WO2019147764A1 - Agents thérapeutiques guidés par un petit arn prédicteur - Google Patents

Agents thérapeutiques guidés par un petit arn prédicteur Download PDF

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WO2019147764A1
WO2019147764A1 PCT/US2019/014892 US2019014892W WO2019147764A1 WO 2019147764 A1 WO2019147764 A1 WO 2019147764A1 US 2019014892 W US2019014892 W US 2019014892W WO 2019147764 A1 WO2019147764 A1 WO 2019147764A1
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srna
samples
predictors
cohort
disease
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David Salzman
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Srnalytics, Inc.
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    • C—CHEMISTRY; METALLURGY
    • C07—ORGANIC CHEMISTRY
    • C07H—SUGARS; DERIVATIVES THEREOF; NUCLEOSIDES; NUCLEOTIDES; NUCLEIC ACIDS
    • C07H21/00—Compounds containing two or more mononucleotide units having separate phosphate or polyphosphate groups linked by saccharide radicals of nucleoside groups, e.g. nucleic acids
    • C07H21/04—Compounds containing two or more mononucleotide units having separate phosphate or polyphosphate groups linked by saccharide radicals of nucleoside groups, e.g. nucleic acids with deoxyribosyl as saccharide radical
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6809—Methods for determination or identification of nucleic acids involving differential detection
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/178—Oligonucleotides characterized by their use miRNA, siRNA or ncRNA

Definitions

  • miRNAs are small non-coding RNA molecules (about 22 nucleotides in length) found in plants and animals that function in RNA silencing and post-transcriptional regulation of gene expression. miRNAs are located within the cell, as well as in the circulation and extracellular environment, and can be detected in biological fluids.
  • miRNAs highly conserved in vertebrates show that each has roughly 400 conserved messenger RNA (mRNA) targets. Accordingly, a particular miRNA can reduce the stability of hundreds of unique mRNAs, and may repress the production of hundreds of proteins. This repression is often relatively mild, for example, usually less than 2-fold.
  • Human disease can be associated with deregulation or dysregulation of miRNAs as demonstrated for chronic lymphocytic leukemia and other B cell malignancies.
  • miR2Disease documents known relationships between miRNA levels (up- or down- regulated miRNAs) and human disease.
  • sRNAs small, non-coding RNAs
  • the invention provides a method for identifying or detecting small RNA (sRNA) predictors of a disease or a condition.
  • the method comprises identifying one or more sRNA sequences that are present in one or more samples of an experimental sample cohort, and which are not present in samples of a comparator cohort (“positive sRNA predictor”).
  • the method further comprises identifying one or more sRNA sequences that are present in one or more samples of a comparator sample cohort, and which are not present in samples of an experimental cohort (“negative sRNA predictor”).
  • the invention In contrast to identifying dysregulated small RNAs (such as microRNAs (miRNAs or miRs) that are up- or down-regulated), the invention identifies sRNAs that are binary predictors, that is, present in one cohort (e.g., an experimental cohort) and not another (e.g., a comparator cohort). Further, by quantifying reads for individual sequences (e.g., iso-miRs), without consolidating reads to annotated reference sequences, the invention unlocks the diagnostic utility of miRs and other sRNAs.
  • sRNAs that are binary predictors, that is, present in one cohort (e.g., an experimental cohort) and not another (e.g., a comparator cohort). Further, by quantifying reads for individual sequences (e.g., iso-miRs), without consolidating reads to annotated reference sequences, the invention unlocks the diagnostic utility of miRs and other sRNAs.
  • the one or more sRNA predictors, or a set of sRNA predictors is validated in an independent cohort of experimental and comparator samples, different from the experimental and comparator samples from whence they were discovered, to evaluate the ability of the sRNA predictors to discriminate experimental and comparator samples.
  • sRNA predictors are identified from sRNA sequencing data. Specifically, sRNA sequencing data is generated or provided for samples across an experimental cohort and a comparator cohort, for example, using any next- generation sequencing platform. sRNA predictors can be identified in sequence data from any type of biological sample, including solid tissues, biological fluids (e.g., cerebrospinal fluid and blood), or in some embodiments, cultured cells. The invention is applicable to various types of eukaryotic and prokaryotic cells and organisms, including animals, plants, and microbes.
  • sRNA predictors can be identified for various utilities in understanding the state of cells or organisms, including utilities in human and animal health, as well as agriculture.
  • the invention finds use in diagnostics, prognostics, drug discovery, toxicology, and therapeutics including personalized medicine.
  • the invention provides for diagnosis or stratification of a human or animal disease.
  • conditions that can define the experimental cohort include neurodegenerative diseases, cardiovascular diseases, inflammatory and/or immunological diseases, and cancers.
  • sRNA predictors can be identified for detecting a disease state, including early or asymptomatic stage disease (e.g., before noticeable or substantial symptoms appear) or distinguishing among diseases or conditions that manifest with similar symptoms.
  • Exemplary conditions include diagnosis (including early diagnosis) or stratification of neurodegenerative conditions such as Alzheimer’s Disease, Parkinson’s Disease, Huntington’s Disease, Amyotrophic Lateral Sclerosis, and Multiple Sclerosis.
