Systems and methods for detection of non-coding rnas

Abstract

The present disclosure relates generally to the use of small RNA biomarkers in a biological sample for early detection of invasive breast cancer (IBC) and noninvasive ductal carcinoma in-situ (DCIS).

Description

SYSTEMS AND METHODS FOR DETECTION OF NON-CODING RNASCROSS REFERENCE

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 512,579 filed July 7, 2023, U.S. Provisional Application No. 63 / 605,973 filed December 4, 2023, and U.S. Provisional Application No. 63 / 606,474 filed December 5, 2023, each of which is incorporated herein by reference in its entirety.BACKGROUND

[0002] Breast screening can improve breast cancer survival by enabling earlier detection; however, for the 45% of women with dense breasts, identification of early stage disease and small tumors can be challenging to detect and lead to false negatives. Nextgeneration liquid-biopsy strategies have the potential to complement mammography and improve early detection for more women, including those with dense breasts.SUMMARY

[0003] A category of cancer-associated, small non-coding RNAs, termed cell-free orphan RNAs (oncRNAs) demonstrate high sensitivity and specificity for early detection of invasive breast cancer (IBC) and noninvasive ductal carcinoma in-situ (DCIS).

[0004] Earlier detection of breast cancer is crucial for improved patient outcomes but cannot always be achieved for all women of screening age through mammography. Confounding issues such as breast density which masks the appearance of tumors and affects more than 40% of women, small tumor sizes, and imaging anomalies can result in false negatives and later stage diagnosis.

[0005] The present disclosure provides systems and methods for analyzing small RNA sequences that serve as biomarkers which are indicative of cancer, tissue of origin, and cancer status of a subject, which may be used to accurately diagnose cancer even at an early stage. The systems and methods described herein can complement or replace mammography and enable earlier detection of breast cancer. In some embodiments, the methods comprise use of cell-free small RNAs in a suitable sample. In some embodiments, the sample is a biological sample. In some embodiments, the biological sample is a whole blood, serum, or plasma sample.

[0006] In an aspect, provided herein is a method, comprising: (a) providing a sample from a subject, wherein the subject has or is suspected of having ductal carcinoma in situ (DCIS); (b) sequencing the sample, thereby generating a data set including data corresponding toa level of one or more non-coding RNA molecules that are expressed in DCIS cells and have a 90th percentile expression in non-cancerous cells of below 0.5 count per-million cells in the sample; (c) inputting the data into a trained algorithm to generate a classification of the sample as positive or negative for DCIS; and (d) electronically outputting the classification in a report.

[0007] In some cases, the trained algorithm has been trained with training samples comprising known positive-DCIS samples and known negative-DCIS samples.

[0008] In some cases, the method further comprises generating an additional classification of the sample as positive or negative for invasive breast cancer. In some cases, wherein the data set in (b) further comprises additional data corresponding to a level of one or more non-coding RNA molecules that are expressed in invasive breast-cancer cells and have a 90thpercentile expression in non-cancerous cells of below 0.5 count per-million cells. In some cases, the report further comprises the additional classification.

[0009] In some cases, the trained algorithm has been trained with training samples comprising: (i) non-cancerous samples, (ii) DCIS samples, and (iii) invasive breast cancer samples. In some cases, (c) comprises generating: (i) a probability of the sample being positive for DCIS, (ii) a probability of the sample being positive for invasive breast cancer (IBC), and (iii) a probability of the sample being negative for cancer. In some cases, if the detection score is greater than 0.3, the sample is classified as positive for breast cancer, wherein the breast cancer comprises DCIS or invasive breast cancer. In some cases, if the invasiveness score is greater than 2.7, the sample is classified as positive for invasive breast cancer.

[0010] In some cases, the sample comprises a cell-free sample. In some cases, the sample comprises plasma. In some cases, the sample comprises less than 1 mL of plasma. In some cases, the plasma comprises double-spun plasma. In some cases, the method further comprises monitoring a change over time in the classification by repeating (a)-(d) in another sample from the subj ect.

[0011] In some cases, the one or more non-coding RNA molecules that are expressed in DCIS cells and have a 90th percentile expression in non-cancerous cells of below 0.5 count per- million cells comprise nucleic acid sequences comprising one or a combination of SEQ ID NO: 1 through SEQ ID NO: 10122. In some cases, the one or more non-coding RNA molecules that are expressed in DCIS cells and have a 90th percentile expression in non-cancerous cells of below 0.5 count per-million cells comprise nucleic acid sequences that comprise at least 70%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99% sequence homology to SEQ ID NO: 1 through SEQ ID NO: 10122.

[0012] In some cases, the sequencing in (b) comprises sequencing by synthesis.

[0013] In another aspect, provided herein is a non-transitory computer-readable storage medium comprising a set of instructions recorded thereon, which, when executed by a processor, cause the processor to implement a method comprising: (a) sequencing a sample from a subject that has or is suspected of having ductal carcinoma in situ (DCIS), thereby generating a data set including data corresponding to a level of one or more non-coding RNA molecules that are expressed in DCIS cells and have a 90th percentile expression in non-cancerous cells of below 0.5 count per-million cells in the sample; (b) inputting the data into a trained algorithm to generate a classification of the sample as positive or negative for DCIS; and (c) electronically outputting the classification in a report.INCORPORATION BY REFERENCE

[0014] 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.BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings (also “figure” and “FIG.” herein), of which:

[0016] FIG. 1A shows an area under the receiver operating characteristic curve (AUROC) of the overall breast cancer prediction model as described herein, in accordance with some embodiments.

[0017] FIG. IB shows the sensitivity (at 90% specificity) of the overall breast cancer prediction model at various stages of cancer, in accordance with some embodiments.

[0018] FIG. 1C shows the sensitivity (at 90% specificity) of the overall breast cancer prediction model for various sizes of tumors, in accordance with some embodiments.

[0019] FIG. ID shows an area under the receiver operating characteristic curve(AUROC) of the distinguishing DCIS from invasive breast cancer, in accordance with some embodiments.

[0020] FIG. IE shows that sensitivity of the model in FIG. ID decreases for higher- grade DCIS samples, which can resemble invasive breast cancers, in accordance with some embodiments..

[0021] FIG. 2A shows an area under the receiver operating characteristic curve (AUROC) of the non-invasive ductal carcinoma in-situ (DCIS) prediction model as described herein, in accordance with some embodiments.

[0022] FIG. 2B shows the sensitivity (at 90% specificity) of the DCIS prediction model, in accordance with some embodiments

[0023] FIGs. 2C and 2D show the sensitivity (at 90% specificity) of the DCIS prediction model for different grades of DCIS, in accordance with some embodiments. FIG. 2C illustrates data exemplifying sensitivity at 90% specificity for various combined grade groups of DCIS. FIG. 2D illustrates data exemplifying sensitivity at 90% specificity for separated grade groups of DCIS.

[0024] FIG. 3A shows an area under the receiver operating characteristic curve (AUROC) of the invasive breast cancer (IBC) prediction model as described herein, in accordance with some embodiments.

[0025] FIGs. 3B and 3C show the sensitivity (at 90% specificity) of the IBC prediction model at various stages of cancer, in accordance with some embodiments. FIG. 3B illustrates data exemplifying sensitivity at 90% specificity for various grouped stages of cancer groups. FIG. 3C illustrates data exemplifying sensitivity at 90% specificity for separated stages of cancer groups.

[0026] FIG. 3D shows the sensitivity (at 90% specificity) of the IBC prediction model for various sizes of tumors, in accordance with some embodiments.

[0027] FIG. 3E shows the sensitivity (at 90% specificity) of the IBC prediction model for various subtypes of cancer, in accordance with some embodiments.

[0028] FIG. 4A illustrates a diagram exemplifying a process or system for oncRNA profiling and related cancer status prediction.

[0029] FIG. 4B illustrates a generative artificial intelligence (Al) model to classify samples as having a cancer presence, DCIS presence, or tumor absence, in accordance with some embodiments.

[0030] FIG. 4C illustrates a Sankey plot showing an initial test with 88% sensitivity for DCIS patients and 91.4% for invasive breast cancer patients, in accordance with some embodiments.

[0031] FIG. 4D illustrates an oncRNA risk score for samples with no tumors, DCIS, and various stages of invasive breast cancer, in accordance with some embodiments.

[0032] FIG. 5A shows data exemplifying an ROC curve for the overall breast cancer prediction model with an AUC at 95% confidence interval for false positive rate of DCIS and IBC samples, in accordance with some embodiments.

