at least 60%. The specificity for development risk of a cancer type may comprise at least 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%. The development risk of a disease may be identified in the patient by the method with an accuracy of at least about 70%. The development risk of a disease may be identified in the patient by the method with an accuracy of at least about 75%., 80%, 85%, 90%, 95%, 06%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or more than about 99.9%. The development risk of a disease may be identified in the patient by the method with a sensitivity of at least about 70%. The development risk of a disease may be identified in the patient by the method with a sensitivity of at least about 75%, 80%, 85%, 90%,Docket No.: 68417-702.60195%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%. The sensitivity may be determined by determining a value or percentage of correctly predicted reference subjects that developed a disease of a plurality of reference subjects with similar characteristics to the subject. The development risk of a disease may be identified in the patient by the method with a specificity of at least about 70%. The development risk of a disease may be identified in the patient by the method with a specificity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%.

[0107] The severity risk prediction may comprise a binary indication for the subject having a severe state of the disease. The severity risk prediction may comprise a binary indication for the subject developing a severe state of the disease. The severity risk prediction may comprise a risk value for the subject having a severe state of the disease. The severity risk prediction may comprise a risk value for the subject developing a severe state of the disease. The severity risk prediction may comprise a value quantifying predicted severity of the disease. The severity risk prediction may comprise a categorization of predicted severity of the disease. The severity risk prediction may comprise any combination of a binary indication for the subject having a severe state of the disease, a binary indication for the subject developing a severe state of the disease, a risk value for the subject having a severe state of the disease, a risk value for the subject developing a severe state of the disease, a value quantifying predicted severity of the disease, and a categorization of predicted severity of the disease. The risk level may comprise a zero level, a low level, a medium level, a high level, or a very high level. The accuracy prediction may comprise a value predicting the accuracy of the diagnostic risk prediction. The accuracy prediction may comprise a value predicting the accuracy of the development risk prediction. The accuracy prediction may comprise a value predicting the accuracy of the severity risk prediction. The accuracy prediction may comprise a value predicting the accuracy of any combination of the accuracy of the diagnostic risk prediction, the accuracy of the development risk prediction, and the accuracy of the severity risk prediction. The accuracy may comprise a sensitivity of severity of a cancer type of at least 60%. The sensitivity for identification of a cancer type may comprise at least 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%,92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%. The accuracy may comprise a specificity of severity of a cancer type of at least 60%. The specificity for severity of a cancer type may comprise at least 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%,74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%,90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%. The severity of a disease may be identified in the patient by the method with an accuracy of at least about 70%. TheDocket No.: 68417-702.601 severity of a disease may be identified in the patient by the method with an accuracy of at least about 75%., 80%, 85%, 90%, 95%, 06%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or more than about 99.9%. The severity of a disease may be identified in the patient by the method with a sensitivity of at least about 70%. The severity of a disease may be identified in the patient by the method with a sensitivity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%. The sensitivity may be determined by determining a value or percentage of correctly predicted reference subjects that developed a corresponding level of severity of a disease of a plurality of reference subjects with similar characteristics to the subject. The severity of a disease may be identified in the patient by the method with a specificity of at least about 70%. The severity of a disease may be identified in the patient by the method with a specificity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%.

[0108] The disease may comprise cancer. The cancer may be an oral cancer. The cancer may be a non-oral cancer. The non-oral cancer may comprise one or more of acute lymphoblastic leukemia, acute myeloid leukemia, adenoid cystic carcinoma, adrenocortical carcinoma, anal cancer, appendix cancer, astrocytoma, basal cell carcinoma, bile duct cancer, bladder cancer, bone cancer, brain stem glioma, brain tumor, breast cancer, bronchial tumors, Burkitt lymphoma, carcinoid tumors, cervical cancer, cholangiocarcinoma, chondrosarcoma, chronic lymphocytic leukemia, chronic myeloid leukemia, colon cancer, colorectal cancer, craniopharyngioma, cutaneous T-cell lymphoma, ductal carcinoma in situ, endometrial cancer, esophageal cancer, Ewing family of tumors, eyelid carcinoma, fallopian tube cancer, fibrosarcoma, gallbladder cancer, gastric cancer, gastrointestinal carcinoid tumors, gastrointestinal stromal tumor, germ cell tumor, glioblastoma, glioma, hairy cell leukemia, head and neck cancer, Hodgkin lymphoma, hypopharyngeal cancer, inflammatory breast cancer, invasive ductal carcinoma, kidney cancer, laryngeal cancer, lip cancer, liver cancer, lung cancer, lymphoma, malignant fibrous histiocytoma, malignant melanoma, medulloblastoma, Merkel cell carcinoma, mesothelioma, metastatic squamous neck cancer, mouth cancer, multiple myeloma, mycosis fungoides, nasal cavity and paranasal sinus cancer, nasopharyngeal cancer, neuroblastoma, nonHodgkin lymphoma, ocular melanoma, oropharyngeal cancer, ovarian cancer, pancreatic cancer, papillary thyroid cancer, parathyroid cancer, penile cancer, pituitary tumor, prostate cancer, rectal cancer, renal cell carcinoma, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcoma, sebaceous carcinoma, skin cancer, small cell lung cancer, small intestine cancer, soft tissue sarcoma, squamous cell carcinoma, stomach cancer, testicular cancer, throat cancer, thymoma and thymic carcinoma, thyroid cancer, uterine cancer, uterine sarcoma, vaginal cancer, vulvar cancer, Waldenstrom macroglobulinemia, or Wilms tumor, or any combination thereof.Docket No.: 68417-702.601The oral disease may be periodontal disease. The oral disease may comprise one or more of caries, halitosis, gingivitis, periodontitis, stomatitis, oral thrush, oral herpes, salivary gland infections, dental abscess, dental erosion, dental fluorosis, dry mouth syndrome, hand, foot, and mouth disease, oral leukoplakia, oral lichen planus, candidiasis, canker sores, oral cysts, jaw disorders, mumps, oral papilloma, pemphigus vulgaris, pericoronitis, tooth decay, toothache, trench mouth, Vincent’s angina, oral ulcers, or any combination thereof. The disease may comprise an infectious disease. The disease may comprise a non-oral disease. The disease may comprise multiple diseases or conditions.

[0109] The biological sample may comprise a saliva sample. The biological sample may comprises an oral tissue sample. The oral tissue sample may comprise one or more of an oral swab sample, an oral rinse sample, an oral brush biopsy sample, an oral mucosal tissue biopsy sample, a tongue scraping sample, a dental plaque sample, a gingival tissue sample, a gingival crevicular fluid sample, a jawbone sample, a tooth fragment sample, a dental pulp sample, a periapical sample, a tooth root tissue sample, a periodontal pocket sample, a buccal cells sample, or a periodontal ligament sample, or any combination thereof.

[0110] The oral point of care location may comprise a dental practice. The oral point of care location may comprise an orthodontic practice. The oral point of care location may comprise an endodontic practice. The oral point of care location may comprise an oral surgeon practice. The oral point of care location may comprise a maxillofacial surgeon practice. The oral point of care location may comprise a periodontal practice. The oral point of care location may comprise a prosthodontia practice. The oral point of care location may comprise any combination of a dental practice, an orthodontic practice, an endodontic practice, an oral surgeon practice, a maxillofacial surgeon practice, a periodontal practice, and a prosthodontia practice.[oni] The oral point of care provider may collect a sample when a change is observed in a subject, such as a change in tissue morphology, such as a lesion. The change may be observed by a healthcare provider at an oral point of care. The sample may be collected repeatedly over time. The samples collected over time may be compared for a patient or a cohort of patients.

[0112] The sample collection tube may be part of a kit. The kit may comprise a diagnostic kit. The sample preservation reagent may be compatible with one or more subtypes of the biological samples. The sample processing reagent may be compatible with one or more subtypes of the biological samples. The sample preservation agent may comprise a formulan-free preservation agent. Transferring the sample collection tube to the remote sample processing location may comprise placing the sample collection tube in an outer packaging. The sample collection tube may be placed in an outer packaging at the oral point of care location. Transferring the sample collection tube to the remote sample processing location may comprise shipping the sampleDocket No.: 68417-702.601 collection tube in the outer packaging. The sample collection tube may be shipped in the outer packaging from the oral point of care location to the remote sample processing location.

[0113] The derivative of the biological sample may comprise one or more of microbial cells, DNA, methylated DNA, ctDNA, RNA, mRNA, miRNA, IncRNA, sRNA, siRNA, snRNA, sncRNA, circulating tumor cell RNA, proteins, enzymes, lipids, hormones, metabolites, cytokines, antigens, antibodies, exosomes, extracellular vesicles, or cancer antigens, or any combination thereof. Processing the biological sample or the derivative thereof at the remote sample processing location may comprise generating a biomarker profile of the subject.Generating the biomarker profile of the subject may comprise detecting one or more biomarkers in the biological sample or the derivative thereof. Generating the biomarker profile of the subject may comprise measuring an amount of the detected biomarkers. Generating the biomarker profile of the subject may comprise measuring a frequency of the detected biomarkers. Generating the biomarker profile of the subject may comprise measuring a concentration of the detected biomarkers. Generating the biomarker profile of the subject may comprise generating a comparative analysis of the detected biomarkers. Generating the biomarker profile of the subject may comprise mapping patterns of the detected biomarkers. Generating the biomarker profile of the subject may comprise any combination of detecting one or more biomarkers in the biological sample or the derivative thereof, measuring an amount of the detected biomarkers, measuring a frequency of the detected biomarkers, measuring a concentration of the detected biomarkers, generating a comparative analysis of the detected biomarkers, and mapping patterns of the detected biomarkers. Mapping patterns of the detected biomarkers may comprise mapping DNA methylation patterns. Mapping patterns of the detected biomarkers may comprise mapping RNA fusion patterns. Mapping patterns of the detected biomarkers may comprise mapping amounts of the detected biomarkers over areas of the biological sample or the derivative thereof. Generation of the biomarker profile of the subject may comprise utilizing a machine learning model to predict one or more characteristics of the subject. Prediction of one or more characteristics of the subject may be based on the biomarker detection.

[0114] The method may further comprise storing the biomarker profile of the subject in a database. The database may be a database of the remote sample processing location. The method may further comprise generating as part of the disease risk profile of the subject a prediction of the subject’s response to a treatment. The method may further comprise generating as part of the disease risk profile of the subject a prediction of the subject’s response to a subset of treatments of a plurality of treatments. The subset of treatments may be selected based on the disease risk profile of the subject.Docket No.: 68417-702.601

[0115] The method may further comprise utilizing a machine learning model. The machine learning model may be utilized to select an appropriate subset of treatments. An appropriate subset of treatments may be selected based on the disease risk profile of the subject. An appropriate subset of treatments may be selected based on disease risk profiles of others. An appropriate subset of treatments may be selected based on both the disease risk profile of the subject and disease risk profiles of others. The method may comprise utilizing a machine learning model to predict the response of the subject to the selected subset of treatments. The machine learning model may be configured to predict the response of the subject to the selected subset of treatments. The prediction of the response of the subject to the selected subset of treatments may be based on the disease risk profile of the subject. The method may further comprise generating as part of the disease risk profile of the subject a prediction of an optimized treatment for the subject from a plurality of treatments. The optimized treatment may comprise one or more treatments with high efficacy. The optimized treatment may comprise one or more treatments with high safety. The optimized treatment may comprise one or more treatments with low toxicity. The optimized treatment may comprise one or more treatments with low reactivity. The optimized treatment may comprise one or more treatments with low cross-reactivity. The optimized treatment may comprise one or more treatments with any other positive characteristic. The optimized treatment may comprise one or more treatments with any combination of high efficacy, high safety, low toxicity, low reactivity, low cross-reactivity, and any other positive characteristic. The optimized treatment may be selected from a plurality of treatments based on the disease risk profile of the subject.

[0116] The method may further comprise comparing the disease risk profile of the subject to disease risk profiles of others. Comparing the disease risk profile of the subject to disease risk profiles of others may be used to select the optimized treatment. A machine learning model may be utilized to predict the optimized treatment based on the disease risk profile of the subject. The method may further comprise generating as part of the disease risk profile of the subject a prediction of disease progression for the subject. A machine learning model may be utilized to predict the disease progression for the subject based on the disease risk profile of the subject. The method may further comprise generating the disease risk profile for the subject based at least in part on the biomarker profile of the subject.