  • the sRNA predictor(s) may be identified by a software program that quantifies the number of reads for each unique sRNA sequence in each sample in the experimental and comparator cohorts.
  • the software program trims the adaptor sequences from the individual sequences, so as to identify individual sRNAs, including miRs and iso-miRs. In this manner, iso-miRs with templated and non-templated variations at the 3’- and 5’- end are identified, among other sRNAs.
  • sequence reads from the experimental cohort and the comparator cohort can each be compiled into a dictionary, and compared, to identify sequences that are present in one cohort, but not the other.
  • Unique sequences and the amount (i.e. read count) of the unique reads for each sample or group of samples in the experimental cohort are annotated.
  • sRNA sequences are not aligned to a reference sequence, and thus, each sequence can be individually quantified across samples.
  • sRNA predictors are selected that have a read count of at least 5 or at least about 50 in the samples from the experimental cohort that are positive for the sRNA predictor. In still other embodiments, the sRNA predictors are present in at least about 7% of the experimental cohort samples, or are present in at least about 10% of comparator samples. In some embodiments, several sRNA predictors (such as four or more) are identified in the experimental cohort and/or the comparator cohort, and which may be selected for inclusion in an sRNA predictor panel. For example, binary predictors identified in the experimental cohort are positive predictors, while binary predictors identified in the comparator cohort are negative predictors.
  • a panel of sRNA predictors is selected for validation or detection of the condition in independent samples.
  • a panel of from 1 to about 200, or from 1 to about 100, or from 1 to about 50 sRNA, or from 1 to about 10 predictors can be selected, where the presence of one or more positive predictors (optionally with the absence of one or more negative predictors) is predictive of the condition that defines the experimental cohort.
  • the presence of 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 positive predictors from the panel, optionally with an absence of the entire panel of negative predictors is predictive of the condition.
  • the panel is large enough to provide nearly complete coverage for the condition in the experimental cohort or in independent samples (e.g., the population).
  • the presence of from 1 to about 100, or from 1 to about 50, or from 1 to about 20, or from 1 to about 10 sRNA positive predictors in a sample can be predictive of the condition that defines the experimental cohort.
  • Validation samples can be evaluated by sRNA sequencing, or alternatively by RT-PCR (including Real Time PCR or any quantitative or qualitative PCR format) or other sRNA detection assay.
  • detection of the sRNA predictors is migrated to one of various detection platforms, which can employ reverse-transcription and amplification, and/or hybridization of a detectable probe (e.g., fluorescent probe).
  • a detectable probe e.g., fluorescent probe.
  • An exemplary format is TAQMAN RealTime PCR Assay.
  • sRNA predictors in the panel, or their amplicons are detected by a hybridization assay.
  • the invention provides a kit comprising a panel of from 1 to about 200 or from 1 to about 100, or from 1 to 50 sRNA predictor assays, which may include one or both of positive and negative predictors.
  • Such assays may comprise amplification primers and/or probes specific for the detection of the sRNA predictors over annotated sequences, as well as over other (non-predictive) 5’- and/or 3’- templated and/or non-templated variations.
  • the kit is in the form of an array, and may contain probes specific for the detection of sRNA predictors by hybridization. The majority, or all, of the sRNA predictors are sRNAs in which any miRNA predictors contain a variation from a reference miRNA sequence.
  • the invention provides a method for determining a condition of a subject.
  • the method comprises obtaining a biological fluid sample, and identifying the presence or absence of one or more sRNA predictors identified in RNA sequence data according to the methods described herein, where the presence of one or more positive sRNA predictors in the sample, and optionally the absence of one or more negative predictors, is predictive or diagnostic for the condition.
  • the sRNA predictor(s) are identified in a sample from a human patient by a detection technology that involves amplification and/or probe hybridization, such as Real Time PCR (e.g., TAQMAN) assay.
  • the biological fluid sample from the patient can be blood, serum, plasma, urine, saliva, or cerebrospinal fluid.
  • the patient is suspected of having a neurodegenerative disease, a cardiovascular disease, an inflammatory and/or immunological disease, or a cancer.
  • the patient may be displaying one or more symptoms of the condition.
  • the patient is suspected of having a neurodegenerative disease selected from Amyotrophic Lateral Sclerosis (ALS), Parkinson’s Disease, Alzheimer’s Disease, Huntington’s Disease, or Multiple Sclerosis.
  • ALS Amyotrophic Lateral Sclerosis
  • Parkinson’s Disease Parkinson’s Disease
  • Alzheimer’s Disease Huntington’s Disease
  • Multiple Sclerosis Multiple Sclerosis
  • the sample is tested across a panel of sRNA detection assays, such as from 1 to about 100, or from about 4 to 100 sRNA detection assays, and in some embodiments the majority of the sRNAs detected in the patient sample (or all of the sRNAs detected in the patient sample) are not annotated reference miRNAs.
  • the panel may however include one or more miRNAs for detection as a control.
  • positive and/or negative predictors can be employed to classify a mixed population of cells in vivo or ex vivo, through targeted expression of a gene with a detectable or biological impact.