[0033] FIG. 5B shows data exemplifying sensitivity at 90% specificity for the overall breast cancer prediction model for a DCIS group and various cancer stages groups.

[0034] FIG. 5C shows data exemplifying sensitivity at 90% specificity for the overall breast cancer prediction model for various tumor T-category groups.

[0035] FIG. 6A shows data exemplifying an ROC curve for the DCIS prediction model with an AUC at 95% confidence interval for false positive rate of DCIS samples, in accordance with some embodiments.

[0036] FIG. 6B shows data exemplifying an ROC curve for the IBC prediction model with an AUC at 95% confidence interval for false positive rate of IBC samples, in accordance with some embodiments.

[0037] FIG. 7 shows a computer system that is programmed or otherwise configured to implement methods provided herein.DETAILED DESCRIPTION

[0038] While various 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 may 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.

[0039] The present disclosure provides systems and methods for analyzing small RNA sequences that serve as biomarkers which are indicative of invasive breast cancer (IBC) and ductal carcinoma in-situ (DCIS). In some embodiments, the methods comprise use of cell- free small RNAs in a suitable sample. In some embodiments, the sample is a biological sample. In some embodiments, the biological sample is a whole blood, serum, or plasma sample.

[0040] Orphan non-coding RNAs (oncRNAs) can be found in DCIS tissue and cancer tissue in higher levels than baseline, but are typically not found in noncancerous tissue at high levels. Specific oncRNAs can be present in different amounts in different types of cancers. Detection of a combination of two or more of oncRNAs can result in a “signature” of a unique oncRNA combination that can be indicative of a cancer or of a pre-cancerous condition such as DCIS. oncRNAs can be used in a liquid biopsy platform for sensitive and accurate early detection of breast cancer. oncRNAs can be used to differentiate invasive breast cancer from DCIS. The systems and methods described herein can be used to monitor patients diagnosed with DCIS to avoid over-treatment.

[0041] Methods and systems of the present disclosure may employ, unless otherwise indicated, techniques of molecular biology (including recombinant techniques), microbiology, cell biology, and biochemistry. Such techniques may be described by, for example, “Molecular Cloning: A Laboratory Manual”, 2ndedition (Sambrook et al., 1989); “Oligonucleotide Synthesis” (M.J. Gait, ed., 1984); “Animal Cell Culture” (R.I. Freshney, ed., 1987); “Methods in Enzymology” (Academic Press, Inc.); “Handbook of Experimental Immunology”, 4thedition (D.M. Weir & C.C. Blackwell, eds., Blackwell Science Inc., 1987); “Gene Transfer Vectors for Mammalian Cells” (J.M. Miller & M.P. Calos, eds., 1987); “Current Protocols in Molecular Biology” (F.M. Ausubel et al., eds., 1987); and “PCR: The Polymerase Chain Reaction”, (Mullis et al., eds., 1994), each of which is incorporated by reference in its entirety.

[0042] Sequencing methods can be used to generate sequencing reads derived small RNA. For example, small RNAs can be detected using Next Generation Sequencing (NGS), e.g., Sequencing-By-Synthesis using, for example, the NovaSeq, Next Seq, HiSeq, HiScan, GenomeAnalyzer, or MiSeq systems (Illumina, Inc., San Diego CA) or the Ultima Genomics Sequencer (Ultima Genomics, Newark CA). Small RNAs can also be detected using Ion Torrent Sequencing (Ion Torrent Systems, Inc., Gulliford, Conn.) or other suitable methods of semiconductor sequencing.

[0043] Determining a level of small RNA biomarkers may comprise counting a number of sequencing reads comprising a small RNA biomarker. The number of sequencing reads that comprise a given biomarker may correspond to an amount of a small RNA biomarker in a sample.

[0044] As used in the present disclosure and claims, the singular forms “a”, “an” and “the” include plural forms unless the context clearly dictates otherwise.

[0045] Whenever the term “at least,” “greater than,” or “greater than or equal to” precedes the first numerical value in a series of two or more numerical values, the term “at least” or “greater than” applies to each one of the numerical values in that series of numerical values.

[0046] Whenever the term “no more than,” “less than,” or “less than or equal to” precedes the first numerical value in a series of two or more numerical values, the term “no more than” or “less than” applies to each one of the numerical values in that series of numerical values.

[0047] It is understood that wherever embodiments are described herein with the language “comprising” otherwise analogous embodiments described in terms of “consisting of’ and / or “consisting essentially of’ are also provided. It is also understood that wherever embodiments are described herein with the language “consisting essentially of’ otherwise analogous embodiments described in terms of “consisting of’ are also provided.

[0048] The term “and / or” as used in a phrase such as “A and / or B” herein is intended to include both A and B; A or B; A (alone); and B (alone). Likewise, the term “and / or” as used in a phrase such as “A, B, and / or C” is intended to encompass each of the following embodiments: A, B, and C; A, B, or C; A or C; A or B; B or C; A and C; A and B; B and C; A (alone); B (alone); and C (alone).

[0049] The term “about” or “approximately” as used herein is meant to refer to within 5%, within 4%, within 3%, within 2%, within 1%, of a given value or range.

[0050] The term “biomarker”, as used herein, generally refers to a biological molecule present in an individual at varying concentrations useful in predicting the cancer status of an individual. A biomarker may include but is not limited to, nucleic acids, proteins and variants and fragments thereof. A biomarker may be DNA comprising the entire or partial nucleic acid sequence encoding the biomarker, or the complement of such a sequence.Biomarker nucleic acids useful in the systems and methods of the present disclosure are considered to include both DNA and RNA comprising the entire or partial sequence of any of the nucleic acid sequences of interest.

[0051] The term “bodily fluid”, as used herein, generally refers to a biological sample in the form of a bodily fluid comprising RNA (RNA) including blood (or a fraction of blood such as plasma or serum), lymph, mucus, tears, saliva, sputum, urine, semen, stool, CSF (cerebrospinal fluid), breast milk, and, ascites fluid. In some embodiments, the bodily fluid is urine. In some embodiments, the bodily fluid is fractionated serum comprising extracellular vesicles such as exosomes.

[0052] The terms “cancer” and “cancerous”, as used herein, generally refer to or describe the physiological condition in mammals in which a population of cells are characterized by unregulated cell growth. In some embodiments, the cancer is a breast cancer. In some embodiments, the cancer is a liver cancer. In some embodiments, the cancer is a gastric cancer. In some embodiments, the cancer is a lung cancer. In some embodiments, the cancer is a colorectal cancer. In some embodiments, the cancer is a kidney cancer.

[0053] The term "correlate" or "correlating", as used herein, generally refers to a statistical association between instances of two events, where events may include numbers, data sets, and the like. For example, when the events involve numbers, a positive correlation (also referred to herein as a "direct correlation") means that as one increases, the other increases as well. A negative correlation (also referred to herein as an "inverse correlation") means that as one increases, the other decreases. The present disclosure provides small RNAs, the levels of which are correlated with a particular outcome measure, such as between the level of a small RNA and the likelihood of developing breast cancer. For example, the increased level of a small RNA may be negatively correlated with a likelihood of good clinical outcome for the patient. In this case, for example, the patient may have a decreased likelihood of long-term survival without recurrence of the cancer and / or a positive response to a chemotherapy, and the like. Such a negative correlation indicates that the patient likely has a poor prognosis or may respond poorly to a chemotherapy, and this may be demonstrated statistically in various ways, e.g., by a high hazard ratio.

[0054] The terms “identical” or “percent identity” or “homology” in the context of two or more nucleic acids, as used herein, generally refer to two or more sequences or subsequences that are the same or have a specified percentage of nucleotides or amino acidresidues that are the same, when compared and aligned (introducing gaps, if necessary) for maximum correspondence, not considering any conservative amino acid substitutions as part of the sequence identity. The percent identity may be measured using sequence comparison software or algorithms or by visual inspection. Various algorithms and software that may be used to obtain alignments of amino acid or nucleotide sequences. These include, but are not limited to, BLAST, ALIGN, Megalign, BestFit, GCG Wisconsin Package, and variations thereof. In some embodiments, two nucleic acids of the present disclosure are substantially identical, meaning they have at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, and in some embodiments at least about 95%, 96%, 97%, 98%, 99% nucleotide or amino acid residue sequence identity, when compared and aligned for maximum correspondence, as measured using a sequence comparison algorithm or by visual inspection. In some embodiments, identity exists over a region of the sequences that is at least about 10, at least about 20, at least about 40-60 nucleotides, at least about 60-80 nucleotides or any integral value therebetween. In some embodiments, identity exists over a longer region than 60-80 nucleotides, such as at least about 80-100 nucleotides, and in some embodiments the sequences are substantially identical over the full length of the sequences being compared.