[0117] The method may further comprise generating the disease risk profile for the subject based at least in part on the biomarker profile of the subject in comparison to a plurality of stored biomarker profiles. The plurality of stored biomarker profiles may comprise previously generated biomarker profiles. The previously generated biomarker profiles may be stored on a database.Docket No.: 68417-702.601

[0118] The method may further comprise generating the disease risk profile for the subject based at least in part on the biomarker profile of the subject in comparison to a subset of the plurality of stored biomarker profiles. The subset of the plurality of stored biomarker profiles may be selected based on similarity to one or more components of the biomarker profile of the subject. The generated disease risk profile may be transmitted to the oral point of care location. Transmitting to the oral point of care location the generated disease risk profile for the subject may comprise transmitting data. Transmitting to the oral point of care location the generated disease risk profile for the subject may comprise transmitting data over a network. The data may comprise the disease risk profile. The data may be transmitted over a network from the remote sample processing location to the oral point of care location. Transmitting to the healthcare professional location the generated disease risk profile for the subject may comprise transmitting data. Transmitting to the healthcare professional location the generated disease risk profile for the subject may comprise transmitting data over a network. The data may comprise the disease risk profile. The data may be transmitted over a network from the remote sample processing location to the healthcare professional location. The data may be stored in the database of the remote sample processing location. The oral point of care location may store the data at a database at the oral point of care location. The healthcare professional location may store the data at a database at the healthcare professional location. The generated disease risk profile for the subject may be transmitted to a secure user interface at the oral point of care location. The generated disease risk profile for the subject may be transmitted to a secure user interface at the healthcare professional location.

[0119] The method may comprise displaying the generated disease risk profile. The generated disease risk profile may be displayed at the oral point of care location. The generated disease risk profile may be displayed at the healthcare professional location. A subset of data of the generated risk profile may be displayed. A subset of data of the generated risk profile may be displayed on a display at the oral point of care location. A subset of data of the generated risk profile may be displayed at the healthcare professional location.Generating Disease Risk Profile Remotely at Sample Processing Location

[0120] A method of remotely generating a transmissible disease risk profile for a subject may be used. The method may comprise receiving a sample collection tube. The sample collection tube may comprise a biological sample. The sample collection tube may comprise a derivative of the biological sample. The biological sample or derivative thereof may be sealed in the sample collection tube. The sample collection tube may comprise a sample preservation reagent. The sample collection tube may comprise a sample processing reagent. The sample collection tubeDocket No.: 68417-702.601 may comprise both a sample preservation reagent and a sample processing reagent. The sample collection tube may be received from a remote oral point of care location. The biological sample or derivative thereof may originate from an oral cavity of a subject. The method may comprise processing the biological sample. The method may comprise processing the derivative of the biological sample. Processing the biological sample or derivative thereof may generate the disease risk profile for the subject. The method may further comprise transmitting data to the oral point of care location. The method may further comprise transmitting data to a healthcare professional location. The data may comprise the generated disease risk profile for the subject. The generated disease risk profile may identify a risk of the subject. The risk of the subject may be for having the disease. The risk of the subject may be for developing the disease. The risk of the subject may be for both having the disease and developing the disease.

[0121] The disease risk profile may identify the risk at an accuracy of at least 60%. The disease risk profile may identify the risk at an accuracy of at least 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%. The disease risk profile may comprise a diagnostic risk prediction of the subject for having the disease. The disease risk profile may comprise a development risk prediction of the subject for having the disease. The disease risk profile may comprise a severity risk prediction of the subject for having the disease. The disease risk profile may comprise an accuracy prediction of the subject for having the disease. The disease risk profile may comprise any combination of a diagnostic risk prediction of the subject for having the disease, a development risk prediction of the subject for having the disease, a severity risk prediction of the subject for having the disease, and an accuracy prediction of the subject for having the disease. The diagnostic prediction may comprise a binary indication for the subject having the disease. The diagnostic prediction may comprise a risk value for the subject having the disease. The diagnostic prediction may comprise a risk level for the subject having the disease. The diagnostic prediction may comprise any combination of a binary indication for the subject having the disease, a risk value for the subject having the disease, and a risk level for the subject having the disease. The accuracy may comprise a sensitivity of identification of a cancer type of at least 60%. The sensitivity for identification of a cancer type may comprise at least 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%. The accuracy may comprise a specificity of identification of a cancer type of at least 60%. The specificity for identification of a cancer type may comprise at least 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%,Docket No.: 68417-702.6011 >, !!> / >, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%. The disease may be identified in the patient using the disease risk profile of the method with an accuracy of at least about 70%. The disease may be identified in the patient using the disease risk profile of the method with an accuracy of at least about 75%., 80%, 85%, 90%, 95%, 06%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or more than about 99.9%. The disease may be identified in the patient using the disease risk profile of the method with a sensitivity of at least about 70%. The disease may be identified in the patient using the disease risk profile of the method with a sensitivity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%. The sensitivity may be determined by determining a value or percentage of correct identifications of disease of one or more reference subjects with similar disease risk profile characteristics to the patient. The disease may be identified in the patient using the disease risk profile of the method with a specificity of at least about 70%. The disease may be identified in the patient using the disease risk profile of the method with a specificity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%.

[0122] The development risk prediction may comprise a binary indication for the subject developing the disease. The development risk prediction may comprise a risk value for the subject developing the disease. The development risk prediction may comprise a risk level for the subject developing the disease. The development risk prediction may comprise any combination of a binary indication for the subject developing the disease, a risk value for the subject developing the disease, and a risk level for the subject developing the disease. The accuracy may comprise a sensitivity of development risk of a cancer type of at least 60%. The sensitivity for identification of a cancer type may comprise at least 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%. The accuracy may comprise a specificity of development risk of a cancer type of at least 60%. The specificity for development risk of a cancer type may comprise at least 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%. The development risk of a disease may be identified in the patient by the method with an accuracy of at least about 70%. The development risk of a disease may be identified in the patient by the method with an accuracy of at least about 75%., 80%, 85%, 90%, 95%, 06%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or more than about 99.9%. The development risk of a disease may be identified in the patient by the method with a sensitivity of at least about 70%. The development risk of a disease may beDocket No.: 68417-702.601 identified in the patient by the method with a sensitivity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%. The sensitivity may be determined by determining a value or percentage of correctly predicted reference subjects that developed a disease of a plurality of reference subjects with similar characteristics to the subject. The development risk of a disease may be identified in the patient by the method with a specificity of at least about 70%. The development risk of a disease may be identified in the patient by the method with a specificity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%.

[0123] The severity risk prediction may comprise a binary indication for the subject having a severe state of the disease. The severity risk prediction may comprise a binary indication for the subject developing a severe state of the disease. The severity risk prediction may comprise a risk value for the subject having a severe state of the disease. The severity risk prediction may comprise a risk value for the subject developing a severe state of the disease. The severity risk prediction may comprise a value quantifying predicted severity of the disease. The severity risk prediction may comprise a categorization of predicted severity of the disease. The severity risk prediction may comprise any combination of a binary indication for the subject having a severe state of the disease, a binary indication for the subject developing a severe state of the disease, a risk value for the subject having a severe state of the disease, a risk value for the subject developing a severe state of the disease, a value quantifying predicted severity of the disease, and a categorization of predicted severity of the disease. The risk level may comprise a zero level, a low level, a medium level, a high level, or a very high level. The accuracy prediction may comprise a value predicting the accuracy of the diagnostic risk prediction. The accuracy prediction may comprise a value predicting the accuracy of the development risk prediction. The accuracy prediction may comprise a value predicting the accuracy of the severity risk prediction. The accuracy prediction may comprise a value predicting the accuracy of any combination of the accuracy of the diagnostic risk prediction, the accuracy of the development risk prediction, and the accuracy of the severity risk prediction. The accuracy may comprise a sensitivity of severity of a cancer type of at least 60%. The sensitivity for identification of a cancer type may comprise at least 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%,92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%. The accuracy may comprise a specificity of severity of a cancer type of at least 60%. The specificity for severity of a cancer type may comprise at least 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%,74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%,90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%. The severity of a diseaseDocket No.: 68417-702.601 may be identified in the patient by the method with an accuracy of at least about 70%. The severity of a disease may be identified in the patient by the method with an accuracy of at least about 75%., 80%, 85%, 90%, 95%, 06%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or more than about 99.9%. The severity of a disease may be identified in the patient by the method with a sensitivity of at least about 70%. The severity of a disease may be identified in the patient by the method with a sensitivity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%. The sensitivity may be determined by determining a value or percentage of correctly predicted reference subjects that developed a corresponding level of severity of a disease of a plurality of reference subjects with similar characteristics to the subject. The severity of a disease may be identified in the patient by the method with a specificity of at least about 70%. The severity of a disease may be identified in the patient by the method with a specificity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%.

[0124] The disease may comprise cancer. The cancer may be an oral cancer. The cancer may be a non-oral cancer. The non-oral cancer may comprise one or more of acute lymphoblastic leukemia, acute myeloid leukemia, adenoid cystic carcinoma, adrenocortical carcinoma, anal cancer, appendix cancer, astrocytoma, basal cell carcinoma, bile duct cancer, bladder cancer, bone cancer, brain stem glioma, brain tumor, breast cancer, bronchial tumors, Burkitt lymphoma, carcinoid tumors, cervical cancer, cholangiocarcinoma, chondrosarcoma, chronic lymphocytic leukemia, chronic myeloid leukemia, colon cancer, colorectal cancer, craniopharyngioma, cutaneous T-cell lymphoma, ductal carcinoma in situ, endometrial cancer, esophageal cancer, Ewing family of tumors, eyelid carcinoma, fallopian tube cancer, fibrosarcoma, gallbladder cancer, gastric cancer, gastrointestinal carcinoid tumors, gastrointestinal stromal tumor, germ cell tumor, glioblastoma, glioma, hairy cell leukemia, head and neck cancer, Hodgkin lymphoma, hypopharyngeal cancer, inflammatory breast cancer, invasive ductal carcinoma, kidney cancer, laryngeal cancer, lip cancer, liver cancer, lung cancer, lymphoma, malignant fibrous histiocytoma, malignant melanoma, medulloblastoma, Merkel cell carcinoma, mesothelioma, metastatic squamous neck cancer, mouth cancer, multiple myeloma, mycosis fungoides, nasal cavity and paranasal sinus cancer, nasopharyngeal cancer, neuroblastoma, nonHodgkin lymphoma, ocular melanoma, oropharyngeal cancer, ovarian cancer, pancreatic cancer, papillary thyroid cancer, parathyroid cancer, penile cancer, pituitary tumor, prostate cancer, rectal cancer, renal cell carcinoma, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcoma, sebaceous carcinoma, skin cancer, small cell lung cancer, small intestine cancer, soft tissue sarcoma, squamous cell carcinoma, stomach cancer, testicular cancer, throat cancer, thymoma and thymic carcinoma, thyroid cancer, uterine cancer, uterine sarcoma, vaginal cancer,Docket No.: 68417-702.601 vulvar cancer, Waldenstrom macroglobulinemia, or Wilms tumor, or any combination thereof. The oral disease may be periodontal disease. The oral disease may comprise one or more of caries, halitosis, gingivitis, periodontitis, stomatitis, oral thrush, oral herpes, salivary gland infections, dental abscess, dental erosion, dental fluorosis, dry mouth syndrome, hand, foot, and mouth disease, oral leukoplakia, oral lichen planus, candidiasis, canker sores, oral cysts, jaw disorders, mumps, oral papilloma, pemphigus vulgaris, pericoronitis, tooth decay, toothache, trench mouth, Vincent’s angina, oral ulcers, or any combination thereof. The disease may comprise an infectious disease. The disease may comprise a non-oral disease. The disease may comprise multiple diseases or conditions.