  • a desired protein can be expressed from a gene construct (such as a plasmid) or expressed from mRNA delivered to cells in vivo or ex vivo.
  • the gene is delivered under the regulatory control of target site(s) specific for the one or more small RNA predictors.
  • the target site(s) can be placed in non-coding segments, such as the 3’ and/or 5’ UTRs, such that the encoded protein is only expressed in biologically significant amounts when the desired predictor(s) are absent in the cell.
  • the protein encoded by the construct may be a reporter protein, a transcriptional activator, a transcriptional repressor, a pro-apoptotic protein, a pro-survival protein, a lytic protein, an enzyme, a cytokine, a toxin, or a cell- surface receptor.
  • the predictors can be used to target expression of a desired protein for therapeutic impact, either to target diseased cells for killing, or to protect non-diseased cells from toxic insult.
  • FIGURES 1A and 1B illustrates the standard method for analyzing small RNA sequencing data, from embodiments of the present invention.
  • the object of standard processes is to identify dysregulated sRNAs (up- or down-regulated) for validation in larger cohorts using targeted assays such as quantitative PCR (e.g., TAQMAN).
  • targeted assays such as quantitative PCR (e.g., TAQMAN).
  • adapter sequences are trimmed, reads are aligned to a reference, and read numbers are quantified for each reference sRNA.
  • Diagnostic sRNAs are selected based on the level of differential expression between samples and/or groups of samples.
  • FIGURE 1 A is an illustrative example showing mapped small RNA sequence reads (in this case a miRNA, miR-X) aligned to a reference.
  • miR-X is present in both a Disease and Control sample, and is not a homogenous sequence, but rather a heterogeneous series of iso-miRs that all map to the same region. Lines representing sequence reads are shaded to depict various iso-miR sequences. The light grey box highlights the annotated miR-X reference sequence.
  • FIGURE 1B is an illustrative example of how the mapped sequencing data for miR-X from FIGURE 1A is condensed and quantified, which is the sum of all of the iso-miRs for miR-X. In this particular example, miR-X would be considered to have diagnostic value/potential, since there is a 2-fold difference in expression when comparing the Disease and Control sample.
  • FIGURE 2 illustrates sequencing data for the human miRNA, miR-lOb derived from a frontal cortex (region BA9) tissue sample taken from a patient with Huntington’s Disease (SRR1759249) or non-diseased, Healthy Control (SRR1759213).
  • the reference is shown with the annotated miR-lOb sequence highlighted. The number of reads for each sequence is shown. In this particular example, there are 8 miR-lOb iso-miRs in addition to the annotated miR-lOb sequence found in these samples.
  • the total read count for the Huntington’s Disease and Healthy Control samples are 1670 and 336, respectively. Thus, there is 5-fold greater amount of ‘total’ miR-lOb in the Huntington’s Disease sample when compared to the Healthy Control.
  • FIGURES 3A and 3B illustrate how miRNA sequencing data is sorted and quantified across samples according to embodiments of the present invention.
  • FIGURE 3A illustrates the approach according to the present disclosure, where iso-miRs (or other sRNAs) are sorted by their individual iso-miR sequences, and therefore do not require alignment to a reference. Lines representing sequence reads are shaded to depict identical iso-miR sequences.
  • FIGURE 3B shows how sequence reads for iso-miRs (or other sRNAs) are quantified based on their unique sequence, not by alignment to a reference.
  • FIGURE 4 illustrates the analytic method described herein for identifying positive and negative predictors in small RNA sequencing data.
  • miR-X there are 2 binary, positive predictors for in the Disease sample and 1 binary, negative predictor in the Control sample. These positive and negative predictors can be used in a diagnostic panel to test for the condition in which they have been identified.
  • Figure 4 illustrates that the miR-X annotated sequence is present in equal amounts in both the Disease and Control sample, and is therefore non-diagnostic.
  • Figure 4 illustrates that a miR-X iso-miR is present in both the Disease and Control sample with a 2.5-fold difference, however since this iso-miR is not binary, it is not included in a diagnostic panel.
  • FIGURE 5 illustrates that quantitative PCR assays (e.g., based on TAQMAN format) can be designed that give >99.9% specificity for iso-miRs or other sRNAs of interest.
  • hairpin-RT TAQMAN qPCR assays were designed for the indicated annotated miR, iso-miR 1 (that has an additional 3’-terminal uridine) or iso-miR 2 (that has an additional 3’-terminal guanidine).
  • Synthetic RNA as indicated was reverse transcribed using a targeted hairpin-RT primer.
  • cDNA was amplified by qPCR in the presence of a TAQMAN probe specific to each RNA sequence. Shown is the percent relative detection, for a TAQMAN assay to detect each synthetic RNA.
  • FIGURE 6 is a heat map in which the top 335 highest frequency small RNAs found in Huntington’s Disease (top), healthy controls (bottom), and both Huntington’s Disease and healthy controls (middle) were clustered using Ward’s agglomerative clustering with incomplete linkage.
  • FIGURE 7 shows experimental validation of eight positive small RNA predictors identified in Huntington’s Disease samples, using Reverse transcription (RT) hairpin-based TAQMAN quantitative polymerase chain reaction (qPCR) assays (ThermoFisher Scientific). Clinical information (disease vs non-disease, and disease grade) was unmasked and the samples were decoded and Ct values were plotted for healthy controls and Huntington’s Disease.