[0055] The term “level”, as used herein, generally refers to qualitative or quantitative determination of the number of copies of a non-coding RNA transcript. An RNA transcript exhibits an “increased level” when the level of the RNA transcript is higher in a first sample, such as in a clinically relevant subpopulation of patients (e.g., patients who have cancer), than in a second sample, such as in a related subpopulation (e.g., patients who do not have cancer). In the context of an analysis of a level of an RNA transcript in a tumor sample obtained from an individual patient, an RNA transcript exhibits “increased level” when the level of the RNA transcript in the subject trends toward, or more closely approximates, the level characteristic of a clinically relevant subpopulation of patients. The methods herein are useful in determining levels of certain small ncRNA sequences as well as the presence or absence of selected small ncRNA biomarkers and the absolute number of distinct small ncRNA sequences.

[0056] The term “metastasis”, as used herein, generally refers to the process by which a cancer spreads or transfers from the site of origin to other regions of the body with the development of a similar cancerous lesion at a new location. A “metastatic” or“ metastasizing” cell is one that loses adhesive contacts with neighboring cells and migrates (e.g., via the bloodstream or lymph) from the primary site of disease to secondary sites.

[0057] As generally used herein, the term “orphan non-coding RNA” or “oncRNA” refers to a category of small RNAs (smRNAs), typically small non-coding RNAs (small ncRNAs) that are present in tumors but largely absent in healthy tissue. By way of non-limiting example only, oncRNAs may refer to small ncRNAs that (i) have a CPM below 0.09 in 95% of normal serum samples; (ii) have an adjusted p-value of < 0.1 following an association study of tumor tissue versus normal tissues using a generalized linear model and correcting for known confounders, including age and sex; and (iii) as for small RNAs generally, are less than about 200 nt in length, such as in the range of 50-100 nt. In some cases, the term oncRNAs have a 90th percentile expression in non-cancerous (or non DCIS) cells of below 0.5 count per-million cells. In addition, while an oncRNA species is a non-coding RNA sequence, it may overlap, in part, with an adjacent coding sequence. Representative embodiments of detection and / or quantification of oncRNA molecules in a sample of a subject may be found in PCT International Application Pub. WO 2019 / 094781 and PCT International Application Pub. WO 2022 / 040106, both of which are expressly incorporated by reference herein in entirety.

[0058] A “patient response” , as used herein, generally may be assessed using any endpoint indicating a benefit to the patient, including, without limitation, (1) inhibition, to some extent, of tumor growth, including slowing down and complete growth arrest; (2) reduction in the number of tumor cells; (3) reduction in tumor size; (4) inhibition (i.e., reduction, slowing down or complete stopping) of tumor cell infiltration into adjacent peripheral organs and / or tissues; (5) inhibition (i.e. reduction, slowing down or complete stopping) of metastasis; (6) enhancement of anti-tumor immune response, which may, but does not have to, result in the regression or rejection of the tumor; (7) relief, to some extent, of one or more symptoms associated with the cancer; (8) increase in the length of survival following treatment; and / or (9) decreased mortality at a given point of time following treatment.

[0059] The terms “polynucleotide” and “nucleic acid” and “nucleic acid molecule”, as used herein, generally refer to polymers of nucleotides of any length, and include DNA and RNA. The polynucleotides can be deoxyribonucleotides, ribonucleotides, modified nucleotides or bases, and / or their analogs, or any substrate that can be incorporated into a polymer by DNA or RNA polymerase.

[0060] The terms “polypeptide” and “peptide” and “protein”, as used herein, generally refer to polymers of amino acids of any length. The polymer may be linear or branched, it may comprise modified amino acids, and it may be interrupted by non-amino acids. The terms also encompass an amino acid polymer that has been modified naturally or byintervention; for example, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other manipulation or modification, such as conjugation with a labeling component. Also included within the definition are, for example, polypeptides containing one or more analogs of an amino acid (including, for example, unnatural amino acids), as well as other modifications. It is understood that, because the polypeptides of the present disclosure may be based upon antibodies or fusion proteins, in certain embodiments, the polypeptides can occur as single chains or associated chains (e.g., dimers).

[0061] The term "prognosis", as used herein, generally refers to the prediction of the likelihood of cancer-attributable death or progression, including recurrence, metastatic spread, and drug resistance, of neoplastic disease, such as breast cancer.

[0062] The term “reference” RNA transcript, as used herein, generally refers to an RNA transcript whose level can be used to compare the level of an RNA transcript in a test sample. In an embodiment, reference RNA transcripts include housekeeping genes, such as betaglobin, alcohol dehydrogenase, or any other RNA transcript, the level or expression of which does not vary depending on the disease status of the cell containing the RNA transcript. In another embodiment, all of the assayed RNA transcripts, or a subset thereof, may serve as reference RNA transcripts.

[0063] The term “small RNA” (smRNA), as used herein, generally refers to RNA that has a length of less than 200 nucleotides and includes transfer RNA (tRNA), ribosomal RNA (rRNA), snoRNAs, microRNA (miRNA), siRNAs, small nuclear (snRNA), Y RNA, vault RNA, antisense RNA, tiRNA (transcription initiation RNA), TSSa-RNA (transcriptional startsite associated RNA) and piwiRNA (piRNA). It is to be understood that a "non-coding RNA" may overlap, in part, with a segment of RNA that does code for a protein, although the entirety of the non-coding RNA sequence per se does not. In some embodiments, a small smRNA as used herein is between 50 and 100 nucleotides. A smRNA may be of endogenous origin (e.g., a human small non-coding RNA) or exogenous origin (e.g., virus, bacteria, parasite). In some embodiments, any of the methods disclosed herein comprise detecting any one or combination of RNAs disclosed above.

[0064] The term “subject”, as used herein, generally refers to any animal (e.g., a mammal), including, but not limited to, humans, non-human primates, canines, felines, rodents, and the like. In some embodiments, the subject is a human subject. The terms "subject," "individual," and "patient" are used interchangeably herein. The terms "subject," "individual,"and "patient" thus encompass individuals having cancer (e.g., breast cancer), including those who have undergone or are candidates for resection (surgery) to remove cancerous tissue.

[0065] The term “therapeutically effective amount”, as used herein, generally refers to a quantity sufficient to achieve a desired therapeutic effect, for example, an amount which results in the prevention or amelioration of or a decrease in the symptoms associated with a disease that is being treated, e.g., disorders associated with cancer growth or a hyperproliferative disorder. The amount of compound administered to the subject will depend on the type and severity of the disease and on the characteristics of the individual, such as general health, age, sex, body weight and tolerance to drugs. It will also depend on the degree, severity and type of disease. The skilled artisan will be able to determine appropriate dosages depending on these and other factors. The regimen of administration can affect what constitutes an effective amount. Further, several divided dosages, as well as staggered dosages, can be administered daily or sequentially, or the dose can be continuously infused, or can be a bolus injection. Further, the dosages of the compound(s) of the present disclosure can be proportionally increased or decreased as indicated by the exigencies of the therapeutic or prophylactic situation. An effective amount of the compounds of the present disclosure, sufficient for achieving a therapeutic effect, may range from about 0.000001 mg per kilogram body weight per day to about 10,000 mg per kilogram body weight per day. In some embodiments, the dosage ranges are from about 0.0001 mg per kilogram body weight per day to about 100 mg per kilogram body weight per day. The compounds disclosed herein can also be administered in combination with each other, or with one or more additional therapeutic compounds.