[0125] In some embodiments, the disease may comprise a virus-associated disease. The virus- associated disease may comprise a disease associated with an infection from one or more viruses. In some cases, the disease may comprise a disease associated with Human Papillomavirus (HPV). The virus-associated disease may comprise a viral infection. The virus-associated disease may comprise a cancer associated with an infection by one or more viruses. The virus-associated disease may comprise a cancer resulting from infection by one or more viruses. The virus- associated disease may comprise a cancer resulting from infection by HPV. The virus-associated disease may comprise a head or neck cancer resulting from infection by HPV. The virus- associated disease may comprise an esophageal cancer resulting from infection by HPV. In some embodiments, the infection status of the subject with the virus may be determined using one or more biomarkers, to determine a risk of the virus-associated disease for the subject. In some cases, a detection of one or more biomarkers associated with HPV may be used to determine a risk of an HPV-associated cancer in a subject. The cancer may comprise head and neck cancer. The cancer may comprise esophageal cancer. The one or more detected biomarkers may comprise one or more of: TP53, PIK3CA, NOTCH1, CDKN2A, FAT1, HRAS, FGFR3, CASP8, MET, EGFR, PTEN, AD API, KRAS, TTN, FAT3, COL11A1, FBXW7, TRPM, DPYD, and HPV. In some embodiments, the panel of biomarkers may comprise one or more of: TP53, CDKN2A, FAT1, CASP8, NOTCH1, PIK3CA, HRAS, NOTCH2, AJUBA, RBI, FBXW7, KMT2D, PTEN, NSD1, FGFR3, EGFR, KRAS, MET, AD API, MYC, NFE2L2, EP300, CDH1, IGFBP3, ANAX1, TGFB1, TP63, CTNNB1, NRAS, FGFR2, FGFR1, ERBB2, PIK3R1, NF1, TTN, FAT3, COL11A1, and TRPM3.

[0126] In some embodiments, one or more genotypes of viruses may be identified. The virus genotypes may comprise HPV genotypes. The HPV genotypes may comprise one or more of: HPV16, HPV18, HPV31, HPV33, HPV52, and HPV35.

[0127] In some cases, a panel comprising a subset of biomarkers may be analyzed to determine a risk of cancer. The risk of cancer may comprise a risk of esophageal cancer. The panel mayDocket No.: 68417-702.601 comprise DPYD, EGFR, and KRAS. In some embodiments, the panel of biomarkers may be used to determine the presence or absence of one or more signs or symptoms of the subject having a viral infection. In some embodiments, the panel of biomarkers may be used to determine the presence or absence of one or more signs or symptoms of a virus-associated cancer in a subject. In some embodiments, the panel of biomarkers may be used to determine the presence or absence of one or more signs or symptoms of a virus-associated cancer in a subject having a viral infection. The virus-associated cancer may comprise head and neck cancer. The virus-associated cancer may comprise esophageal cancer. The panel of biomarkers may be used for determining a risk of viral infection of a subject. The panel of biomarkers may be used for determining a presence or absence of viral infection of a subject. The panel of biomarkers may be used for determining a type of viral infection of a subject. The panel of biomarkers may be used for determining a severity of viral infection of a subject. The panel of biomarkers may be used for determining a disease risk of a virus-associated cancer of a subject. The panel of biomarkers may be used for determining a presence or absence of a virus-associated cancer of a subject. The panel of biomarkers may be used for determining a type of a virus-associated cancer of a subject. The panel of biomarkers may be used for determining a severity of a virus- associated cancer of a subject. The panel of biomarkers may be used for determining a viral infection characteristic and determining a virus-associated cancer characteristic. The risk of a disease may be identified in the patient by the method with an accuracy of at least about 70%. The risk of a disease may be identified in the patient by the method with an accuracy of at least about 75%., 80%, 85%, 90%, 95%, 06%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or more than about 99.9%. The risk of a disease may be identified in the patient by the method with a sensitivity of at least about 70%. The risk of a disease may be identified in the patient by the method with a sensitivity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%. The sensitivity may be determined by determining a value or percentage of correctly predicted reference subjects that developed a corresponding level of risk of a disease of a plurality of reference subjects with similar characteristics to the subject. The risk of a disease may be identified in the patient by the method with a specificity of at least about 70%. The risk of a disease may be identified in the patient by the method with a specificity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%. The type of a disease may be identified in the patient by the method with an accuracy of at least about 70%. The type of a disease may be identified in the patient by the method with an accuracy of at least about 75%., 80%, 85%, 90%, 95%, 06%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or more than about 99.9%. The type of a disease may be identified in the patient by the method with a sensitivity of at least about 70%. The type of aDocket No.: 68417-702.601 disease may be identified in the patient by the method with a sensitivity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%. The sensitivity may be determined by determining a value or percentage of correctly predicted reference subjects that developed a corresponding type of a disease of a plurality of reference subjects with similar characteristics to the subject. The type of a disease may be identified in the patient by the method with a specificity of at least about 70%. The type of a disease may be identified in the patient by the method with a specificity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%.

[0128] In some embodiments, the method may further comprise determining an eligibility of a subject for a treatment. In some embodiments, the method may further comprise determining an eligibility of a subject for a targeted therapy, immunotherapy, or clinical trial enrollment. In some embodiments, the method may further comprise determining one or more treatment options. The biomarkers may be used to predict the success of the one or more treatment options. The biomarkers may be used to predict a reaction of one or more subjects to one or more treatments. The biomarkers may be used to determine an effectiveness of one or more treatments. In some cases, the biomarkers may comprise one or more of PIK3CA E545K, or H1047R. In some cases, the biomarkers may be used to predict an effectiveness of a treatment. The treatment may comprise PI3K inhibitors such as alpelisib. The biomarkers may comprise one or more of HRAS Q61, G12, or G13 mutations. In some cases, the biomarkers may be used to predict an effectiveness of a treatment. In some cases, the treatment may comprise contacting a sample with farnesyltransferase inhibitors tipifamib. The eligibility for treatment of a disease may be identified for the patient by the method with an accuracy of at least about 70%. The eligibility for treatment of a disease may be identified for the patient by the method with an accuracy of at least about 75%., 80%, 85%, 90%, 95%, 06%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or more than about 99.9%. The eligibility for treatment of a disease may be identified for the patient by the method with a sensitivity of at least about 70%. The eligibility for treatment of a disease may be identified for the patient by the method with a sensitivity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%. The eligibility for treatment of a disease may be identified for the patient by the method with a specificity of at least about 70%. The eligibility for treatment of a disease may be identified for the patient by the method with a specificity of at least about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more than 99.5%.

[0129] In some embodiments, the method may further comprise generating an output in response to a determination. The output may be an alert, such as a text alert. The alert may comprise a contacting message. The output message may comprise a recommendation to follow up with anDocket No.: 68417-702.601 oncologist. The output may comprise an action. The action may comprise reserving an appointment with a healthcare specialist. The action may comprise generating an appointment reservation with a healthcare specialist. The reservation may be a follow-up by an oncologist.

[0130] In some embodiments, the method may further comprise generating a risk of disease based at least in part on location-specific biomarkers. In some embodiments, the method may further comprise generating a risk of disease based at least in part on collection mechanismspecific biomarkers. The one or more biomarkers may be specific to a location a sample was collected or a sample collection method, or both. The location of sample collection may comprise a location within an oral cavity of a subject. The sample collection method may comprise a use of a swab or a brush.

[0131] In some embodiments, the method may comprise modifying a depth of screening. The Depth of screening may be increased by adjusting library input. The library input may be adjusted into sequencing run for lower variant allele fraction detection. In some embodiments, the library input may be adjusted by about IX, 2X, 3X, 4X, 5X, 6X, 7X, 8X, 9X, 10X, 1 IX, 12X, 13X, 14X, 15X, 16X, 17X, 18X, 19X, 20X, or more than 20X. In some embodiments, the library input may be adjusted by 10X.

[0132] The biological sample may comprise a saliva sample. The biological sample may comprises an oral tissue sample. The oral tissue sample may comprise one or more of an oral swab sample, an oral rinse sample, an oral brush biopsy sample, an oral mucosal tissue biopsy sample, a tongue scraping sample, a dental plaque sample, a gingival tissue sample, a gingival crevicular fluid sample, a jawbone sample, a tooth fragment sample, a dental pulp sample, a periapical sample, a tooth root tissue sample, a periodontal pocket sample, a buccal cells sample, or a periodontal ligament sample, or any combination thereof.

[0133] The oral point of care location may comprise a dental practice. The oral point of care location may comprise an orthodontic practice. The oral point of care location may comprise an endodontic practice. The oral point of care location may comprise an oral surgeon practice. The oral point of care location may comprise a maxillofacial surgeon practice. The oral point of care location may comprise a periodontal practice. The oral point of care location may comprise a prosthodontia practice. The oral point of care location may comprise any combination of a dental practice, an orthodontic practice, an endodontic practice, an oral surgeon practice, a maxillofacial surgeon practice, a periodontal practice, and a prosthodontia practice.

[0134] The method may be performed at a remote sample processing location. The remote sample processing location may comprise a laboratory location. The remote sample processing location may comprise a sequencing location. The remote sample processing location may comprise a testing location. The remote sample processing location may comprise a tissueDocket No.: 68417-702.601 processing location. The remote sample processing location may comprise a tissue storage location. The remote sample processing location may comprise any combination of a laboratory location, a sequencing location, a testing location, a tissue processing location, and a tissue storage location. The remote sample processing location may comprise a plurality of remote sample processing locations. The plurality of remote sample processing locations may be in communication with each other.

[0135] The sample collection tube may be part of a kit. The kit may comprise a diagnostic kit. The sample preservation reagent may be compatible with one or more subtypes of the biological samples. The sample processing reagent may be compatible with one or more subtypes of the biological samples. The sample preservation agent may comprise a formulan-free preservation agent. Receiving the sample collection tube may comprise receiving the sample collection tube in an outer packaging. The outer packaging may be placed around the sample collection tube. The outer packaging may be placed around the sample collection tube at the oral point of care location.

[0136] The method may comprise removing the outer packaging prior to processing. Receiving the sample collection tube may comprise receiving a shipment. The shipment may comprise the sample collection tube. The sample collection tube may be from the oral point of care location. The derivative of the biological sample may comprise one or more of microbial cells, DNA, methylated DNA, ctDNA, RNA, mRNA, miRNA, IncRNA, sRNA, siRNA, snRNA, sncRNA, circulating tumor cell RNA, proteins, enzymes, lipids, hormones, metabolites, cytokines, antigens, antibodies, exosomes, extracellular vesicles, or cancer antigens, or any combination thereof. Processing the biological sample may comprise generating a biomarker profile of the subject.

[0137] Generating the biomarker profile of the subject may comprise detecting one or more biomarkers in the biological sample or the derivative thereof. Generating the biomarker profile of the subject may comprise measuring an amount of the detected biomarkers. Generating the biomarker profile of the subject may comprise measuring a frequency of the detected biomarkers. Generating the biomarker profile of the subject may comprise measuring a concentration of the detected biomarkers. Generating the biomarker profile of the subject may comprise generating a comparative analysis of the detected biomarkers. Generating the biomarker profile of the subject may comprise mapping patterns of the detected biomarkers. Generating the biomarker profile of the subject may comprise any combination of detecting one or more biomarkers in the biological sample or the derivative thereof, measuring an amount of the detected biomarkers, measuring a frequency of the detected biomarkers, measuring a concentration of the detected biomarkers, generating a comparative analysis of the detectedDocket No.: 68417-702.601 biomarkers, and mapping patterns of the detected biomarkers. Mapping patterns of the detected biomarkers may comprise mapping DNA methylation patterns. Mapping patterns of the detected biomarkers may comprise mapping RNA fusion patterns. Mapping patterns of the detected biomarkers may comprise mapping amounts of the detected biomarkers over areas of the biological sample or the derivative thereof. Generation of the biomarker profile of the subject may comprise utilizing a machine learning model to predict one or more characteristics of the subject. Prediction of one or more characteristics of the subject may be based on the biomarker detection. The method may comprise storing the biomarker profile of the subject in a database. The database may be at the remote sample processing location.

[0138] The method may further comprise generating as part of the disease risk profile of the subject a prediction of the subject’s response to a treatment. The method may further comprise generating as part of the disease risk profile of the subject a prediction of the subject’s response to a subset of treatments of a plurality of treatments. The subset of treatments may be selected based on the disease risk profile of the subject.