  • RT Reverse transcription
  • qPCR quantitative polymerase chain reaction
  • FIGURE 8 shows an analysis of eight biomarkers for a correlation of Ct to disease grade using Box- Whisker plots.
  • FIGURE 9 is a heat map in which the top 335 highest frequency small RNAs found in Parkinson’s Disease (top), healthy controls (bottom), and both Parkinson’s Disease and healthy controls (middle) were clustered using Ward’s agglomerative clustering with incomplete linkage. Analysis of tissue from frontal cortex (region BA9), CSF (cerebrospinal fluid), and Serum is shown.
  • FIGURE 10 illustrates tissue-specific biomarker overlap for Parkinson’s disease predictors.
  • TIS indicates tissue
  • CSF indicates cerebrospinal fluid
  • SER indicates serum
  • FIGURE 11 is a heat map in which the top 335 highest frequency small RNAs found in Alzheimer’s Disease (top), healthy controls (bottom), and both Alzheimer’s Disease and healthy controls (middle) were clustered using Ward’s agglomerative clustering with incomplete linkage. Analysis of CSF, Serum, and Whole Blood (WB) is shown.
  • FIGURE 12 illustrates tissue-specific biomarker overlap for Alzheimer’s
  • TIS tissue
  • CSF cerebrospinal fluid
  • SER serum
  • WB whole blood
  • FIGURE 13 is a heat map in which the top 335 highest frequency small RNAs found in breast cancer tissue (top), healthy controls (bottom), and both breast cancer and healthy controls (middle) were clustered using Ward’s agglomerative clustering with incomplete linkage.
  • FIGURE 14 illustrates a potential application of negative predictors in a therapeutic context.
  • FIGURE 15 illustrates a potential application of positive predictors in a therapeutic context.
  • the invention provides a method for identifying or detecting binary small RNA (sRNA) predictors of a disease or a condition.
  • the method comprises identifying one or more sRNA sequences that are present in one or more samples of an experimental cohort, and which are not present in any of the samples in a comparator cohort (“positive sRNA predictors”).
  • the method further comprises identifying one or more sRNA sequences that are present in one or more samples of the comparator cohort, and which are not present in any of the samples of the experimental cohort (“negative sRNA predictors”).
  • the invention In contrast to identifying dysregulated sRNAs (such as miRNAs that are up- or down- regulated), the invention identifies sRNAs that are binary predictors, that is, sRNAs that are only present in one cohort (e.g., an experimental cohort) and not another (e.g., a comparator cohort). Further, by quantifying reads for individual sequences (e.g., iso- miRs), without consolidating reads to annotated reference sequences, the invention unlocks the diagnostic utility of miRs and other sRNAs.
  • sRNAs that are binary predictors, that is, sRNAs that are only present in one cohort (e.g., an experimental cohort) and not another (e.g., a comparator cohort). Further, by quantifying reads for individual sequences (e.g., iso- miRs), without consolidating reads to annotated reference sequences, the invention unlocks the diagnostic utility of miRs and other sRNAs.
  • the presence of the one or more sRNA predictors is tested in an independent cohort of experimental and comparator samples, to evaluate the ability of the sRNA predictors to discriminate samples, thereby validating the diagnostic, prognostic, or other utility of the sRNA predictors.
  • Diagnostic kits that detect one or a panel of sRNA predictors (positive and/or negative predictors) in a sample can be prepared in any desired detection format, including quantitative or qualitative PCR or hybridization-based assays, as described more fully herein.
  • sRNA sequencing data is generated or provided from a sample or group of samples across an experimental cohort and comparator cohort, and sRNA predictors are identified in the RNA sequencing data according to the following disclosure.
  • sRNA sequencing enriches and sequences small RNA species, such as microRNA (miRNA), Piwi-interacting RNA (piRNA), small interfering RNA (siRNA), vault RNA (vtRNA), small nucleolar RNA (snoRNA), transfer RNA-derived small RNAs (tsRNA), ribosomal RNA-derived small RNA fragments (rsRNA), small rRNA- derived RNA (srRNA), and small nuclear RNA (U-RNA).
  • miRNA microRNA
  • piRNA Piwi-interacting RNA
  • siRNA small interfering RNA
  • vault RNA vault RNA
  • snoRNA small nucleolar RNA
  • tsRNA transfer RNA-derived small RNAs
  • rsRNA ribosomal RNA-
  • input material may be enriched for small RNAs.
  • Sequence library construction is performed with sRNA-enriched material using any of several processes or commercially-available kits depending on the high-throughput sequencing platform being employed.
  • sRNA sequencing library preparation comprises isolating total RNA from samples, size fractionation, ligation of sequencing adaptors, reverse transcription and PCR amplification, and DNA sequencing.
  • RNA i.e. total RNA
  • the small RNAs are isolated by size fractionation, for example, by running the isolated RNA on a denaturing polyacrylamide gel (or using any of a variety of commercially available kits).
  • a ligation step then adds adaptors to both ends of the small RNAs, which act as primer binding sites during reverse transcription and PCR amplification.