[0066] The term “sample”, as used herein, generally refers to a biological sample obtained or derived from a source of interest, as described herein. In some embodiments, a source of interest comprises an organism, such as an animal or human. In some embodiments, a biological sample comprises biological tissue or fluid. In some embodiments, a biological sample may be or comprise bone marrow; whole blood; plasma; serum; blood cells; platelets; ascites; tissue or fine needle biopsy samples; cell-containing body fluids; free floating nucleic acids; sputum; saliva; urine; cerebrospinal fluid, peritoneal fluid; pleural fluid; pancreatic cyst fluid; feces; lymph; gynecological fluids; skin swabs; vaginal swabs; oral swabs; nasal swabs; washings or lavages such as a ductal lavages or bronchoalveolar lavages; aspirates; scrapings; bone marrow specimens; tissue biopsy specimens; surgical specimens; amniotic fluid; semen; bile; gastric juice; breast milk; synovial fluid; pericardial fluid; otorrhea; ascitic fluid; pus; prostatic secretions; aqueous or vitreous humor; endolymph; perilymph; colonic lavages;intestinal secretions; hepatic secretions; intracranial fluid, other body fluids, secretions, and / or excretions; and / or cells therefrom, etc. In some embodiments, a biological sample is or comprises cells obtained from an individual. In some embodiments, a sample is a “primary sample” obtained directly from a source of interest by any appropriate method. For example, in some embodiments, a primary biological sample is obtained by methods selected from the group consisting of biopsy (e.g., fine needle aspiration, tissue biopsy, bone marrow biopsy, laparoscopic biopsy, liquid biopsy, punch biopsy, swabbing, lumbar puncture, etc.), surgery, collection of body fluid (e.g., blood, lymph, feces etc.), etc. In some embodiments, as will be clear from context, the term “sample” refers to a preparation that is obtained by processing (e.g., by removing one or more components of and / or by adding one or more agents to) a primary sample. For example, filtering using a semi-permeable membrane. Such a “processed sample” may comprise, for example nucleic acids or proteins extracted from a sample or obtained by subjecting a primary sample to techniques such as amplification or reverse transcription of RNA, isolation and / or purification of certain components, etc.

[0067] In some embodiments, the biological sample used for determining the level of one or more small non-coding RNA biomarkers is a sample containing circulating small ncRNAs, e.g., extracellular small ncRNAs. Extracellular small ncRNAs freely circulate in a wide range of biological materials, including bodily fluids, such as fluids from the circulatory system, e.g., a blood sample or a lymph sample, or from another bodily fluid such as urine or saliva. Accordingly, in some embodiments, the biological sample used for determining the level of one or more small ncRNA biomarkers is a bodily fluid, for example, blood, fractions thereof, serum, plasma, urine, saliva, tears, sweat, semen, vaginal secretions, lymph, bronchial secretions, CSF, whole blood, stool, interstitial fluid, synovial fluid, gastric acid, sebum, mucus, bile, etc. In some embodiments, the sample is a sample that is obtained non-invasively, such as a stool sample. In some embodiments, the sample is a serum sample from a human.

[0068] In some embodiments, any of the methods disclosed herein comprise using a small volume sample. In some embodiments, the methods disclosed comprise isolating total RNA or small RNA, e.g., small non-coding RNA, and / or amplifying total or small RNA in a sample of no more than about 20 microliters of sample, 40 microliters of sample, 80 microliters of sample, 100 microliters of sample, 200 microliters of sample, 300 microliters of sample, 400 microliters of sample, 500 microliters of sample, 600 microliters of sample, 700 microliters of sample, 800 microliters of sample, 900 microliters of sample, 1 milliliter of sample, 1.1 milliliters of sample, 1.2 milliliters of samples, 1.3 milliliters of samples, 1.4 milliliters of samples, 1.5 milliliters ofsamples, 1.6 milliliters of samples, 1.7 milliliters of samples, 1.8 milliliters of samples, 1.9 milliliters of samples, 2.0 milliliters of sample. In some embodiments, the sample size is from about 25 microliters to about 2 milliliters of liquid sample in the form of subject plasma, whole blood or serum.

[0069] In some embodiments, the sample has a volume of about 0.001 mL to about 100 mL. In some embodiments, the sample has a volume of about 0.001 mL to about 0.01 mL, about 0.001 mL to about 0.1 mL, about 0.001 mL to about 0.5 mL, about 0.001 mL to about 1 mL, about 0.001 mL to about 10 mL, about 0.001 mL to about 100 mL, about 0.01 mL to about 0.1 mL, about 0.01 mL to about 0.5 mL, about 0.01 mL to about 1 mL, about 0.01 mL to about 10 mL, about 0.01 mL to about 100 mL, about 0.1 mL to about 0.5 mL, about 0.1 mL to about 1 mL, about 0.1 mL to about 10 mL, about 0.1 mL to about 100 mL, about 0.5 mL to about 1 mL, about 0.5 mL to about 10 mL, about 0.5 mL to about 100 mL, about 1 mL to about 10 mL, about 1 mL to about 100 mL, or about 10 mL to about 100 mL. In some embodiments, the sample has a volume of about 0.001 mL, about 0.01 mL, about 0.1 mL, about 0.5 mL, about 1 mL, about 10 mL, or about 100 mL. In some embodiments, the sample has a volume of at least about 0.001 mL, about 0.01 mL, about 0.1 mL, about 0.5 mL, about 1 mL, or about 10 mL. In some embodiments, the sample has a volume of at most about 0.01 mL, about 0.1 mL, about 0.5 mL, about 1 mL, about 10 mL, or about 100 mL.

[0070] Circulating small non-coding RNAs can include small non-coding RNAs in cells, extracellular small non-coding RNAs in microvesicles, in exosomes and extracellular small non-coding RNAs that are not associated with cells or microvesicles (extracellular, non-vesicular small non-coding RNA). In some embodiments, the biological sample used for determining the level of one or more small non-coding RNA biomarkers (e.g., a sample containing circulating small non-coding RNA) may contain cells. In other embodiments, the biological sample may be free or substantially free of cells (e.g., a plasma or serum sample). In some embodiments, a sample containing circulating small non-coding RNAs, e.g., extracellular small non-coding RNAs, is a blood-derived sample. Blood-derived sample types may include, e.g., a plasma sample, a serum sample, a blood sample, etc. In other embodiments, a sample containing circulating small non-coding RNAs is a lymph sample. Circulating small non-coding RNAs are also found in urine and saliva, and biological samples derived from these sources are likewise suitable for determining the level of one or more small non-coding RNA biomarkers.

[0071] In some embodiments, biological samples may be subjected to one or more processing operations as part of methods and systems as described herein. A processing operation may be carried out to isolate a substance from the biological sample, to purify the biological sample, to separate the biological sample into one or more fractions for further use or processing, to quantitate the amount of a substance, to detect the presence or absence or one or more substances, to transform or modify a substance for further downstream processing or analysis, or any combination thereof. For example, enzymes may be added to digest protein and remove contamination, or to inactivate nucleases that might otherwise degrade nucleic acids (RNA or DNA) during purification. The one or more processing operations may immediately follow sample collection, may be immediately prior to another processing or assaying operation, or may be carried out contemporaneously with another processing or assaying operation. The processing operation(s) may also be carried out on a sample or an intermediate in the process that has been appropriately stored for a designated amount of time. Any number of suitable processing operations may be performed on the biological sample or part thereof.

[0072] Processing operations as described herein may include, but are not limited to, immunoassays, enzyme-linked immunosorbent assays (ELISA), radioimmunoassays (RIA), ligand binding assays, functional assays, enzymatic assays, enzymatic treatments (e.g., with kinases, phosphatases, ligases, transcriptases, reverse transcriptases), enzymatic digestions (e.g., nucleases), spectroscopic assays (e.g., UV-vis spectroscopy, Fourier transform infrared spectroscopy, circular dichroism spectroscopy) spectrophotometric assays (e.g., ultraviolet-visible light spectrophotometry), immunoprecipitations (IP), sequencing reactions, electrophoresis, chromatography, enrichments, pull-downs, and mass spectrometry (MS). In some embodiments, a method as described herein comprises not performing one or more processing operations.

[0073] In some embodiments, the biological sample is subjected to one or more immunoprecipitation (IP) reactions, such as an in vitro or in vivo crosslinking and IP reaction (CLIP). The one or more IP reactions may enrich or pull-down one or more substances of interest. In some embodiments, an IP processing operation includes a cross-linking operation to covalently link two or more different substances (e.g., to link protein and DNA or protein and RNA). Immunoprecipitation of the cross-linked substances may provide an indication of biological substances that are associated with one another and / or may be used to enrich for specific substances that are known or suspected to interact with one another. In an example, one or more RNAs of interest are cross-linked to one or more corresponding proteins. IP of the proteins crosslinked with the RNAs allows for subsequent isolation and downstream processing of the RNAs of interest. In another example, an IP reaction may be carried out using antibodies specific for an RNA modification of interest, e.g., an adenosine modification (such as m6A, mxA, alternativepolyadenylation, or adenosine-to-inosine RNA editing), a uridine modification (such as conversion to pseudouridine), or other RNA modifications as alluded to above.