[0139] The method may further comprise utilizing a machine learning model. The machine learning model may be utilized to select an appropriate subset of treatments. An appropriate subset of treatments may be selected based on the disease risk profile of the subject. An appropriate subset of treatments may be selected based on disease risk profiles of others. An appropriate subset of treatments may be selected based on both the disease risk profile of the subject and disease risk profiles of others. The method may comprise utilizing a machine learning model to predict the response of the subject to the selected subset of treatments. The machine learning model may be configured to predict the response of the subject to the selected subset of treatments. The prediction of the response of the subject to the selected subset of treatments may be based on the disease risk profile of the subject. The method may further comprise generating as part of the disease risk profile of the subject a prediction of an optimized treatment for the subject from a plurality of treatments. The optimized treatment may comprise one or more treatments with high efficacy. The optimized treatment may comprise one or more treatments with high safety. The optimized treatment may comprise one or more treatments with low toxicity. The optimized treatment may comprise one or more treatments with low reactivity. The optimized treatment may comprise one or more treatments with low cross-reactivity. The optimized treatment may comprise one or more treatments with any other positive characteristic. The optimized treatment may comprise one or more treatments with any combination of high efficacy, high safety, low toxicity, low reactivity, low cross-reactivity, and any other positive characteristic. The optimized treatment may be selected from a plurality of treatments based on the disease risk profile of the subject.Docket No.: 68417-702.601

[0140] The method may further comprise comparing the disease risk profile of the subject to disease risk profiles of others. Comparing the disease risk profile of the subject to disease risk profiles of others may be used to select the optimized treatment. A machine learning model may be utilized to predict the optimized treatment based on the disease risk profile of the subject. The method may further comprise generating as part of the disease risk profile of the subject a prediction of disease progression for the subject. A machine learning model may be utilized to predict the disease progression for the subject based on the disease risk profile of the subject. The method may further comprise generating the disease risk profile for the subject based at least in part on the biomarker profile of the subject.

[0141] The method may further comprise generating the disease risk profile for the subject based at least in part on the biomarker profile of the subject in comparison to a plurality of stored biomarker profiles. The plurality of stored biomarker profiles may comprise previously generated biomarker profiles. The previously generated biomarker profiles may be stored on a database.

[0142] Data may be transmitted to the oral point of care location. Data may be transmitted to the healthcare professional location. The data may comprise the generated disease risk profile for the subject. The data may be transmitted over a network. The data may be transmitted over a network to the oral point of care location. The data may be transmitted over a network to the healthcare professional location. The data transmitted over the network may comprise the generated disease risk profile for the subject. Data may be transmitted to a database at the oral point of care location. Data may be transmitted to a database at the healthcare professional location. Data may be transmitted to a secure user interface at the oral point of care location. Data may be transmitted to a secure user interface at the healthcare professional location. The data transmitted to a secure user interface comprise the generated disease risk profile for the subject.

[0143] In yet another embodiment, the method may comprise a method of point of care testing to identify a disease in a patient, said method comprising: obtaining a saliva sample from the patient; extracting a DNA sample from the saliva sample; administering a reagent set to the DNA sample, wherein the reagent set may comprise a panel of biomarkers, and generating a plurality of amplicons or derivatives thereof; sequencing the plurality of amplicons to generate a report comprising the presence or absence of one or more biomarkers of the panel of biomarkers; wherein the report identifies the disease in the patient.

[0144] In some embodiments, the DNA sample may comprise at least 50 ng of DNA derived from the saliva sample.Docket No.: 68417-702.601

[0145] In some embodiments, the panel of biomarkers may comprise at least one biomarker selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, AD API, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0146] In some embodiments, the panel of biomarkers may comprise at least two biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, AD API, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0147] In some embodiments, the panel of biomarkers may comprise at least three biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, AD API, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0148] In some embodiments, the panel of biomarkers may comprise at least four biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, AD API, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0149] In some embodiments, the panel of biomarkers may comprise at least five biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, AD API, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0150] In some embodiments, the panel of biomarkers may comprise at least six biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, AD API, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0151] In some embodiments, the panel of biomarkers may comprise at least seven biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, AD API, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0152] In some embodiments, the panel of biomarkers may comprise at least eight biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, AD API, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0153] In some embodiments, the panel of biomarkers may comprise at least nine biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1,Docket No.: 68417-702.601AD API, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, F0XL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0154] In some embodiments, the panel of biomarkers may comprise at least ten biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, AD API, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0155] In some embodiments, the panel of biomarkers may comprise at least eleven biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, AD API, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0156] In some embodiments, the panel of biomarkers may comprise at least twelve biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, AD API, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0157] In some embodiments, the panel of biomarkers may comprise one or more of: TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, AD API, HRAS, KRAS, and FGFR3.

[0158] In some embodiments, the panel of biomarkers may comprise AKT1, BRAF, EGFR, ERBB2, FOXL2, GNA11, GNAQ, GNAS, HRAS, IDH1, IDH2, KIT, KRAS, MET, NRAS, PDGFRA, PIK3CA, RET, TERT, and TP53.

[0159] In some embodiments, the panel of biomarkers may comprise AKT1, BRAF, EGFR, ERBB2, FOXL2, GNA11, GNAQ, KIT, KRAS, MET, NRAS, PDGFRA, PIK3CA, RET, and TP53.

[0160] In some embodiments, the panel of biomarkers may comprise one or more of: TP53, CDKN2A, FAT1, CASP8, NOTCH1, PIK3CA, HRAS, NOTCH2, AJUBA, RBI, FBXW7, KMT2D, PTEN, NSD1, FGFR3, EGFR, KRAS, MET, AD API, MYC, NFE2L2, EP300, CDH1, IGFBP3, ANAX1, TGFB1, TP63, CTNNB1, NRAS, FGFR2, FGFR1, ERBB2, PIK3R1, NF1, TTN, FAT3, COL11A1, and TRPM3.

[0161] In some embodiments, the sequencing may comprise a limit of detection (“LoD”) of 0.05% for variant allele frequency (“VAF”).

[0162] In some embodiments, one or more genotypes of viruses may be identified. The virus genotypes may comprise HPV genotypes. The HPV genotypes may comprise one or more of: HPV16, HPV18, HPV31, HPV33, HPV52, and HPV35.Docket No.: 68417-702.601EXAMPLES

[0163] The following examples are provided to further illustrate some embodiments of the present disclosure, but are not intended to limit the scope of the disclosure; it will be understood by their exemplary nature that other procedures, methodologies, or techniques known to those skilled in the art may alternatively be used.Example 1: Result Processing Timeline

[0164] In one non limiting example, information such as cancer detection or monitoring as illustrated in the background information of FIG. 1 may be generated as a bioinformatics report. As illustrated in the example process diagram of FIG. 2, results may be generated for a patient from a generation of a bioinformatics report, for example, from a sample. In this exemplary method, as illustrated in FIG. 2, over 2-3 days samples may be selected, shipped, and accessioned. Bulk kits may be transferred to one or more contracted laboratories, for example. As illustrated in FIG. 2, DNA extraction may then take place about 3 hours after, using for example high-throughput extraction methods that may be automated. Then, for example as illustrated in FIG. 2, about 3 hours later, target amplicon generation may take place using for example a panel of 13 genes to provide, for example 90% or more inclusivity. As illustrated in FIG. 2, the next step may be library prep and multiplexed NextGen Sequencing methods over, for example 2-3 days. For example, NextSeq 500 / 550 high output methods may be used on, for example 200 samples at about 10,000 reads per target. The LoD may be for example 0.05% VAF from, for example 50ng of analyzed swab DNA as illustrated in FIG. 2. Then, for example, a bioinformatics report may be generated 1-2 days after, using for example automated omics analysis pipeline and report generation as illustrated in FIG. 2. The patient may be informed of the result.

[0165] The panel of genes may be, for example, the genes listed in FIG. 3 for various conditions like, for example, cancers such as squamous cell carcinomas. The squamous cell carcinomas may originate in the upper aero digestive tract for example. The panels may show over 90% inclusivity for DNA alterations for example.Example 2: Exemplary Method for Conventional Screening at Routine Annual Dental Visit

[0166] One exemplary conventional method of conventional visual and tactile examination not involving a processing method is illustrated in FIG. 4. As illustrated in FIG. 4, for example, at a routine annual dental visit the patient may undergo conventional visual and tactile examination. Then, as illustrated in FIG. 4 if the patient has no clinically evident lesion or symptoms no further action is taken. If the patient has a clinically evident but seemingly innocuous lesion,Docket No.: 68417-702.601 periodic follow-ups may be initiated. If the lesion dissolves during periodic follow up, no further action need be taken. If the lesion persists or progresses, a biopsy may be performed or the patient may be referred to a specialist. If, as illustrated in FIG. 4 for example, the conventional visual and tactile examination reveals a clinically evident, suspicious lesion then a biopsy of the lesion may be performed or the patient may be referred to a specialist. Next, as illustrated in the conventional method of FIG. 4, if after proposal of the biopsy of the lesion or referral to a specialist the patient declines a biopsy or referral then there may be performed a cytologic adjunct for additional assessment. If a negative result is obtained as illustrated in FIG. 4, periodic follow-ups may be performed. If a positive result is obtained, additional referrals to specialists or biopsies may be performed. The biopsy or referral to a specialist may directly result in a definitive diagnostic of a malignant disorder or of no malignancy as illustrated in FIG.4Example 3: Remote Processing Method from Oral Point of Care Location Sampling

[0167] As illustrated in FIG. 5, a much different method may offer a more streamlined, efficient, and cost-effective screening and follow-up process. As shown in the exemplary process diagram of FIG. 5, at a routine dentist visit the dentist may perform a swab test as part of a routine checkup. The swab sample may be sent to a remote lab, and the result may be sent to the dentist to notify the patient. The results, as illustrated in FIG. 5, may be positive or negative, and if a positive result is obtained from the lab processing, a diagnostic biopsy may be performed at follow up. If negative, a screen may be performed, for example every 3 years.

[0168] In processing, markers of DNA alteration may be detected as a panel as illustrated by the exemplary data shown in FIG. 6. The gene biomarkers of DNA alteration may be, for example, TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and AD API as shown in FIG. 6. This panel may have 91% or more coverage for a cancer, for example oral carcinoma. Percentages for tumors with a mutation of the gene of the panel may be determined as shown in FIG. 6. In some embodiments, the gene biomarkers may comprise one or more of: TP53, PIK3CA, NOTCH1, CDKN2A, FAT1, HRAS, FGFR3, CASP8, MET, EGFR, PTEN, AD API, KRAS, TTN, FAT3, COL11 Al, FBXW7, TRPM, DPYD, and HPV. In some embodiments, the biomarkers may comprise a panel. In some embodiments, the panel may comprise a subset of biomarkers.

[0169] The method may be performed partially at the oral point of care location as shown in the exemplary process diagram of FIG. 7. For example, a sample may be taken at an oral point of care location. The sample may be deposited in a collection tube at the oral point of care location, then transferred to a remote sample processing location. FIG. 7 further illustrates that theDocket No.: 68417-702.601 biological sample or a derivative thereof may be processed at the remote sample processing location and a disease risk profile may be generated at the remote sample processing location. The oral point of care location may receive the disease risk profile transmitted by the remote sample processing location. Alternatively or in addition to the oral point of care location, a healthcare professional location may receive the disease risk profile transmitted by the remote sample processing location as shown in FIG. 7.

[0170] An exemplary method for the processing at the remote sample processing location is illustrated in FIG. 8. For example, the remote sample processing location may receive a biological sample or a derivative thereof from a point of care location that is remote form the sample processing location. The sample processing location may process the biological sample or derivate, generate a biomarker profile, then generate a disease risk profile as shown in FIG. 8. The remote sample processing location may then transmit the disease risk profile to the oral point of care location, a healthcare professional location, or both as illustrated in FIG. 8.

[0171] An exemplary process diagram for a method of outputting predictions from the sample processing is illustrated in FIG. 9. For example, biomarkers may be first detected, then patterns of the detected biomarkers may be mapped and determined to predict characteristics based on the patterns. As illustrated in FIG. 9, for example, a biomarker profile may then be generated from the predicted characteristics, and the biomarker profile may be stored. The biomarker profile may be compared to one or more reference biomarker profiles. This may then be used, for example, to generate a disease risk profile. As illustrated in FIG. 9 for example, the disease risk profile may be compared to one or more reference disease risk profiles. From this comparison, for example, predictions may be output concerning, for example optimized treatments for the subject, treatment response predictions, diagnosis predictions, disease risk predictions, disease progression predictions, or any additional predictions as illustrated in FIG. 9.