  • a preadenylated single strand DNA 3’-adaptor followed by a 5’-adaptor are ligated to the small RNAs using a ligating enzyme such as T4 RNA Ligase 2 Truncated (T4 Rnl2tr K227Q).
  • the adaptors are designed to capture small RNAs with a 5’-phosphate and 3’-hydroxyl group, characteristic of biologically processed small RNAs (e.g., microRNAs), rather than RNA degradation products with a 5’ hydroxyl and 3’ phosphate group.
  • the sRNA library is then reverse transcribed and amplified by PCR. This step converts the small adaptor ligated RNAs into cDNA clones that are the template for the sequencing reaction. Primers designed with unique nucleotide tags can also be used in this step to create ID tags (i.e., bar codes) in pooled library multiplex sequencing.
  • Any DNA sequencing platform can be employed, including any next-generation sequencing platform such as pyrosequencing (e.g., 454 Life Sciences), polymerase- based sequence-by-synthesis (e.g., Illumina), or sequencing-by-ligation (e.g., ABI Solid Sequencing platform), among others.
  • next-generation sequencing platform such as pyrosequencing (e.g., 454 Life Sciences), polymerase- based sequence-by-synthesis (e.g., Illumina), or sequencing-by-ligation (e.g., ABI Solid Sequencing platform), among others.
  • sequencing data can be generated and/or provided from historical studies, and evaluated for sRNA predictors according to the following disclosure.
  • the sequencing data can be in any format, such as FASTA or FASTQ format.
  • FASTA format is a text-based format for representing nucleotide sequences, where nucleotides are represented using single-letter codes. The format also allows for sequence names and comments to precede the sequences.
  • FASTQ format includes corresponding quality scores. Both the sequence letter and quality score are each encoded with a single ASCII character for brevity.
  • sRNA predictors can be identified in any biological samples, including solid tissues and/or biological fluids.
  • sRNA predictors can be identified in prokaryotic or eukaryotic organisms, including animals (e.g., vertebrates and invertebrates), plants, microbes (e.g., bacteria and yeast), or in some embodiments, cultured cells derived from these sources.
  • the experimental and comparator samples are biological fluid samples from human or animal subjects (e.g., a mammalian subject), such as blood, serum, plasma, urine, saliva, or cerebrospinal fluid.
  • miRNAs can be found in biological fluid, as a result of a secretory mechanism that may play an important role in cell-to-cell signaling.
  • samples in the experimental cohort and the comparator cohort can be biological fluid samples, such as blood, serum, plasma, urine, saliva, or cerebrospinal fluid.
  • sRNA predictors are identified in at least two different types of fluid samples. For example, with regard to detection of neurodegenerative disease, sRNA predictors can be identified in both blood (or serum) and cerebrospinal fluid.
  • An experimental cohort is a collection of samples that have a defined condition.
  • the experimental cohort can be a collection of samples from human or animal subjects or patients.
  • Conditions include, in some embodiments, neurodegenerative diseases, cardiovascular diseases, inflammatory and/or immunological diseases, and cancers, including particular conditions described more fully below.
  • Experimental cohorts can be further defined based on late-stage or early-stage disease, or course of disease progression, treatment received, and patient response to treatment.
  • An experimental cohort generally comprises a plurality of samples, but in various embodiments, includes at least 1 sample, or at least about 5 samples, or at least about 10 samples, or at least about 15 samples, or at least about 20 samples, or at least about 25 samples, or at least about 50 samples, or at least about 75 samples, or at least about 100 samples, or at least about 150 samples, or at least about 200 samples, or at least about 250 samples. Larger experimental cohorts (e.g., at least 100 samples) are preferred in some embodiments.
  • a comparator cohort is a collection of samples that do not have the condition that defines the experimental cohort.
  • the comparator cohort can include samples from subjects or patients identified as healthy comparators, or otherwise having a different condition or disease, including conditions or diseases with similar, but different symptoms to the disease or condition of interest (e.g., similar symptoms to the disease or condition that defines the experimental cohort samples).
  • a comparator cohort generally comprises a plurality of samples, but in various embodiments, includes at least 1 sample, or at least about 5 samples, or at least about 10 samples, or at least about 15 samples, or at least about 20 samples, or at least about 25 samples, or at least about 50 samples, or at least about 75 samples, or at least about 100 samples, or at least about 150 samples, or at least about 200 samples, or at least about 250 samples.
  • comparator cohorts are preferred in some embodiments (e.g., at least 100 samples), however the comparator cohort may be similar in size to or smaller than the experimental cohort.
  • the comparator cohort is similar to the experimental cohort in patient make-up, in terms of, for example, age, gender, and/or ethnicity.
  • sRNA predictors can be identified for various utilities in understanding the state of cells or organisms, including utilities in human and animal health, as well as agriculture.
  • the invention finds use in diagnostics, prognostics, drug discovery, toxicology, and therapeutics including personalized medicine.
  • the invention provides for diagnosis or stratification of a human or animal disease.
  • sRNA predictors can be identified for detecting a disease state, including early stage or asymptomatic disease (e.g., before noticeable or substantial symptoms) or distinguishing diseases or conditions that manifest with similar symptoms.