[0074] In some embodiments, the sample is subjected to one or more isolation operations. Isolation operations may target a general class of molecules (e.g., nucleic acids, such as RNAs) or a specific molecule (e.g., a specific annotated RNA molecule).

[0075] In some embodiments, a processing operation may comprise adding (e.g., spiking in) one or more substances. The one or more spike-in substances may be for any suitable purpose, including but not limited to, quality control, enrichment of target species, the act of depleting nontarget species, or any combination thereof. In some embodiments, a spike-in substance may comprise a synthetic biomolecule (e.g., nucleic acid, such as an RNA or a modified RNA, i.e., an RNA containing base modifications as alluded to above) corresponding to a target biomolecule. In some embodiments, the spike-in substance may comprise an endogenous or exogenous biomolecule. The spike-in molecule may be selected based on any appropriate property such as abundance or relative abundance, origin, sequence or part thereof, global or local structure (such as secondary or tertiary structure), or any combination thereof. Quantification of a spike-in substance following one or more downstream processing operations may be used for quality control as well as quantity control.

[0076] In some embodiments, a sample is processed or analyzed by a third party. For example, one party may conduct a purification or enrichment processing operation on a sample. The purified or enriched sample may then be subjected to a subsequent processing operation (e.g., a quantitation) by a different party.

[0077] In some embodiments, biological samples containing or suspected of containing one or more RNAs are subjected to one or more processing operations to remove or change RNA modifications that may inhibit further downstream processing (e.g., subsequent reverse transcription). Alternatively or additionally, chemical modifications may be removed or retained to determine the presence or absence of a relationship between chemical modifications and a disease state or any pathological state, e.g., a cancer state of a subject or population of subjects. Chemically modified RNA bases may include, without limitation, N6-methyladenosine (m6A), inosine (I), 5-methylcytosine (m5C), pseudouridine ( ), 5-hydroxymethylcytosine (hm5C), N1- methyladenosine (nfA), or 7-m ethylguanosine (m7G). For example, biological samples which contain or are suspected of contained methylated RNAs (e.g, comprising m6A, m5C, hm5C, m1A, or m7G) may be treated with one or more demethylation enzymes (e.g, AlkB) to remove alkyl groups which may interfere with downstream processing (e.g., by reverse transcriptases).

[0078] In some embodiments, biological samples containing or suspected of containing RNAs are subjected to one or more processing operations to facilitate downstream processing operations (e.g., isolation). In some embodiments, a sample containing or suspected of containing RNAs may be treated with one or more enzymes, cofactors, and / or other reagents to bring about an end-repair process. The sample containing or suspected of containing one or more RNAs is subjected to treatment with a polynucleotide kinase (PNK) to ensure that end-modified RNA species having a 3'-phosphate group are not lost to further analysis. That is, PNK enzymes dephosphorylate 3'-phosphate RNA species and thus allow for subsequent polyadenylation and inclusion in downstream processing steps.

[0079] The terms “treating” or “treatment” or “ treat”, as used herein, generally refer to both 1) therapeutic measures that cure, slow down, lessen symptoms of, and / or halt progression of a diagnosed pathologic condition or disorder and 2) prophylactic or preventative measures that prevent or slow the development of a targeted pathologic condition or disorder. Thus those in need of treatment include those already diagnosed with the disorder; those prone to have the disorder; and those in whom the disorder is to be prevented. In some embodiments, a subject is successfully “treated” according to the methods of the present disclosure if the patient shows one or more of the following: a reduction in the number of and / or complete absence of cancer cells; a reduction in the tumor size; an inhibition of tumor growth; inhibition of and / or an absence of cancer cell infiltration into peripheral organs including the spread of cancer cells into soft tissue and bone; inhibition of and / or an absence of tumor or cancer cell metastasis; inhibition and / or an absence of cancer growth; relief of one or more symptoms associated with the specific cancer; reduced morbidity and mortality; improvement in quality of life; reduction in tumorigenicity; reduction in the number or frequency of cancer stem cells; or some combination of such effects.

[0080] The term "tumor", as used herein, generally refers to all neoplastic cell growth and proliferation, whether malignant or benign, and all pre-cancerous and cancerous cells and tissues.

[0081] The term "tumor sample", as used herein, generally refers to a sample comprising tumor material obtained from a cancer patient. The term encompasses tumor tissue samples, for example, tissue obtained by surgical resection and tissue obtained by biopsy, such as for example, a core biopsy or a fine needle biopsy. In a particular embodiment, the tumor sample is a fixed, wax-embedded tissue sample, such as a formalin-fixed, paraffin-embedded tissue sample. Additionally, the term "tumor sample" encompasses a sample comprising tumorcells obtained from sites other than the primary tumor, e.g., circulating tumor cells. The term also encompasses cells that are the progeny of the patient's tumor cells, e.g. cell culture samples derived from primary tumor cells or circulating tumor cells. The term further encompasses samples that may comprise protein or nucleic acid material shed from tumor cells in vivo, e.g., bone marrow, blood, plasma, serum, and the like. The term also encompasses samples that have been enriched for tumor cells or otherwise manipulated after their procurement and samples comprising polynucleotides and / or polypeptides that are obtained from a patient' s tumor material.

[0082] RNA sequencing can be used to identify a category of cancer-associated, small non-coding RNAs, termed orphan non-coding RNAs (oncRNAs). OncRNAs can be actively secreted from living cancer cells and are stable and abundant in the blood of cancer patients. A catalog of hundreds of thousands of oncRNAs and thousands of patient oncRNA profiles, spanning all major cancer types, can be used to diagnose cancer or ductal carcinoma in situ (DCIS). When combined with proprietary artificial intelligence technology, the systems and methods described herein can have increased sensitivity and specificity, as well as the ability to reveal dynamic changes in the biology of a patient’s tumor over time. The systems and methods described herein can be used across multiple cancer care settings such as screening and early detection, monitoring, molecular residual disease and therapy selection.

[0083] The systems and methods described herein can be used to detect cancer at the earliest stages and the smallest tumor sizes, as well as ductal carcinoma in situ (DCIS), using a standard blood sample. In some cases, stage I breast cancer sensitivity is about 87% at 90% specificity. In some cases, tumor size Tla-b (10 mm or smaller) sensitivity is about 81% at 90% specificity. In addition to distinctively reporting tumor size data, the systems and methods described herein can be used to detect DCIS at a reported sensitivity of about 78% at 90% specificity. Overall sensitivity across all breast cancer stages and DCIS can be about 85% at 90% specificity, far exceeding DNA-based liquid biopsy performance which falls short in breast cancer detection due to low shedding rates and technical barriers.

[0084] As illustrated in FIG. 4A and 4B, the systems and methods described herein can be used to indicate cancer presence, DCIS presence, or tumor absence in a liquid biopsy sample obtained from a patient (i.e., serum, plasma, etc.) 406. The patient’s individual oncRNA profile (small serum RNA profile) 405 can be determined from the patient sample. The patient’s oncRNA profile 405 can be input into a generative Al model 403 that compares the patient’soncRNA profile 405 with a breast cancer oncRNA fingerprint 402 to make one or more oncRNA-based predictions 404.

[0085] The breast cancer oncRNA fingerprint 402 may be obtained through an oncRNA discovery model 401. In some embodiments, an independent non-cancer serum reference cohort can be received as input by the oncRNA discovery model 401. In some embodiments, samples from patients with DCIS or invasive breast cancer tissue (or both) are input into the oncRNA discovery model 401.

[0086] In some embodiments, the oncRNA discovery model 401 can generate a cancer oncRNA fingerprint 402. In some embodiments, the cancer oncRNA fingerprint 402 can be received as input by a generative Al model 403. In some embodiments, patient serum or plasma 406 can be processed to generate a small RNA profile 405. In some embodiments, the generative Al model 403 can additionally receive input data comprising the patient oncRNA profile 405. In some embodiments, the generative Al model 403 can process the input cancer oncRNA fingerprint data 402 and the patient oncRNA profile 405 to produce one or more oncRNA-based predictions 404.