[0172] An exemplary process diagram for a method of reporting results from the sample processing is illustrated in FIG. 10. The sample may be collected by oral swab collected by a dentist or hygienist. Data collection may then be performed, with sample information uploaded to a portal. Shipping of the sample may be performed to a centralized lab for testing. Accessioning of the data may be performed to cross-reference and assess patient suitability. Nucleic acid extraction may be performed. Targeted amplification, library preparation, and sequencing may be performed. The sequencing may be performed on an NGS platform. Data processing may be performed comprising aligning the sequence. Variant calling may be performed. Data annotation and interpretation may be performed. Reporting may be performed of the data analysis results. Reporting may comprise review by a medical professional. The data from the results may be uploaded, for example on a portal. Results may be reported in variousDocket No.: 68417-702.601 selected formats. Negative results may be reported by email. Positive results may be reported by a recommendation, such as a referral to an oncologist.

[0173] The results may be reviewed and analyzed by an oral care professional such as a dentist, an orthodontist, or a dental hygienist at an oral point of care. The oral care professional may diagnose a disease such as an oral cancer or oral disease based on analysis of the results. Alternatively or additionally, the oral care professional may share the results with a medical professional at a healthcare facility. The oral care professional may provide a recommendation for one or more treatments of a disease to a subject based on review or analysis of the results. The oral care professional may provide a recommendation for one or more treatments of a disease to a medical professional at a healthcare facility based on review or analysis of the results. The results may be shared by the oral care professional with a medical professional at a healthcare facility. The medical professional may provide a recommendation for one or more treatments of a disease to a subject. The one or more treatments may include a chemotherapy, radiation, an immunotherapy, a surgery, or any combination thereof.Computing Systems

[0174] Referring to FIG. 10, a block diagram is shown depicting an exemplary machine that includes a computer system 1000 (e.g., a processing or computing system) within which a set of instructions may execute for causing a device to perform or execute any one or more of the aspects and / or methodologies for static code scheduling of the present disclosure. The components in FIG. 10 are examples only and do not limit the scope of use or functionality of any hardware, software, embedded logic component, or a combination of two or more such components implementing particular embodiments.

[0175] Computer system 1000 may include one or more processors 1001, a memory 1003, and a storage 1008 that communicate with each other, and with other components, via a bus 1040. The bus 1040 may also link a display 1032, one or more input devices 1033 (which may, for example, include a keypad, a keyboard, a mouse, a stylus, etc.), one or more output devices 134, one or more storage devices 1035, and various tangible storage media 1036. All of these elements may interface directly or via one or more interfaces or adaptors to the bus 1040. For instance, the various tangible storage media 1036 may interface with the bus 1040 via storage medium interface 1026. Computer system 1000 may have any suitable physical form, including but not limited to one or more integrated circuits (ICs), printed circuit boards (PCBs), mobile handheld devices (such as mobile telephones or PDAs), laptop or notebook computers, distributed computer systems, computing grids, or servers.Docket No.: 68417-702.601

[0176] Computer system 1000 includes one or more processor(s) 1001 (e.g., central processing units (CPUs) or general-purpose graphics processing units (GPGPUs)) that carry out functions. Processor(s) 1001 optionally contains a cache memory unit 1002 for temporary local storage of instructions, data, or computer addresses. Processor(s) 1001 are configured to assist in execution of computer readable instructions. Computer system 1000 may provide functionality for the components depicted in FIG. 10 as a result of the processor(s) 1001 executing non-transitory, processor-executable instructions embodied in one or more tangible computer-readable storage media, such as memory 1003, storage 1008, storage devices 1035, and / or storage medium 1036. The computer-readable media may store software that implements particular embodiments, and processor(s) 1001 may execute the software. Memory 1003 may read the software from one or more other computer-readable media (such as mass storage device(s) 1035, 1036) or from one or more other sources through a suitable interface, such as network interface 1020. The software may cause processor(s) 1001 to carry out one or more processes or one or more steps of one or more processes described or illustrated herein. Carrying out such processes or steps may include defining data structures stored in memory 1003 and modifying the data structures as directed by the software.

[0177] The memory 1003 may include various components (e.g., machine readable media) including, but not limited to, a random access memory component (e.g., RAM 1004) (e.g., static RAM (SRAM), dynamic RAM (DRAM), ferroelectric random access memory (FRAM), phasechange random access memory (PRAM), etc.), a read-only memory component (e.g., ROM 1005), and any combinations thereof. ROM 1005 may act to communicate data and instructions unidirectionally to processor(s) 1001, and RAM 1004 may act to communicate data and instructions bidirectionally with processor(s) 1001. ROM 1005 and RAM 1004 may include any suitable tangible computer-readable media described below. In one example, a basic input / output system 1006 (BIOS), including basic routines that help to transfer information between elements within computer system 1000, such as during start-up, may be stored in the memory 1003.

[0178] Fixed storage 1008 is connected bidirectionally to processor(s) 1001, optionally through storage control unit 1007. Fixed storage 1008 provides additional data storage capacity and may also include any suitable tangible computer-readable media described herein. Storage 1008 may be used to store operating system 1009, executable(s) 1010, data 1011, applications 1012 (application programs), and the like. Storage 1008 may also include an optical disk drive, a solid-state memory device (e.g., flash-based systems), or a combination of any of the above. Information in storage 1008 may, in appropriate cases, be incorporated as virtual memory in memory 1003.Docket No.: 68417-702.601

[0179] In one example, storage device(s) 1035 may be removably interfaced with computer system 1000 (e.g., via an external port connector (not shown)) via a storage device interface 1025. Particularly, storage device(s) 1035 and an associated machine-readable medium may provide non-volatile and / or volatile storage of machine-readable instructions, data structures, program modules, and / or other data for the computer system 1000. In one example, software may reside, completely or partially, within a machine-readable medium on storage device(s) 1035. In another example, software may reside, completely or partially, within processor(s) 1001

[0180] Bus 1040 connects a wide variety of subsystems. Herein, reference to a bus may encompass one or more digital signal lines serving a common function, where appropriate. Bus 1040 may be any of several types of bus structures including, but not limited to, a memory bus, a memory controller, a peripheral bus, a local bus, and any combinations thereof, using any of a variety of bus architectures. As an example and not by way of limitation, such architectures include an Industry Standard Architecture (ISA) bus, an Enhanced ISA (EISA) bus, a Micro Channel Architecture (MCA) bus, a Video Electronics Standards Association local bus (VLB), a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, an Accelerated Graphics Port (AGP) bus, HyperTransport (HTX) bus, serial advanced technology attachment (SATA) bus, and any combinations thereof.

[0181] Computer system 1000 may also include an input device 1033. In one example, a user of computer system 100 may enter commands and / or other information into computer system 1000 via input device(s) 1033. Examples of an input device(s) 1033 include, but are not limited to, an alpha-numeric input device (e.g., a keyboard), a pointing device (e.g., a mouse or touchpad), a touchpad, a touch screen, a multi-touch screen, a joystick, a stylus, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), an optical scanner, a video or still image capture device (e.g., a camera), and any combinations thereof. In some embodiments, the input device is a Kinect, Leap Motion, or the like. Input device(s) 1033 may be interfaced to bus 1040 via any of a variety of input interfaces 1023 (e.g., input interface 1023) including, but not limited to, serial, parallel, game port, USB, FIREWIRE, THUNDERBOLT, or any combination of the above.

[0182] In particular embodiments, when computer system 1000 is connected to network 1030, computer system 1000 may communicate with other devices, specifically mobile devices and enterprise systems, distributed computing systems, cloud storage systems, cloud computing systems, and the like, connected to network 1030. Communications to and from computer system 1000 may be sent through network interface 1020. For example, network interface 1020 may receive incoming communications (such as requests or responses from other devices) in theDocket No.: 68417-702.601 form of one or more packets (such as Internet Protocol (IP) packets) from network 130, and computer system 1000 may store the incoming communications in memory 1003 for processing. Computer system 1000 may similarly store outgoing communications (such as requests or responses to other devices) in the form of one or more packets in memory 1003 and communicated to network 1030 from network interface 1020. Processor(s) 1001 may access these communication packets stored in memory 1003 for processing.

[0183] Examples of the network interface 1020 include, but are not limited to, a network interface card, a modem, and any combination thereof. Examples of a network 1030 or network segment 1030 include, but are not limited to, a distributed computing system, a cloud computing system, a wide area network (WAN) (e.g., the Internet, an enterprise network), a local area network (LAN) (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a direct connection between two computing devices, a peer-to-peer network, and any combinations thereof. A network, such as network 1030, may employ a wired and / or a wireless mode of communication. In general, any network topology may be used.

[0184] Information and data may be displayed through a display 1032. Examples of a display 1032 include, but are not limited to, a cathode ray tube (CRT), a liquid crystal display (LCD), a thin film transistor liquid crystal display (TFT-LCD), an organic liquid crystal display (OLED) such as a passive-matrix OLED (PMOLED) or active-matrix OLED (AMOLED) display, a plasma display, and any combinations thereof. The display 1032 may interface to the processor(s) 1001, memory 1003, and fixed storage 1008, as well as other devices, such as input device(s) 1033, via the bus 1040. The display 1032 is linked to the bus 1040 via a video interface 1022, and transport of data between the display 1032 and the bus 1040 may be controlled via the graphics control 1021. In some embodiments, the display is a video projector. In some embodiments, the display is a head-mounted display (HMD) such as a VR headset. In further embodiments, suitable VR headsets include, by way of non-limiting examples, HTC Vive, Oculus Rift, Samsung Gear VR, Microsoft HoloLens, Razer OSVR, FOVE VR, Zeiss VR One, Avegant Glyph, Freefly VR headset, and the like. In still further embodiments, the display is a combination of devices such as those disclosed herein.

[0185] In addition to a display 1032, computer system 1000 may include one or more other peripheral output devices 1034 including, but not limited to, an audio speaker, a printer, a storage device, and any combinations thereof. Such peripheral output devices may be connected to the bus 1040 via an output interface 1024. Examples of an output interface 1024 include, but are not limited to, a serial port, a parallel connection, a USB port, a FIREWIRE port, a THUNDERBOLT port, and any combinations thereof.Docket No.: 68417-702.601

[0186] In addition or as an alternative, computer system 1000 may provide functionality as a result of logic hardwired or otherwise embodied in a circuit, which may operate in place of or together with software to execute one or more processes or one or more steps of one or more processes described or illustrated herein. Reference to software in this disclosure may encompass logic, and reference to logic may encompass software. Moreover, reference to a computer- readable medium may encompass a circuit (such as an IC) storing software for execution, a circuit embodying logic for execution, or both, where appropriate. The present disclosure encompasses any suitable combination of hardware, software, or both.

[0187] Those of skill in the art will appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality.

[0188] The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0189] The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by one or more processor(s), or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor may read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.Docket No.: 68417-702.601

[0190] In accordance with the description herein, suitable computing devices include, by way of non-limiting examples, cloud computing platforms, distributed computing platforms, server clusters, server computers, desktop computers, laptop computers, notebook computers, subnotebook computers, netbook computers, and netpad computers.

[0191] In some embodiments, the computing device includes an operating system configured to perform executable instructions. The operating system is, for example, software, including programs and data, which manages the device’s hardware and provides services for execution of applications. Those of skill in the art will recognize that suitable server operating systems include, by way of non -limiting examples, FreeBSD, OpenBSD, NetBSD®, Linux, Apple® Mac OS X Server®, Oracle® Solaris®, Windows Server®, and Novell® NetWare®. Those of skill in the art will recognize that suitable personal computer operating systems include, by way of nonlimiting examples, Microsoft® Windows®, Apple® Mac OS X®, UNIX®, and UNIX-like operating systems such as GNU / Linux®. In some embodiments, the operating system is provided by cloud computing. Those of skill in the art will also recognize that suitable mobile smartphone operating systems include, by way of non-limiting examples, Nokia® Symbian® OS, Apple® iOS®, Research in Motion® BlackBerry OS®, Google® Android®, Microsoft® Windows Phone® OS, Microsoft® Windows Mobile® OS, Linux®, and Palm® WebOS®.Non-transitory Computer Readable Storage Medium

[0192] In some embodiments, the platforms, systems, media, and methods disclosed herein include one or more non-transitory computer readable storage media encoded with a program including instructions executable by the operating system of an optionally networked computing device. In further embodiments, a computer readable storage medium is a tangible component of a computing device. In still further embodiments, a computer readable storage medium is optionally removable from a computing device. In some embodiments, a computer readable storage medium includes, by way of non-limiting examples, CD-ROMs, DVDs, flash memory devices, solid state memory, magnetic disk drives, magnetic tape drives, optical disk drives, distributed computing systems including cloud computing systems and services, and the like. In some cases, the program and instructions are permanently, substantially permanently, semipermanently, or non-transitorily encoded on the media.Computer Programs

[0193] In some embodiments, the platforms, systems, media, and methods disclosed herein include at least one computer program, or use of the same. A computer program includes a sequence of instructions, executable by one or more processor(s) of the computing device’sDocket No.: 68417-702.601CPU, written to perform a specified task. Computer readable instructions may be implemented as program modules, such as functions, objects, Application Programming Interfaces (APIs), computing data structures, and the like, which perform particular tasks or implement particular abstract data types. In light of the disclosure provided herein, those of skill in the art will recognize that a computer program may be written in various versions of various languages.