  • sRNA predictors are identified that distinguish disease courses, such as slowly and quickly progressing disease states, or disease subtypes (e.g., relapsing remitting MS, secondary progressive MS, primary progressive MS, or progressive relapsing MS), or stratify for disease severity.
  • experimental and comparator cohorts are designed to distinguish two or more disease states, based upon classification of each patient’s disease across the two or more states.
  • sRNA predictors identify patients for response to one or more available therapeutic regimens.
  • experimental and comparator cohorts are designed to distinguish responses to treatment (e.g., by classifying patient samples based upon treatment received by each patient and/or the response achieved).
  • sRNA predictors are identified that distinguish a toxic response to an environmental or pharmaceutical agent.
  • the presence and/or absence of sRNA predictors are applied as surrogate endpoints to establish safety and/or efficacy of a candidate agent, or for treatment monitoring, by evaluating the presence and/or absence of the sRNA predictors in patient samples during clinical trials or during treatment.
  • positive predictors may be found before treatment with a candidate agent, and may decrease or be eliminated with successful drug treatment.
  • negative predictors may be absent before treatment, but may emerge during successive treatment.
  • various types of diseases and conditions can be evaluated in accordance with various embodiments, including neurodegenerative disease, cardiovascular disease, inflammatory and/or immunological disease, and cancer.
  • Neurodegenerative disease is an umbrella term for the progressive loss of structure or function of neurons, including death of neurons.
  • exemplary neurodegenerative diseases include Alzheimer’s Disease, Amyotrophic Lateral Sclerosis (ALS), Huntington’s Disease, Multiple Sclerosis, Parkinson’s Disease, and various types of dementia (e.g., Frontotemporal Dementia, Lewy Body Dementia, or Vascular Dementia).
  • Neurodegenerative conditions generally result in progressive degeneration and/or death of neuronal cells.
  • the neurodegenerative disease results in dementia in at least a substantial portion of patients.
  • the neurodegenerative disease results in a motion disorder in at least a substantial portion of patients. While conditions can be late on-set, in some embodiments, the disease can manifest as early on-set (e.g., before about 50 years of age).
  • sRNA predictors are identified in a cohort of Alzheimer’s Disease (AD) samples.
  • AD is characterized by loss of neurons and synapses in the cerebral cortex and certain subcortical regions. This loss results in gross atrophy of the affected regions, including degeneration in the temporal lobe and parietal lobe, and parts of the frontal cortex and cingulate gyrus.
  • Alzheimer's Disease has been hypothesized to be a protein misfolding disease, caused by accumulation of abnormally folded Amyloid-beta and Tau proteins in the brain.
  • the experimental cohort samples are biological fluid samples from patients diagnosed as having AD. Comparator cohort samples can be patients identified as not having AD, and may optionally include patients with other (non-AD) neurodegenerative or inflammatory disease.
  • sRNA predictors are identified in a cohort of Parkinson’s Disease (PD) samples.
  • PD manifests as bradykinesia, rigidity, resting tremor and posture instability.
  • PD is a degenerative disorder of the central nervous system that involves the death of dopamine-generating cells in the substantia nigra, a region of the midbrain. The mechanism by which the brain cells in PD are lost may involve an abnormal accumulation of the protein alpha-synuclein bound to ubiquitin in the damaged cells. The alpha-synuclein-ubiquitin complex cannot be directed to the proteosome. This protein accumulation forms proteinaceous cytoplasmic inclusions called Lewy bodies.
  • the experimental cohort samples are biological fluid samples from patients diagnosed as having PD. Comparator cohort samples can be patients identified as not having PD, and may optionally include patients with other (non-PD) neurodegenerative or inflammatory disease.
  • sRNA predictors are identified in a cohort of Huntington’s Disease (HD) samples.
  • HD causes astrogliosis and loss of medium spiny neurons. Areas of the brain are affected according to their structure and the types of neurons they contain, reducing in size as they cumulatively lose cells. The areas affected are mainly in the striatum, but also the frontal and temporal cortices. Mutant Huntington is an aggregate-prone protein.
  • the experimental cohort samples are biological fluid samples from patients diagnosed as having HD. Comparator cohort samples can be patients identified as not having HD, and may optionally include patients with other (non-HD) neurodegenerative or inflammatory disease.
  • sRNA predictors are identified in a cohort of Amyotrophic Lateral Sclerosis (ALS) samples.
  • ALS is a disease in which motor neurons are selectively targeted for degeneration.
  • Some patients with familial ALS have a missense mutation in the gene encoding the antioxidant enzyme Cu/Zn superoxide dismutase 1 (SOD1).
  • SOD1 Cu/Zn superoxide dismutase 1
  • TDP-43 and FUS protein aggregates have been implicated in some cases of the disease, and a mutation in chromosome 9 (C9orf72) is thought to be the most common known cause of sporadic ALS.
  • the experimental cohort samples are biological fluid samples from patients diagnosed as having ALS.
  • Comparator cohort samples can be patients identified as not having ALS, and may optionally include patients with other (non-ALS) neurodegenerative disease.