[0087] FIG. 4B shows a detailed view of the generative Al model 403. A generative Al model similar to that disclosed in “Deep generative Al models analyzing circulating orphan non-coding RNAs enable accurate detection of early-stage non-small cell lung cancer. Mehran Karimzadeh et al. medRxiv 2024.04.09.24304531; doi: htps / / dpi.org / 10.1101 / 2024 04,09.2430 5^ can be used. This paper is herein incorporated by reference in entirety. The model 403 can distinguish three groups of samples during training: 1) samples from individuals without any diagnosis of breast cancer or DCIS (healthy controls), 2) samples with individuals with a diagnosis of DCIS, and 3) samples from individuals with a diagnosis of breast cancer. The model 403 can estimate the probability of a patient sample belonging to either of these three groups. In some cases, the model provides three probability scores (one for each of the groups described above).

[0088] As described herein, “a detection score” generally refers to the probability of cancer presence, which would encompass both DCIS and invasive breast cancer. A detection score scan be calculated as follows:

[0089] A detection score can fall withing a range of -6.9 and 6.9. A detection score lower than zero will indicate that it is more likely that a patient has no tumor than DCIS or IBC. A detection score above zero will indicate that it is more likely that a patient has DCIS or IBC than no tumor. The higher the detection score, the more likely it is that a patient has cancer (IBC or DCIS). In some cases, the model 403 outputs a binary indication on whether or not a sample is cancerous. The binary indication can depend on a detection score cut-off value. The detection cut-off score at 90% specificity based on a 5-fold cross validated cohort range of scores can be around 0.304. The detection cut-off score can be about 0.1 to about 0.5. The detection cut-off score can be about 0.1 to about 0.2, about 0.1 to about 0.3, about 0.1 to about 0.4, about 0.1 to about 0.5, about 0.2 to about 0.3, about 0.2 to about 0.4, about 0.2 to about 0.5, about 0.3 to about 0.4, about 0.3 to about 0.5, or about 0.4 to about 0.5. The detection cut-off score can be about 0.1, about 0.2, about 0.3, about 0.4, or about 0.5. The detection cut-off score can be at least about 0.1, about 0.2, about 0.3, or about 0.4. The detection cut-off score can be at most about 0.2, about 0.3, about 0.4, or about 0.5.

[0090] As described herein, “an invasiveness score” can be used to distinguish invasive breast cancer (IBC) from DCIS. An invasiveness score can be calculated from a sample that has passed the detection score cut-off value (i.e, has already been predicted as cancerous). An invasiveness score scan be calculated as follows:

[0091] An invasiveness score can fall withing a range of -8.4 and 14.2. A detection score lower than zero will indicate that it is more likely that a patient has DCIS than IBC. A detection score above zero will indicate that it is more likely that a patient has IBC than DCIS. The higher the invasiveness score, the more likely it is that a patient has invasive cancer (i.e., cancer that has migrated from the ducts and entered surrounding breast tissue). In some cases, the model 403 outputs a binary indication on whether or not the cancer is invasive. The binary indication can depend on a invasiveness score cut-off value. The invasiveness cut-off score at 90% specificity based on a 5-fold cross validated cohort range of scores can be around 2.373. The invasiveness cut-off score can be about 1 to about 4. The invasiveness cut-off score can be about 1 to about 1.5, about 1 to about 2, about 1 to about 2.2, about 1 to about 2.3, about 1 to about 2.4, about 1 to about 2.5, about 1 to about 3, about 1 to about 3.5, about 1 to about 4, about 1.5 to about 2, about 1.5 to about 2.2, about 1.5 to about 2.3, about 1.5 to about 2.4, about 1.5 to about 2.5, about 1.5 to about 3, about 1.5 to about 3.5, about 1.5 to about 4, about 2 to about 2.2, about 2 to about 2.3, about 2 to about 2.4, about 2 to about 2.5, about 2 to about 3, about 2 to about 3.5, about 2 to about 4, about 2.2 to about 2.3, about 2.2 to about 2.4, about 2.2 to about 2.5, about 2.2 to about3, about 2.2 to about 3.5, about 2.2 to about 4, about 2.3 to about 2.4, about 2.3 to about 2.5, about 2.3 to about 3, about 2.3 to about 3.5, about 2.3 to about 4, about 2.4 to about 2.5, about 2.4 to about 3, about 2.4 to about 3.5, about 2.4 to about 4, about 2.5 to about 3, about 2.5 to about 3.5, about 2.5 to about 4, about 3 to about 3.5, about 3 to about 4, or about 3.5 to about 4. The invasiveness cut-off score can be about 1, about 1.5, about 2, about 2.2, about 2.3, about 2.4, about 2.5, about 3, about 3.5, or about 4. The invasiveness cut-off score can be at least about 1, about 1.5, about 2, about 2.2, about 2.3, about 2.4, about 2.5, about 3, or about 3.5. The invasiveness cut-off score can be at most about 1.5, about 2, about 2.2, about 2.3, about 2.4, about 2.5, about 3, about 3.5, or about 4.

[0092] FIG. 4C shows a Sankey plot showing an initial test with 88% sensitivity for DCIS patients and 91.4% sensitivity for invasive breast cancer patients. In other words, 91.4% of the invasive breast cancer samples were predicted as having a breast tumor, and 88% of DCIS samples were predicted as having a breast tumor. The second rule-out test from the same oncRNA profile has sensitivity of 62.4% at 90% specificity.

[0093] FIG. 4D shows that the invasiveness score is lowest when a tumor is not present and increases for DCIS and later-stage cancer samples.EXAMPLES

[0094] The Cancer Genome Atlas (TCGA) small RNA-seq database was used to determine a library of 20,538 small non-coding RNAS (oncRNAs), through a female-specific analysis, that were significantly enriched among 1,103 breast tumors compared to 349 controls from normal tissues spanning multiple tissue sites. The diagnostic performance of these oncRNAs were assessed in an independent cohort of serum samples from 708 female individuals, including 380 breast cancer patients (221 IBC and 159 DCIS; mean age: 58.0 ± 13.4 years; ever-smoker: 25.8%) (Table 7) and 328 age-matched controls (mean age: 58.4 ± 137 years; ever-smoker: 19.8%) (Table 7). The small RNA content from 1 mL of serum from each patient was sequenced at an average depth of 21.6 million 50-bp single-end reads. 19,736 (96%) of the breast cancer-specific oncRNA library was detected within at least one sample from the study cohort (Table 7). A generative Al model was trained, using 5-fold cross-validation to predict breast cancer and then distinguish invasive BC from DCIS.Table 1: Study Demographics

[0095] As shown in FIG. 1A, the oncRNA-based model achieved an overall ROC of 0.96 (95% CI, 0.94-0.97) for prediction of breast cancer (IBC or DCIS) versus cancer-free controls. As shown in FIG. IB, sensitivities (at 90% specificity) in DCIS (Stage 0), Stage I, Stage II, Stage III, and Stage IV diagnoses were 0.88, 0.90, 0.91, 0.90, and 0.94, respectively. As shown in FIG. 1C, sensitivities (at 90% specificity) in Tumor Sizes TO, Tla-b, Tic, T2, T3, and T4 were 0.88, 0.88, 0.88, 0.92, 0.90, and 1.00, respectively. The TNM staging system is used to describe the amount and spread of cancer in a patient’s body. T describes the size of the tumor and any spread of cancer into nearby tissue; N describes spread of cancer to nearby lymph nodes; and M describes metastasis. The TNM staging system describes tumor sizes using a T numbersystem. TX: Main tumor cannot be measured. TO: Main tumor cannot be found. Tl, T2, T3, T4 refer to the size and / or extent of the main tumor. The higher the number after the T, the larger the tumor or the more it has grown into nearby tissues. T's may be further divided to provide more detail, such as T3a and T3b (Table 5).

[0096] As shown in FIG. ID, the oncRNA-based model can be used to distinguish DCIS from invasive breast cancer with an overall ROC of 0.96 (95% CI, 0.87-0.93). As shown in FIG. IE, sensitivities (at 90% specificity) of the model decrease for higher-grade DCIS samples. Stratified sensitivity for DCIS G1 samples was 0.87. Stratified sensitivity for DCIS G2 samples was 0.63. Stratified sensitivity for DCIS G3 samples was 0.44.

[0097] Restricting to DCIS alone, an AUC of 0.94 (95% CI, 0.91-0.98) was observed, as shown in FIG. 2A. As shown in FIG. 2B, sensitivities at 90% specificity were 0.88 (0.77-0.95) for DCIS. Within DCIS, sensitivity at lower grades (G1 / G2) was 0.79 (0.49-0.95; n=14) and high grade (G2-3 / G3) sensitivity was 0.88 (0.64-0.99; n=17) (FIG. 2C). 27 samples had no grading information available. FIG. 2D shows sensitivities at 90% specificity for DCIS grades Gl, G2, G2-3, and G3.