[0194] The functionality of the computer readable instructions may be combined or distributed as desired in various environments. In some embodiments, a computer program comprises one sequence of instructions. In some embodiments, a computer program comprises a plurality of sequences of instructions. In some embodiments, a computer program is provided from one location. In other embodiments, a computer program is provided from a plurality of locations. In various embodiments, a computer program includes one or more software modules. In various embodiments, a computer program includes, in part or in whole, one or more web applications, one or more mobile applications, one or more standalone applications, one or more web browser plug-ins, extensions, add-ins, or add-ons, or combinations thereof.Software Modules

[0195] In some embodiments, the platforms, systems, media, and methods disclosed herein include software, server, and / or database modules, or use of the same. In view of the disclosure provided herein, software modules are created by techniques known to those of skill in the art using machines, software, and languages known to the art. The software modules disclosed herein are implemented in a multitude of ways. In various embodiments, a software module comprises a file, a section of code, a programming object, a programming structure, a distributed computing resource, a cloud computing resource, or combinations thereof. In further various embodiments, a software module comprises a plurality of files, a plurality of sections of code, a plurality of programming objects, a plurality of programming structures, a plurality of distributed computing resources, a plurality of cloud computing resources, or combinations thereof. In various embodiments, the one or more software modules comprise, by way of non-limiting examples, a web application, a mobile application, a standalone application, and a distributed or cloud computing application. In some embodiments, software modules are in one computer program or application. In other embodiments, software modules are in more than one computer program or application. In some embodiments, software modules are hosted on one machine. In other embodiments, software modules are hosted on more than one machine. In further embodiments, software modules are hosted on a distributed computing platform such as a cloud computing platform. In some embodiments, software modules are hosted on one or moreDocket No.: 68417-702.601 machines in one location. In other embodiments, software modules are hosted on one or more machines in more than one location.Databases

[0196] In some embodiments, the platforms, systems, media, and methods disclosed herein include one or more databases, or use of the same. In view of the disclosure provided herein, those of skill in the art will recognize that many databases are suitable for storage and retrieval of information, for example customer incident data. In various embodiments, suitable databases include, by way of non-limiting examples, relational databases, non-relational databases, object oriented databases, object databases, entity-relationship model databases, associative databases, XML databases, document oriented databases, and graph databases. Further non-limiting examples include SQL, PostgreSQL, MySQL, Oracle, DB2, Sybase, and MongoDB. In some embodiments, a database is Internet-based. In further embodiments, a database is web-based. In still further embodiments, a database is cloud computing-based. In a particular embodiment, a database is a distributed database. In other embodiments, a database is based on one or more local computer storage devices.LIST OF EMBODIMENTS

[0197] The following list of embodiments of the invention are to be considered as disclosing various features of the invention, which features may be considered to be specific to the particular embodiment under which they are discussed, or which are combinable with the various other features as listed in other embodiments. Thus, simply because a feature is discussed under one particular embodiment does not necessarily limit the use of that feature to that embodiment.

[0198] Embodiment 1. A method of point of care testing to identify a disease in a patient, said method comprising: (a) obtaining a saliva sample from the patient; (b) extracting a DNA sample from the saliva sample; (c) administering a reagent set to the DNA sample, wherein the reagent set comprises a panel of biomarkers, and generating a plurality of amplicons or derivatives thereof; and (d) sequencing the plurality of amplicons to generate a report comprising the presence or absence of one or more biomarkers of the panel of biomarkers; wherein the report identifies the disease in the patient.

[0199] Embodiment 2. The method of embodiment 1, wherein the DNA sample comprises at least 50 ng of DNA derived from the saliva sample.

[0200] Embodiment 3. The method of embodiment 1, wherein the panel of biomarkers comprises at least one biomarker selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, ADAP1, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.Docket No.: 68417-702.601

[0201] Embodiment 4. The method of embodiment 1, wherein the panel of biomarkers comprises at least two biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, ADAP1, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0202] Embodiment 5. The method of embodiment 1, wherein the panel of biomarkers comprises at least three biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, ADAP1, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0203] Embodiment 6. The method of embodiment 1, wherein the panel of biomarkers comprises at least four biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, ADAP1, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0204] Embodiment 7. The method of embodiment 1, wherein the panel of biomarkers comprises at least five biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, ADAP1, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0205] Embodiment 8. The method of embodiment 1, wherein the panel of biomarkers comprises at least six biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, ADAP1, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0206] Embodiment 9. The method of embodiment 1, wherein the panel of biomarkers comprises at least seven biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, ADAP1, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0207] Embodiment 10. The method of embodiment 1, wherein the panel of biomarkers comprises at least eight biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, ADAP1, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0208] Embodiment 11. The method of embodiment 1, wherein the panel of biomarkers comprises at least nine biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, ADAP1, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0209] Embodiment 12. The method of embodiment 1, wherein the panel of biomarkers comprises at least ten biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1,Docket No.: 68417-702.601EGFR, CDKN2A, PTEN, FAT1, ADAP1, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, F0XL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0210] Embodiment 13. The method of embodiment 1, wherein the panel of biomarkers comprises at least eleven biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, ADAP1, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0211] Embodiment 14. The method of embodiment 1, wherein the panel of biomarkers comprises at least twelve biomarkers selected from TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, ADAP1, HRAS, KRAS, FGFR3, AKT1, BRAF, ERBB2, FOXL2, GNA11, GNAQ, GNAS, IDH1, IDH2, KIT, NRAS, PDGFRA, RET, and TERT.

[0212] Embodiment 15. The method of embodiment 1, wherein the panel of biomarkers comprises TP53, CASP8, PIK3CA, MET, NOTCH1, EGFR, CDKN2A, PTEN, FAT1, AD API, HRAS, KRAS, and FGFR3.

[0213] Embodiment 16. The method of embodiment 1, wherein the panel of biomarkers comprises AKT1, BRAF, EGFR, ERBB2, FOXL2, GNA11, GNAQ, GNAS, HRAS, IDH1, IDH2, KIT, KRAS, MET, NRAS, PDGFRA, PIK3CA, RET, TERT, and TP53.

[0214] Embodiment 17. The method of embodiment 1, wherein the panel of biomarkers comprises AKT1, BRAF, EGFR, ERBB2, FOXL2, GNA11, GNAQ, KIT, KRAS, MET, NRAS, PDGFRA, PIK3CA, RET, and TP53.

[0215] Embodiment 18. The method of embodiment 1, wherein the sequencing comprises a limit of detection (“LoD”) of 0.05% for variant allele frequency (“VAF”).

[0216] Embodiment 19. A method of remotely generating a transmissible disease risk profile for a subject comprising: (a) receiving a sample collection tube comprising a biological sample or derivative thereof sealed therein and a sample preservation reagent or a sample processing reagent, or both, from a remote oral point of care location, wherein the biological sample originates from an oral cavity of a subject; (b) processing the biological sample or the derivative thereof to generate the disease risk profile for the subject; (c) transmitting to the oral point of care location or a healthcare professional location the generated disease risk profile for the subject, wherein the generated disease risk profile identifies a risk of the subject for having or developing the disease.

[0217] Embodiment 20. The method of embodiment 19, wherein the disease risk profile identifies the risk at an accuracy of at least 60%.

[0218] Embodiment 21. The method of embodiment 19, wherein the disease risk profile comprises one or more of a diagnostic risk prediction, a development risk prediction, a severity risk prediction, or an accuracy prediction of the subject for having the disease.Docket No.: 68417-702.601

[0219] Embodiment 22. The method of embodiment 21, wherein the diagnostic prediction comprises one or more of a binary indication for the subject having the disease, a risk value for the subject having the disease, or a risk level for the subject having the disease.

[0220] Embodiment 23. The method of embodiment 21, wherein the development risk prediction comprises one or more of a binary indication for the subject developing the disease, a risk value for the subject developing the disease, or a risk level for the subject developing the disease.

[0221] Embodiment 24. The method of embodiment 21, wherein the severity risk prediction comprises one or more of a binary indication for the subject having a severe state of the disease, a binary indication for the subject developing a severe state of the disease, a risk value for the subject having a severe state of the disease, a risk value for the subject developing a severe state of the disease, a value quantifying predicted severity of the disease, or a categorization of predicted severity of the disease.

[0222] Embodiment 25. The method of embodiment 21, wherein the risk level comprises one or more of a zero level, low level, medium level, high level, or very high level.

[0223] Embodiment 26. The method of embodiment 21, wherein the accuracy prediction comprises a value predicting the accuracy of one or more of the diagnostic risk prediction, the development risk prediction, or the severity risk prediction.

[0224] Embodiment 27. The method of embodiment 19, wherein the disease comprises cancer.

[0225] Embodiment 28. The method of embodiment 27, wherein the cancer is an oral cancer.

[0226] Embodiment 29. The method of embodiment 19, wherein the disease comprises an oral disease.

[0227] Embodiment 30. The method of embodiment 29, wherein the oral disease is periodontal disease.

[0228] Embodiment 31. The method of embodiment 29, wherein the oral disease comprises one or more of caries, halitosis, gingivitis, periodontitis, stomatitis, oral thrush, oral herpes, salivary gland infections, dental abscess, dental erosion, dental fluorosis, dry mouth syndrome, hand, foot, and mouth disease, oral leukoplakia, oral lichen planus, candidiasis, canker sores, oral cysts, jaw disorders, mumps, oral papilloma, pemphigus vulgaris, pericoronitis, tooth decay, toothache, trench mouth, Vincent’s angina, oral ulcers, or any combination thereof.

[0229] Embodiment 32. The method of embodiment 19, wherein the disease comprises an infectious disease.

[0230] Embodiment 33. The method of embodiment 19, wherein the disease comprises a nonoral disease.

[0231] Embodiment 34. The method of embodiment 19, wherein the disease comprises multiple diseases or conditions.Docket No.: 68417-702.601

[0232] Embodiment 35. The method of embodiment 19, wherein the biological sample comprises a saliva sample.

[0233] Embodiment 36. The method of embodiment 35, wherein the biological sample comprises an oral tissue sample.

[0234] Embodiment 37. The method of embodiment 36, wherein the oral tissue sample comprises one or more of an oral swab sample, an oral rinse sample, an oral brush biopsy sample, an oral mucosal tissue biopsy sample, a tongue scraping sample, a dental plaque sample, a gingival tissue sample, a gingival crevicular fluid sample, a jawbone sample, a tooth fragment sample, a dental pulp sample, a periapical sample, a tooth root tissue sample, a periodontal pocket sample, a buccal cells sample, or a periodontal ligament sample, or any combination thereof.

[0235] Embodiment 38. The method of embodiment 19, wherein the oral point of care location comprises a dental practice, an orthodontic practice, an endodontic practice, an oral surgeon practice, a maxillofacial surgeon practice, a periodontal practice, or a prosthodontia practice.

[0236] Embodiment 39. The method of embodiment 19, further comprising performing the method at a remote sample processing location.

[0237] Embodiment 40. The method of embodiment 39, wherein the remote sample processing location comprises a laboratory location, a sequencing location, a testing location, a tissue processing location, a tissue storage location, or any combination thereof.