  • sRNA predictors are identified in a cohort of samples from migraine subjects, such as biological fluid samples from migraine subjects.
  • the migraine is episodic migraine, chronic migraine, or cluster headache.
  • sRNA predictors in these embodiments are useful for evaluating the subject’s condition, or alternatively or in addition, selecting an appropriate treatment.
  • Comparator cohort samples can be subjects identified as not having migraine, and may optionally include patients with other non-migraine conditions, or a different form of migraine from the experimental cohort.
  • Cardiovascular disease is a class of diseases that involve the heart or blood vessels.
  • Cardiovascular disease includes coronary artery diseases (CAD) such as angina and myocardial infarction.
  • CAD coronary artery diseases
  • Other CVDs are stroke, heart failure, hypertensive heart disease, rheumatic heart disease, cardiomyopathy, heart arrhythmia, congenital heart disease, valvular heart disease, carditis, aortic aneurysms, peripheral artery disease, and venous thrombosis.
  • the underlying mechanisms of coronary artery disease, stroke, and peripheral artery disease involve atherosclerosis, which may be caused by high blood pressure, smoking, diabetes, lack of exercise, obesity, high blood cholesterol, poor diet, and excessive alcohol consumption, among other things.
  • the experimental cohort comprises samples from patients having coronary artery disease, peripheral artery disease, cerebrovascular disease, cardiomyopathy, hypertensive heart disease, heart failure (e.g., congestive heart failure), pulmonary heart disease, cardiac dysrhythmia, inflammatory heart disease, endocarditis, myocarditis, inflammatory cardiomegaly, valvular heart disease, congenital heart disease, or rheumatic heart disease.
  • the comparator cohort can comprise samples from patients that do not have the CVD, or a distinct CVD from the experimental cohort.
  • sRNA predictors are identified to stratify patients for risk of an acute event related to CVD, such as myocardial infarction or stroke.
  • an acute event related to CVD such as myocardial infarction or stroke.
  • Existing cardiovascular disease or a previous cardiovascular event such as a heart attack or stroke, is the strongest predictor of a future cardiovascular event.
  • Age, sex, smoking, blood pressure, blood lipids and diabetes are important predictors of future cardiovascular disease in people who are not known to have cardiovascular disease. These measures, and sometimes others, may be combined into composite risk scores to estimate an individual's future risk of cardiovascular disease. Numerous risk scores exist although their respective merits are debated.
  • NT- proBNP N-terminal pro B-type natriuretic peptide
  • the experimental cohort comprises patients at a high risk of myocardial infarction or stroke (e.g., top 25% or top 20% or top 10% of risk scores), and the comparator cohort comprises patients with relatively low risk scores for the same (e.g., bottom quartile or less).
  • the sRNA predictor identifies or evaluates an immunological or inflammatory disease.
  • the condition is an autoimmune or inflammatory disorder, such as Lupus (SLE), Scleroderma, Vasculitis, Diabetes mellitus (e.g., Type 1 or Type 2), Graves’ disease, Rheumatoid arthritis, Multiple Sclerosis, Fibromyalgia, Psoriasis, Crohn’s Disease, Celiac Disease, COPD, or a fibrotic condition such as pulmonary fibrosis (e.g., IPF).
  • the condition is an inflammatory condition, which may manifest as type I hypersensitivity, type II hypersensitivity, type III hypersensitivity, and/or type IV hypersensitivity.
  • the inflammatory condition may be chronic.
  • the experimental cohort samples are biological fluid samples from patients diagnosed as having a particular inflammatory disease.
  • Comparator cohort samples can be patients identified as not having the particular inflammatory disease, and may optionally include patients with other inflammatory disease.
  • the comparator cohort comprises patients with a positive or negative (or even toxic) response to a particular treatment regimen.
  • the sRNA predictor is predictive of the presence of cancer, or the presence of an aggressive cancer, or is predictive of remission or recurrence, metastasis, progression free interval, overall survival, or response to treatment (e.g., radiation therapy, chemotherapy, or treatment with a checkpoint inhibitor selected from anti-CTLA4, PD-l, PD-L1, IDO, or CAR T-cell therapy).
  • treatment e.g., radiation therapy, chemotherapy, or treatment with a checkpoint inhibitor selected from anti-CTLA4, PD-l, PD-L1, IDO, or CAR T-cell therapy.
  • the sRNA predictor is predictive of high toxicity upon treatment with a particular agent.
  • the sRNA predictors are predictive of a complete response of a particular cancer to a particular treatment.
  • the cancer may be Carcinoma, Sarcoma, Lymphoma, Germ cell, or Blastoma.
  • the cancer can occur in sites including, but not limited to lung, skin, breast, ovary, intestine, pancreas, bone, and brain, among others.
  • the cancer is stage I or stage II cancer.
  • the cancer is stage III or stage IV.