[0098] Restricting to IBC alone, an AUC of 0.90 (95% CI, 0.86-0.94) was observed, as shown in FIG. 3A. As shown in FIG. 3B, sensitivities in early-stage (IA / IB / IIA) and latestage (IIB or higher) diagnoses were 0.90 (0.83-0.94, n=135) and 0.92 (0.84-0.97, n=86), respectively. As shown in FIG. 3C, sensitivities in Stage I, Stage II, Stage III, and Stage IV diagnoses were 0.89, 0.91, 0.88, and 1.00, respectively. Sensitivities at 90% specificity were also high for small tumors at 0.88 (0.70-0.98) for Tla-b (>lmm to <10mm; n=26) and 0.88 (0.78- 0.94) for Tic (>10mm to <20mm; n=72) (FIG. 3D). While the IBC cohort primarily consisted of individuals with Luminal A breast cancer, the model had strong prediction across all breast cancer subtypes with sensitivities of 0.90 (0.85-0.94), 0.83 (0.67-0.94), 0.83 (0.52-0.98), and 1.00 (0.48-1.00) for luminal A (n=164), luminal B (n=36), HER2 enriched (n=12), and triple negative (n=5), respectively (FIG. 3E).

[0099] As shown in FIG. 5A, for example, the oncRNA-based model achieved an overall AUC of 0.95 (95% CI, 0.93-0.96) for prediction of breast cancer (IBC and DCIS samples) versus cancer-free controls. As shown in FIG. 5A, the specificity was 0.85 (95% CI, 0.81-0.89). As shown in FIG. 5B, restricting to DCIS alone, in DCIS and in Stage I, Stage II, Stage III, and Stage IV, diagnoses were 0.78, 0.87, 0.86, 0.86, and 1.00, respectively. As shownin FIG. 5C, restricting to IBC alone, in Tis and in Tla-b, Tic, T2, T3, and T4, diagnoses were 0.78, 0.81, 0.90, 0.89, 0.90, and 0.75, respectively.

[0100] As shown in FIG. 6A, for example, restricting to DCIS alone, an AUC of 0.93 (95% CI, 0.9-0.96) was observed, with a specificity of 0.78 (95% CI, 0.65-0.87). As shown in FIG. 6B, for example, restricting to IBC alone, an AUC of 0.95 (95% CI, 0.93-0.97) was observed, with a specificity of 0.87 (95% CI, 0.82-0.91).

[0101] The sensitivity of cancer detection in IBC at 90% specificity ranged from 0.88 to 1.00 across cancer stages (Table 2) and tumor sizes (Table 3). For the earliest stages of IBC, relatively high sensitivities of 0.89 (95% CI, 0.80 - 0.95) in stage I and 0.91 (95% CI, 0.82 - 0.96) in stage II (Table 2) were observed.Table 2: Model sensitivity in IBC by tumor stageFor each tumor stage, as defined by the AJCC 7th Edition breast cancer staging system, sensitivity and 95% Pearson-Clopper confidence intervals (CI) are reported at 90% specificity for the number of samples (N).Table 3: Model sensitivity in IBC by tumor sizeFor each tumor stage, as defined by the AJCC 7th Edition breast cancer staging system, sensitivity and 95% Pearson-Clopper confidence intervals (CI) are reported at 90% specificity for the number of samples (N).Table 4: Model sensitivity in IBC by subtype.

[0102] The study demonstrated the utility of analyzing oncRNAs as a blood-based biomarker using an Al algorithm for sensitive and accurate early detection of breast cancer in a large cohort. Additionally, this oncRNA-based assay performs well in detecting small, early- stage breast tumors, as well as the earliest precursor of breast cancer, DCIS, at the lowest grade.Computer Systems

[0103] The present disclosure provides computer systems that are programmed to implement methods of the disclosure. FIG. 7 shows a computer system 101 that is programmed or otherwise configured to, for example, implement a trained machine learning classifier to determine the cancer status a subject from small RNA sequencing read derived from a sample from the subject, plan a course of treatment for a subject based on small RNA sequencing reads derived from a sample from the subject, monitor the cancer status of a subject over time (e.g., before and after treatment) based on small RNA sequencing reads derived from a sample from the subject, measure the efficacy or tolerability of a therapeutic intervention for an individual based on on small RNA sequencing reads derived from a sample from the subject, or process a biological sample as described herein.

[0104] The computer system 101 can regulate various aspects of analysis, calculation, and generation of the present disclosure, such as, for example, implement a trained machine learning classifier to determine the cancer status a subject on small RNA sequencing reads derived from a sample from the subject, planning a course of treatment for a subject based on small RNA sequencing reads derived from a sample from the subject, monitoring the cancer status of a subject over time (e.g., before and after treatment) based on small RNA sequencing reads derived from a sample from the subject, measuring the efficacy or tolerability of a therapeutic intervention for an individual based on small RNA sequencing reads derived from a sample from the subject, or processing a biological sample as described herein. The computersystem 101 can be an electronic device of a user or a computer system that is remotely located with respect to the electronic device. The electronic device can be a mobile electronic device.

[0105] The computer system 101 includes a central processing unit (CPU, also “processor” and “computer processor” herein) 105, which can be a single core or multi core processor, or a plurality of processors for parallel processing. The computer system 101 also includes memory or memory location 110 (e.g., random-access memory, read-only memory, flash memory), electronic storage unit 115 (e.g., hard disk), communication interface 120 (e.g., network adapter) for communicating with one or more other systems, and peripheral devices 125, such as cache, other memory, data storage and / or electronic display adapters. The memory 110, storage unit 115, interface 120 and peripheral devices 125 are in communication with the CPU 105 through a communication bus (solid lines), such as a motherboard. The storage unit 115 can be a data storage unit (or data repository) for storing data. The computer system 101 can be operatively coupled to a computer network (“network”) 130 with the aid of the communication interface 120. The network 130 can be the Internet, an internet and / or extranet, or an intranet and / or extranet that is in communication with the Internet.

[0106] The network 1310 In some embodiments is a telecommunication and / or data network. The network 130 can include one or more computer servers, which can enable distributed computing, such as cloud computing. For example, one or more computer servers may enable cloud computing over the network 130 (“the cloud”) to perform various aspects of analysis, calculation, and generation of the present disclosure, such as, for example, implementing a trained machine learning classifier to determine the cancer status a subject on small RNA sequencing reads derived from a sample from the subject, planning a course of treatment for a subject based on small RNA sequencing reads derived from a sample from the subject, monitoring the cancer status of a subject over time (e.g., before and after treatment) based on small RNA sequencing reads derived from a sample from the subject, measuring the efficacy or tolerability of a therapeutic intervention for an individual based on small RNA sequencing reads derived from a sample from the subject, or processing a biological sample as described herein. Such cloud computing may be provided by cloud computing platforms such as, for example, Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform, and IBM cloud. The network 130, In some embodiments with the aid of the computer system 101, can implement a peer-to-peer network, which may enable devices coupled to the computer system 101 to behave as a client or a server.

[0107] The CPU 105 may comprise one or more computer processors and / or one or more graphics processing units (GPUs). The CPU 105 can execute a sequence of machine- readable instructions, which can be embodied in a program or software. The instructions may be stored in a memory location, such as the memory 110. The instructions can be directed to the CPU 105, which can subsequently program or otherwise configure the CPU 105 to implement methods of the present disclosure. Examples of operations performed by the CPU 105 can include fetch, decode, execute, and writeback.

[0108] The CPU 105 can be part of a circuit, such as an integrated circuit. One or more other components of the system 101 can be included in the circuit. In some embodiments, the circuit is an application specific integrated circuit (ASIC).

[0109] The storage unit 115 can store files, such as drivers, libraries and saved programs. The storage unit 115 can store user data, e.g., user preferences and user programs. The computer system 101 in some embodiments can include one or more additional data storage units that are external to the computer system 101, such as located on a remote server that is in communication with the computer system 101 through an intranet or the Internet.

[0110] The computer system 101 can communicate with one or more remote computer systems through the network 130. For instance, the computer system 101 can communicate with a remote computer system of a user. Examples of remote computer systems include personal computers (e.g., portable PC), slate or tablet PC’s (e.g., Apple® iPad, Samsung® Galaxy Tab), telephones, Smart phones (e.g., Apple® iPhone, Android-enabled device, Blackberry®), or personal digital assistants. The user can access the computer system 101 via the network 130.