[0238] Embodiment 41. The method of embodiment 39, wherein the remote sample processing location comprises a plurality of remote processing locations in communication with each other.

[0239] Embodiment 42. The method of embodiment 19, wherein the sample collection tube is part of a kit.

[0240] Embodiment 43. The method of embodiment 42, wherein the kit comprises a diagnostic kit.

[0241] Embodiment 44. The method of embodiment 19, wherein the sample preservation reagent or the sample processing reagent, or both, are compatible with one or more subtypes of the biological samples.

[0242] Embodiment 45. The method of embodiment 19, wherein the sample preservation agent comprises a formulan-free preservation agent.

[0243] Embodiment 46. The method of embodiment 19, wherein receiving the sample collection tube comprises receiving the sample collection tube in an outer packaging.

[0244] Embodiment 47. The method of embodiment 46, wherein the outer packaging is placed around the sample collection tube at the oral point of care location.Docket No.: 68417-702.601

[0245] Embodiment 48. The method of embodiment 46, further comprising removing the outer packaging prior to processing.

[0246] Embodiment 49. The method of embodiment 19, wherein receiving the sample collection tube further comprises receiving a shipment comprising the sample collection tube from the oral point of care location.

[0247] Embodiment 50. The method of embodiment 19, wherein the derivative of the biological sample comprises one or more of microbial cells, DNA, methylated DNA, ctDNA, RNA, mRNA, miRNA, IncRNA, sRNA, siRNA, snRNA, sncRNA, circulating tumor cell RNA, proteins, enzymes, lipids, hormones, metabolites, cytokines, antigens, antibodies, exosomes, extracellular vesicles, or cancer antigens, or any combination thereof.

[0248] Embodiment 51. The method of embodiment 19, wherein processing the biological sample further comprises generating a biomarker profile of the subject.

[0249] Embodiment 52. The method of embodiment 51, wherein generating the biomarker profile of the subject comprises detecting one or more biomarkers in the biological sample or the derivative thereof.

[0250] Embodiment 53. The method of embodiment 52, wherein generating the biomarker profile of the subject further comprises measuring an amount of the detected biomarkers, measuring a frequency of the detected biomarkers, measuring a concentration of the detected biomarkers, generating a comparative analysis of the detected biomarkers, or mapping patterns of the detected biomarkers, or any combination thereof.

[0251] Embodiment 54. The method of embodiment 53, wherein mapping patterns of the detected biomarkers comprises mapping DNA methylation patterns.

[0252] Embodiment 55. The method of embodiment 53, wherein mapping patterns of the detected biomarkers comprises mapping RNA fusion patterns.

[0253] Embodiment 56. The method of embodiment 53, wherein mapping patterns of the detected biomarkers comprises mapping amounts of the detected biomarkers over areas of the biological sample or the derivative thereof.

[0254] Embodiment 57. The method of embodiment 51, wherein generation of the biomarker profile of the subject further comprises utilizing a machine learning model to predict one or more characteristics of the subject based on the biomarker detection.

[0255] Embodiment 58. The method of embodiment 51, wherein the method further comprises storing the biomarker profile of the subject in a database.

[0256] Embodiment 59. The method of embodiment 19, further comprising generating as part of the disease risk profile of the subject a prediction of the subject’s response to a treatment.Docket No.: 68417-702.601

[0257] Embodiment 60. The method of embodiment 19, further comprising generating as part of the disease risk profile of the subject a prediction of the subject’s response to a subset of treatments of a plurality of treatments.

[0258] Embodiment 61. The method of embodiment 60, wherein the subset of treatments is selected based on the disease risk profile of the subject.

[0259] Embodiment 62. The method of embodiment 61, further comprising utilizing a machine learning model to select an appropriate subset of treatments based on the disease risk profile of the subject and disease risk profiles of others.

[0260] Embodiment 63. The method of embodiment 62, further comprising utilizing a machine learning model to predict the response of the subject to the selected subset of treatments.

[0261] Embodiment 64. The method of embodiment 63, wherein the machine learning model is configured to predict the response of the subject to the selected subset of treatments based the disease risk profile of the subject and disease risk profiles of others.

[0262] Embodiment 65. The method of embodiment 60, further comprising generating as part of the disease risk profile of the subject a prediction of an optimized treatment for the subject from a plurality of treatments.

[0263] Embodiment 66. The method of embodiment 65, wherein the optimized treatment comprises one or more treatments most likely to be effective against the disease of the disease risk profile for the subject.

[0264] Embodiment 67. The method of embodiment 66, wherein the optimized treatment is selected from a plurality of treatments based on the disease risk profile of the subject.

[0265] Embodiment 68. The method of embodiment 67, further comprising comparing the disease risk profile of the subject to disease risk profiles of others to select the optimized treatment.

[0266] Embodiment 69. The method of embodiment 68, further comprising utilizing a machine learning model to predict the optimized treatment based on the disease risk profile of the subject.

[0267] Embodiment 70. The method of embodiment 19, further comprising generating as part of the disease risk profile of the subject a prediction of disease progression for the subject.

[0268] Embodiment 71. The method of embodiment 70, further comprising utilizing a machine learning model to predict the disease progression for the subject based on the disease risk profile of the subj ect.

[0269] Embodiment 72. The method of embodiment 52, further comprising generating the disease risk profile for the subject based at least in part on the biomarker profile of the subject.Docket No.: 68417-702.601

[0270] Embodiment 73. The method of embodiment 72, further comprising generating the disease risk profile for the subject based at least in part on the biomarker profile of the subject in comparison to a plurality of stored biomarker profiles.

[0271] Embodiment 74. The method of embodiment 73, wherein the plurality of stored biomarker profiles comprise previously generated biomarker profiles stored on a database.

[0272] Embodiment 75. The method of embodiment 74, further comprising generating the disease risk profile for the subject based at least in part on the biomarker profile of the subject in comparison to a subset of the plurality of stored biomarker profiles.

[0273] Embodiment 76. The method of embodiment 75, wherein the subset of the plurality of stored biomarker profiles is selected based on similarity to one or more components of the biomarker profile of the subject.

[0274] Embodiment 77. The method of embodiment 19, further comprising storing the disease risk profile for the subject in a database.

[0275] Embodiment 78. The method of embodiment 19, wherein transmitting to the oral point of care location or the healthcare professional location the generated disease risk profile for the subject comprises transmitting data comprising the disease risk profile over a network to the oral point of care location or the healthcare professional location.

[0276] Embodiment 79. The method of embodiment 78, wherein transmitting the data to the oral point of care location or the healthcare professional location further comprises transmitting the data to a database at the oral point of care location or a database at the healthcare professional location.

[0277] Embodiment 80. The method of embodiment 79, wherein transmitting to the oral point of care location or the healthcare professional location the generated disease risk profile for the subject comprises transmitting data comprising the disease risk profile to a secure user interface at the oral point of care location or at the healthcare professional location.

[0278] While preferred embodiments of the present disclosure have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the disclosure. It should be understood that various alternatives to the embodiments of the present disclosure may be employed in practicing the present disclosure. It is intended that the following claims define the scope of the present disclosure and that methods and structures within the scope of these claims and their equivalents be covered thereby.

Claims

1. Docket No.: 68417-702.601CLAIMSWHAT IS CLAIMED IS:

1. A method of point of care testing to identify a disease in a patient, said method comprising: a. obtaining a saliva sample from the patient; b. extracting a deoxyribonucleic acid (DNA) sample from the saliva sample; c. enriching the DNA sample with a reagent set, wherein the reagent set comprises a panel of biomarkers, thereby generating a plurality of amplicons or derivatives thereof, wherein the panel of biomarkers comprises at least one biomarker selected from TP53, N0TCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and AD API; d. sequencing the plurality of amplicons or derivatives thereof to detect the panel of biomarkers; and e. treating the disease in the patient by identifying the disease in the patient.

2. The method of claim 1, wherein the panel of biomarkers comprises at least two biomarkers selected from TP53, N0TCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAP1.

3. The method of claim 1, wherein the panel of biomarkers comprises at least three biomarkers selected from TP53, N0TCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAP1.

4. The method of claim 1, wherein the panel of biomarkers comprises at least four biomarkers selected from TP53, N0TCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAP1.

5. The method of claim 1, wherein the panel of biomarkers comprises at least five biomarkers selected from TP53, N0TCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAP1.

6. The method of claim 1, wherein the panel of biomarkers comprises at least six biomarkers selected from TP53, N0TCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAP1.Docket No.: 68417-702.6017. The method of claim 1, wherein the panel of biomarkers comprises at least seven biomarkers selected from TP53, N0TCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT8. The method of claim 1, wherein the panel of biomarkers comprises at least eight biomarkers selected from TP53, N0TCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT9. The method of claim 1, wherein the panel of biomarkers comprises at least nine biomarkers selected from TP53, N0TCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT10. The method of claim 1, wherein the panel of biomarkers comprises at least ten biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT11 . The method of claim 1, wherein the panel of biomarkers comprises at least eleven biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT12. The method of claim 1, wherein the panel of biomarkers comprises at least twelve biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT13. The method of claim 1, wherein the panel of biomarkers comprises TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT14. The method of claim 1, further comprising identifying the disease utilizing the panel of biomarkers.

15. The method of claim 1, wherein the disease comprises cancer.

16. The method of claim 15, wherein the cancer is an oral cancer.

17. The method of claim 16, wherein the oral cancer comprises a cancer of the oral cavity.

18. The method of claim 16, wherein the oral cancer comprises one or more of: squamous cell carcinoma (SCC), salivary gland tumors, lymphoma, melanoma, sarcoma, or odontogenic tumors, or any combination thereof.

19. The method of claim 1, wherein the disease comprises an oral disease.

20. The method of claim 19, wherein the oral disease is periodontal disease.Docket No.: 68417-702.60121 . The method of claim 19, wherein the oral disease comprises one or more of caries, halitosis, gingivitis, periodontitis, stomatitis, oral thrush, oral herpes, salivary gland infections, dental abscess, dental erosion, dental fluorosis, dry mouth syndrome, hand, foot, and mouth disease, oral leukoplakia, oral lichen planus, candidiasis, canker sores, oral cysts, jaw disorders, mumps, oral papilloma, pemphigus vulgaris, pericoronitis, tooth decay, toothache, trench mouth, Vincent’s angina, oral ulcers, or any combination thereof.

22. The method of claim 1, wherein the treating the disease comprises applying a treatment capable of treating the disease.

23. The method of claim 22, wherein the treatment comprises one or more of: a chemotherapy, radiation, an immunotherapy, or a surgery.

24. The method of claim 15, wherein the treating the disease comprises applying a treatment capable of treating the cancer.

25. The method of claim 24, wherein the treatment comprises one or more of: a chemotherapy, radiation, an immunotherapy, or a surgery.

26. The method of claim 16, wherein the treating the disease comprises applying a treatment capable of treating the oral cancer.

27. The method of claim 26, wherein the treatment comprises one or more of: a chemotherapy, radiation, an immunotherapy, or a surgery.

28. The method of claim 1, wherein identifying the disease further comprises measuring an amount of the detected panel of biomarkers, measuring a frequency of the detected panel of biomarkers, measuring a concentration of the detected panel of biomarkers, generating a comparative analysis of the detected panel of biomarkers, or mapping patterns of the detected panel of biomarkers, or any combination thereof.

29. The method of claim 28, wherein the mapping patterns of the detected panel of biomarkers comprises mapping DNA methylation patterns of the detected panel of biomarkers to the enriched DNA sample.

30. The method of claim 28, wherein the mapping patterns of the detected panel of biomarkers comprises mapping DNA fusion patterns to the enriched DNA sample.31 . The method of claim 28, wherein the mapping patterns of the detected panel of biomarkers comprises mapping amounts of the at least one biomarker of the detected panel of biomarkers over areas of the enriched DNA sample or a derivative thereof.Docket No.: 68417-702.60132. The method of claim 1, further comprising utilizing a machine learning model to identify the disease in the patient based at least in part on the detected panel of biomarkers.

33. The method of claim 32, further comprising generating a prediction, using the machine learning model, of the patient’s response to the treating the disease based at least in part on the detected panel of biomarkers.

34. The method of claim 33, wherein the disease comprises a cancer.

35. The method of claim 34, wherein the treating the disease comprises applying a treatment responsive to the cancer.

36. The method of claim 35, wherein the treatment comprises one or more of: a chemotherapy, radiation, an immunotherapy, or a surgery.