[0111] Methods as described herein can be implemented by way of machine (e.g., computer processor) executable code stored on an electronic storage location of the computer system 101, such as, for example, on the memory 110 or electronic storage unit 115. The machine executable or machine readable code can be provided in the form of software. During use, the code can be executed by the processor 105. In some embodiments, the code can be retrieved from the storage unit 115 and stored on the memory 110 for ready access by the processor 105. In some situations, the electronic storage unit 115 can be precluded, and machineexecutable instructions are stored on memory 110.

[0112] The code can be pre-compiled and configured for use with a machine having a processer adapted to execute the code or can be compiled during runtime. The code can be supplied in a programming language that can be selected to enable the code to execute in a precompiled or as-compiled fashion.

[0113] Embodiments of the systems and methods provided herein, such as the computer system 101, can be embodied in programming. Various aspects of the technology may be thought of as “products” or “articles of manufacture” typically in the form of machine (or processor) executable code and / or associated data that is carried on or embodied in a type of machine readable medium. Machine-executable code can be stored on an electronic storage unit, such as memory (e.g., read-only memory, random-access memory, flash memory) or a hard disk. “Storage” type media can include any or all of the tangible memory of the computers, processors or the like, or associated modules thereof, such as various semiconductor memories, tape drives, or disk drives, which may provide non-transitory storage at any time for the software programming. All or portions of the software may at times be communicated through the Internet or various other telecommunication networks. Such communications, for example, may enable loading of the software from one computer or processor into another, for example, from a management server or host computer into the computer platform of an application server. Thus, another type of media that may bear the software elements includes optical, electrical and electromagnetic waves, such as used across physical interfaces between local devices, through wired and optical landline networks and over various air-links. The physical elements that carry such waves, such as wired or wireless links, optical links or the like, also may be considered as media bearing the software. As used herein, unless restricted to non-transitory, tangible “storage” media, terms such as computer or machine “readable medium” refer to any medium that participates in providing instructions to a processor for execution.

[0114] Hence, a machine readable medium, such as computer-executable code, may take many forms, including a tangible storage medium, a carrier wave medium or physical transmission medium. Non-volatile storage media include, for example, optical or magnetic disks, such as any of the storage devices in any computer(s) or the like, such as may be used to implement the databases, etc. shown in the drawings. Volatile storage media include dynamic memory, such as main memory of such a computer platform. Tangible transmission media include coaxial cables; copper wire and fiber optics, including the wires that comprise a bus within a computer system. Carrier-wave transmission media may take the form of electric or electromagnetic signals, or acoustic or light waves such as those generated during radiofrequency (RF) and infrared (IR) data communications. Common forms of computer-readable media therefore include for example: a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any other optical medium, punch cards paper tape, any other physical storage medium with patterns of holes, a RAM, a ROM, a PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave transporting data or instructions, cables or links transporting such a carrier wave, or any other medium from which a computer may read programming code and / or data. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.

[0115] The computer system 101 can include or be in communication with an electronic display 135 that comprises a user interface (UI) 140 for providing, for example, a visual display indicative of the cancer status a subject based on small RNA sequencing reads derived from a sample from the subject, a visual display indicative of a planned course of treatment for a subject based on small RNA sequencing reads derived from a sample from the subject, a visual display indicative of monitoring the cancer status of a subject over time (e.g., before and after treatment) based on small RNA sequencing reads derived from a sample from the subject, a visual display indicated of a measurement the efficacy or tolerability of a therapeutic intervention for an individual based on small RNA sequencing reads derived from a sample from the subject, or a visual display indicative of processing a biological sample as described herein. Examples of UIs include, without limitation, a graphical user interface (GUI) and web-based user interface.

[0116] Methods and systems of the present disclosure can be implemented by way of one or more algorithms. An algorithm can be implemented by way of software upon execution by the central processing unit 105. The algorithm can, for example, determine the cancer status a subject from small RNA expression data derived from a sample from the subject, plan a course of treatment for a subject based on small RNA sequencing reads derived from a sample from the subject, monitor the cancer status of a subject over time (e.g., before and after treatment) based on small RNA sequencing reads derived from a sample from the subject, measure the efficacy or tolerability of a therapeutic intervention for an individual based on small RNA sequencing reads derived from a sample from the subject, or process a biological sample as described herein.

[0117] Table 5 lists 10,122 oncRNAs that are expressed in DCIS cells and have a 90th percentile expression in non-DCIS cells of below 0.5 count per-million cells. The systemsand methods described herein can be used to determine whether a subject has DCIS based on the level of one or more oncRNAs from Table 5 detected in a sample from the subject.Table 5: oncRNA Sequences associated with DCIS

Claims

CLAIMSWHAT IS CLAIMED IS:

1. A method, comprising:(a) providing a sample from a subject, wherein said subject has or is suspected of having ductal carcinoma in situ (DCIS);(b) sequencing said sample, thereby generating a data set including data corresponding to a level of one or more non-coding RNA molecules that are expressed in DCIS cells and have a 90th percentile expression in non-cancerous cells of below 0.5 count per-million cells in said sample;(c) inputting said data into a trained algorithm to generate a classification of said sample as positive or negative for DCIS; and(d) electronically outputting said classification in a report.

2. The method of claim 1, wherein said trained algorithm has been trained with training samples comprising known positive-DCIS samples and known negative-DCIS samples.

3. The method of claim 1, further comprising generating an additional classification of said sample as positive or negative for invasive breast cancer.

4. The method of claim 3, wherein said data set in (b) further comprises additional data corresponding to a level of one or more non-coding RNA molecules that are expressed in invasive breast-cancer cells and have a 90thpercentile expression in non-cancerous cells of below 0.5 count per-million cells.

5. The method of claim 3, wherein said report further comprises the additional classification.

6. The method of claim 1, wherein said trained algorithm has been trained with training samples comprising: (i) non-cancerous samples, (ii) DCIS samples, and (iii) invasive breast cancer samples.

7. The method of claim 1, wherein (c) comprises generating: (i) a probability of said sample being positive for DCIS, (ii) a probability of said sample being positive for invasive breast cancer (IBC), and (iii) a probability of said sample being negative for cancer.

8. The method of claim 7, further comprising generating a detection score, wherein said detection score is equal to:

9. The method of claim 8, wherein if said detection score is greater than 0.3, said sample is classified as positive for breast cancer, wherein said breast cancer comprises DCIS or invasive breast cancer.

10. The method of claim 9, further comprising generating an invasiveness score, wherein said invasiveness score is equal to:

11. The method of claim 10, wherein if said invasiveness score is greater than 2.7, said sample is classified as positive for invasive breast cancer.

12. The method of claim 1, wherein said sample comprises a cell-free sample.

13. The method of claim 1, wherein said sample comprises plasma.

14. The method of claim 13, wherein said sample comprises less than 1 mL of plasma.

15. The method of claim 13, wherein said plasma comprises double-spun plasma.

16. The method of claim 1, further comprising monitoring a change over time in said classification by repeating (a)-(d) in another sample from said subject.

17. The method of claim 1, wherein said one or more non-coding RNA molecules that are expressed in DCIS cells and have a 90th percentile expression in non-cancerous cells of below 0.5 count per-million cells comprise nucleic acid sequences comprising one or a combination of SEQ ID NO: 1 through SEQ ID NO: 10122.

18. The method of claim 1, wherein said one or more non-coding RNA molecules that are expressed in DCIS cells and have a 90th percentile expression in non-cancerous cells of below 0.5 count per-million cells comprise nucleic acid sequences that comprise at least 70%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99% sequence homology to SEQ ID NO: 1 through SEQ ID NO: 10122.

19. The method of claim 1, wherein said sequencing in (b) comprises sequencing by synthesis.

20. A non-transitory computer-readable storage medium comprising a set of instructions recorded thereon, which, when executed by a processor, cause the processor to implement a method comprising:(a) sequencing a sample from a subject that has or is suspected of having ductal carcinoma in situ (DCIS), thereby generating a data set including data corresponding to a level of one or more noncoding RNA molecules that are expressed in DCIS cells and have a 90th percentile expression in non-cancerous cells of below 0.5 count per-million cells in said sample;(b) inputting said data into a trained algorithm to generate a classification of said sample as positive or negative for DCIS; and(c) electronically outputting said classification in a report.

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