37. The method of claim 34, wherein the cancer is an oral cancer.

38. The method of claim 33, further comprising selecting, using the machine learning model, one or more treatment options of a plurality of treatment options based at least in part on the prediction of the patient’s response to the treating the disease.

39. The method of claim 38, wherein the plurality of treatment options comprise one or more of: a chemotherapy, radiation, an immunotherapy, or a surgery, or any combination thereof.

40. The method of claim 39, further comprising utilizing the machine learning model to select a subset of the plurality of treatment options.41 . The method of claim 40, wherein the selected subset of the plurality of treatment options comprises one or more treatments having one or more of: high efficacy, high safety, low toxicity, low reactivity, low cross-reactivity, or any combination thereof.

42. A method of point of care testing to identify a disease in a patient, said method comprising: a. obtaining a saliva sample from the patient; b. extracting a DNA sample from the saliva sample; c. administering a reagent set to the DNA sample, wherein the reagent set comprises a panel of biomarkers, and generating a plurality of amplicons or derivatives thereof; d. sequencing the plurality of amplicons to generate a report comprising the presence or absence of one or more biomarkers of the panel of biomarkers;Docket No.: 68417-702.601 wherein the report identifies the disease in the patient.

43. The method of claim 42, wherein the panel of biomarkers comprises at least one biomarker selected from TP53, N0TCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT44. A method of point of care testing to identify a disease in a patient, said method comprising: a. obtaining a saliva sample from the patient; b. extracting a DNA sample from the saliva sample; c. enriching the DNA sample with a reagent set, wherein the reagent set comprises a panel of biomarkers, thereby generating a plurality of amplicons or derivatives thereof, wherein the panel of biomarkers comprises at least one biomarker selected from TP53, N0TCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and AD API; d. sequencing the plurality of amplicons or derivatives thereof to detect the panel of biomarkers, thereby identifying the disease in the patient.

45. The method of claim 44, wherein the panel of biomarkers comprises at least two biomarkers selected from TP53, N0TCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT46. The method of claim 44, wherein the panel of biomarkers comprises at least three biomarkers selected from TP53, N0TCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT47. The method of claim 44, wherein the panel of biomarkers comprises at least four biomarkers selected from TP53, N0TCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT48. The method of claim 44, wherein the panel of biomarkers comprises at least five biomarkers selected from TP53, N0TCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT49. The method of claim 44, wherein the panel of biomarkers comprises at least six biomarkers selected from TP53, N0TCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPTDocket No.: 68417-702.60150. The method of claim 44, wherein the panel of biomarkers comprises at least seven biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT51 . The method of claim 44, wherein the panel of biomarkers comprises at least eight biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT52. The method of claim 44, wherein the panel of biomarkers comprises at least nine biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT53. The method of claim 44, wherein the panel of biomarkers comprises at least ten biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT54. The method of claim 44, wherein the panel of biomarkers comprises at least eleven biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT55. The method of claim 44, wherein the panel of biomarkers comprises at least twelve biomarkers selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT56. The method of claim 44, wherein the panel of biomarkers comprises TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAPT57. The method of claim 44, wherein the disease comprises cancer.

58. The method of claim 57, wherein the cancer is an oral cancer.

59. The method of claim 58, wherein the oral cancer comprises cancer of the oral cavity.

60. The method of claim 58, wherein the oral cancer comprises one or more of: squamous cell carcinoma (SCC), salivary gland tumors, lymphoma, melanoma, sarcoma, or odontogenic tumors, or any combination thereof.61 . The method of claim 44, wherein the disease comprises an oral disease.

62. The method of claim 61, wherein the oral disease is periodontal disease.

63. The method of claim 61, wherein the oral disease comprises one or more of caries, halitosis, gingivitis, periodontitis, stomatitis, oral thrush, oral herpes, salivary gland infections, dental abscess,Docket No.: 68417-702.601 dental erosion, dental fluorosis, dry mouth syndrome, hand, foot, and mouth disease, oral leukoplakia, oral lichen planus, candidiasis, canker sores, oral cysts, jaw disorders, mumps, oral papilloma, pemphigus vulgaris, pericoronitis, tooth decay, toothache, trench mouth, Vincent’s angina, oral ulcers, or any combination thereof.

64. The method of claim 44, further comprising determining a treatment responsive to the disease and capable of treating the disease.

65. The method of claim 64, wherein the treatment comprises one or more of: a chemotherapy, radiation, an immunotherapy, or a surgery.

66. The method of claim 64, wherein the treatment comprises a treatment responsive to a cancer and capable of treating the cancer.

67. The method of claim 66, wherein the treatment comprises one or more of: a chemotherapy, radiation, an immunotherapy, or a surgery.

68. The method of claim 66, wherein the treatment comprises a treatment responsive to an oral cancer and capable of treating the oral cancer.

69. The method of claim 68, wherein the treatment comprises one or more of: a chemotherapy, radiation, an immunotherapy, or a surgery.

70. The method of claim 44, wherein the identifying the disease further comprises measuring an amount of the panel of biomarkers, measuring a frequency of the panel of biomarkers, measuring a concentration of the panel of biomarkers, generating a comparative analysis of the panel of biomarkers, or mapping patterns of the panel of biomarkers, or any combination thereof.71 . The method of claim 70, wherein the mapping patterns of the panel of biomarkers comprises mapping DNA methylation patterns of the panel of biomarkers to the enriched DNA sample.

72. The method of claim 70, wherein the mapping patterns of the panel of biomarkers comprises mapping DNA fusion patterns of the panel of biomarkers to the enriched DNA sample.

73. The method of claim 70, wherein the mapping patterns of the panel of biomarkers comprises mapping amounts of the one or more biomarkers of the panel of biomarkers over one or more areas of the enriched DNA sample or a derivative thereof.

74. The method of claim 44, further comprising utilizing a machine learning model to identify the disease in the patient based at least in part on the panel of biomarkers.

75. A method of identifying a subject as having or being at an increased risk of having a disease, comprising processing a biological sample obtained from an oral cavity of the subject at an oral pointDocket No.: 68417-702.601 of care location to generate a report identifying the subject as having or being at an increased risk of having the disease at an accuracy of at least 60%, wherein the biological sample is processed at a location that is remote from the oral point of care location.

76. A method of generating a disease risk profde for a subject, comprising:(a) obtaining a biological sample from an oral cavity of a subject, wherein the biological sample is obtained at an oral point of care location;(b) depositing the biological sample at the oral point of care location in a sample collection tube comprising a sample preservation reagent or a sample processing reagent, or both;(c) transferring the sample collection tube comprising the biological sample sealed therein to a remote sample processing location, wherein the remote sample processing location processes the biological sample or a derivative thereof to detect a panel of biomarkers of the disease risk profile for the subject, wherein the detected panel of biomarkers comprises at least one biomarker selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and ADAP1; and(d) transmitting to the oral point of care location or a healthcare professional location the generated disease risk profile for the subject, wherein the disease risk profile identifies a risk of the subject for having or developing the disease.

77. The method of claim 76, wherein the disease risk profile identifies the risk of the subject for having or developing the disease at an accuracy of at least 60%.

78. The method of claim 76, wherein the disease risk profile comprises one or more of a diagnostic risk prediction, a development risk prediction, a severity risk prediction, or an accuracy prediction of the subject for having the disease.

79. The method of claim 78, wherein the diagnostic risk prediction comprises one or more of a binary indication for the subject having the disease, a risk value for the subject having the disease, or a risk level for the subject having the disease.

80. The method of claim 78, wherein the development risk prediction comprises one or more of a binary indication for the subject developing the disease, a risk value for the subject developing the disease, or a risk level for the subject developing the disease.81 . The method of claim 78, wherein the severity risk prediction comprises one or more of a binary indication for the subject having a severe state of the disease, a binary indication for the subjectDocket No.: 68417-702.601 developing a severe state of the disease, a risk value for the subject having a severe state of the disease, a risk value for the subject developing a severe state of the disease, a value quantifying predicted severity of the disease, or a categorization of predicted severity of the disease.

82. The method of claim 78, wherein the risk level comprises one or more of a zero level, low level, medium level, high level, or very high level.

83. The method of claim 78, wherein the accuracy prediction comprises a value predicting an accuracy of one or more of: the diagnostic risk prediction, the development risk prediction, or the severity risk prediction.

84. The method of claim 76, wherein the disease comprises cancer.

85. The method of claim 84, wherein the cancer is an oral cancer.

86. The method of claim 76, wherein the disease comprises an oral disease.

87. The method of claim 86, wherein the oral disease is periodontal disease.

88. The method of claim 86, wherein the oral disease comprises one or more of caries, halitosis, gingivitis, periodontitis, stomatitis, oral thrush, oral herpes, salivary gland infections, dental abscess, dental erosion, dental fluorosis, dry mouth syndrome, hand, foot, and mouth disease, oral leukoplakia, oral lichen planus, candidiasis, canker sores, oral cysts, jaw disorders, mumps, oral papilloma, pemphigus vulgaris, pericoronitis, tooth decay, toothache, trench mouth, Vincent’s angina, oral ulcers, or any combination thereof.

89. The method of claim 76, wherein the biological sample comprises a saliva sample.

90. The method of claim 76, wherein the biological sample comprises an oral tissue sample.91 . The method of claim 90, wherein the oral tissue sample comprises one or more of an oral swab sample, an oral rinse sample, an oral brush biopsy sample, an oral mucosal tissue biopsy sample, a tongue scraping sample, a dental plaque sample, a gingival tissue sample, a gingival crevicular fluid sample, a jawbone sample, a tooth fragment sample, a dental pulp sample, a periapical sample, a tooth root tissue sample, a periodontal pocket sample, a buccal cells sample, or a periodontal ligament sample, or any combination thereof.

92. The method of claim 76, wherein the oral point of care location comprises a dental practice, an orthodontic practice, an endodontic practice, an oral surgeon practice, a maxillofacial surgeon practice, a periodontal practice, or a prosthodontia practice.

93. The method of claim 76, wherein the biological sample comprises one or more of microbial cells, DNA, methylated DNA, ctDNA, RNA, mRNA, miRNA, IncRNA, sRNA, siRNA, snRNA, sncRNA,Docket No.: 68417-702.601 circulating tumor cell RNA, proteins, enzymes, lipids, hormones, metabolites, cytokines, antigens, antibodies, exosomes, extracellular vesicles, or cancer antigens, or any combination thereof.

94. The method of claim 76, wherein the detecting the panel of biomarkers further comprises measuring an amount of the panel of biomarkers, measuring a frequency of the panel of biomarkers, measuring a concentration of the panel of biomarkers, generating a comparative analysis of the panel of biomarkers, or mapping patterns of the panel of biomarkers, or any combination thereof.

95. The method of claim 94, wherein the mapping patterns of the panel of biomarkers comprises mapping DNA methylation patterns or DNA fusion patterns of the panel of biomarkers to the biological sample.

96. The method of claim 94, wherein the mapping patterns of the detected panel of biomarkers comprises mapping amounts of the detected panel of biomarkers over areas of the biological sample or the derivative thereof.

97. The method of claim 76, further comprising storing the detected panel of biomarkers in a database of the remote sample processing location.

98. The method of claim 76, further comprising generating as part of the disease risk profde of the subject a prediction of the subject’s response to a treatment.

99. The method of claim 98, further comprising generating the disease risk profile for the subject based at least in part on the detected panel of biomarkers of the subject in comparison to a plurality of stored reference panels of biomarkers.

100. The method of claim 99, wherein the stored reference panels of biomarkers comprise previously detected panels of biomarkers stored on a database.

101. A method of point of care testing to identify a disease in a patient, said method comprising: a. enriching a deoxyribonucleic acid (DNA) sample derived from the patient with a reagent set, wherein the reagent set comprises a panel of biomarkers, thereby generating a plurality of amplicons or derivatives thereof, wherein the panel of biomarkers comprises at least one biomarker selected from TP53, NOTCH1, HRAS, CDKN2A, PIK3CA, FGFR3, CASP8, FAT1, EGFR, PTEN, KRAS, MET, and AD API; and b. sequencing the plurality of amplicons or derivatives thereof to detect the panel of biomarkers, thereby identifying the disease.

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