  • Illustrative cancers include, but are not limited to, basal cell carcinoma, biliary tract cancer; bladder cancer; bone cancer; brain and central nervous system cancer; breast cancer; cancer of the peritoneum; cervical cancer; choriocarcinoma; colon and rectum cancer; connective tissue cancer; cancer of the digestive system; endometrial cancer; esophageal cancer; eye cancer; cancer of the head and neck; gastric cancer (including gastrointestinal cancer); glioblastoma; hepatic carcinoma; hepatoma; intra epithelial neoplasm; kidney or renal cancer; larynx cancer; leukemia; liver cancer; lung cancer (e.g., small-cell lung cancer, non-small cell lung cancer, adenocarcinoma of the lung, and squamous carcinoma of the lung); melanoma; myeloma; neuroblastoma; oral cavity cancer (lip, tongue, mouth, and pharynx); ovarian cancer; pancreatic cancer; prostate cancer
  • the experimental cohort samples are biological fluid samples from patient diagnosed as having a particular defined cancer.
  • Comparator cohort samples can be patients identified as not having the cancer, and may optionally include patients with other non- cancerous disease or condition.
  • the sRNA predictor may be identified by a software program that quantifies the number of reads for each unique sRNA sequence in each sample in the experimental and comparator sample cohorts.
  • the software program trims the adaptor sequences from the individual sequences, so as to identify individual sRNAs, including miRs and iso-miRs and other sRNAs. In this manner, iso-miRs with templated and non-templated variations at the 3’- and 5’- end are identified.
  • iso-miR refers to those sequences that have variations with respect to the reference miRNA sequence (e.g., as used by miRBase).
  • miRBase each miRNA is associated with a miRNA precursor and with one or two mature miRNA (-5p and -3p).
  • Deep sequencing has detected a large amount of variability in miRNA biogenesis, meaning that from the same miRNA precursor many different sequences can be generated.
  • iso-miRs There are four main variations of iso-miRs: (1) 5' trimming, where the 5' cleavage site is upstream or downstream from the referenced miRNA sequence; (2) 3' trimming, where the 3' cleavage site is upstream or downstream from the reference miRNA sequence; (3) 3' nucleotide addition, where nucleotides are added to the 3' end of the reference miRNA; and (4) nucleotide substitution, where nucleotides are changed from the miRNA precursor.
  • the software program in some embodiments trims a user-defined 3’ sequencing adaptor from the sRNA sequence reads.
  • the adaptor is defined by the user, based on the sequencing platform. By removing the adaptor sequence, iso-miRs and other sRNAs can be identified and quantified in samples.
  • the software program searches for regular expressions corresponding to a user-defined 3’ adaptor and deletes them from the sRNA sequence reads as follows: a. adaptor sequence b. adaptor sequence permitting 1 wild-card c. adaptor sequence permitting 1 insertion d. adaptor sequence permitting 1 deletion e. adaptor sequence permitting 2 deletions f. adaptor sequence permitting 1 deletion and 1 wild-card g. adaptor sequence permitting 1 insertion and 1 wild-card h. adaptor sequence permitting 2 wild-cards i. adaptor sequence permitting 3 wild-cards j. adaptor sequence permitting 4 wild cards.
  • a wild-card is defined as being any one of the 4 deoxyribonucleic acids: (A) adenine, (T) thymine, (G) guanine, or (C) cytosine.
  • the first nucleotide at the 5’ end of the user-specified 3’ adaptor sequence is not altered (e.g., not considered an insertion or deletion or otherwise subject to wild-card change), thus preserving sRNA sequences at the junction where the 3’ terminal nucleotide of the sRNA is ligated to the 5’ terminal nucleotide of the 3’ adapter. If the 5’ terminal nucleotide of the user- specified 3’ adaptor does not correspond with what the user has specified, the 3’ adapter sequence is not trimmed, but can be independently verified, if needed.
  • sRNA having a length of at least 15 nucleotides, or at least 20 nucleotides (after trimming), are considered for analysis.
  • sequence reads from the experimental cohort and the comparator cohort can be each compiled into a dictionary, and compared, to identify sequences that are present in samples of the experimental cohort, but not the comparator cohort (e.g. positive predictors), and/or to identify sequences that are present in the comparator cohort, but not the experimental cohort (e.g. negative predictors).
  • Sequence reads that are in both cohorts are discarded, and sequence reads that are unique to either the experimental cohort or comparator cohort are added to an output file, the unique reads being candidate sRNA predictors.
  • the output file annotates the unique sequences and the count of the unique sequence reads for each sample or group of samples in the cohorts.
  • the sequence reads are not filtered by a quality score. Further, sRNA sequences are not aligned to a reference sequence, and thus, each sequence can be individually quantified across samples.
  • sRNA predictors are selected that have a count of (or an average count of) at least 5, at least 10, at least 20, at least 50, at least 75, at least 100, at least 200, at least 500, or at least 1000 reads in samples that are positive for the predictor (e.g., in the experimental cohort for positive predictors or the comparator cohort for negative predictors).
  • one or more (or all) positive sRNA predictors are present in at least about 5%, or at least about 10% of the experimental cohort samples, or at least about 15% of experimental cohort samples, or at least about 20% of experimental cohort samples, or at least about 30% of experimental cohort samples, or at least about 40% or at least about 50% of experimental cohort samples.
  • At least 1, or at least about 5, or at least about 10, or at least about 20, or at least about 30, or at least about 40, or at least about 50, or at least about 100 positive sRNA predictors are identified in the experimental cohort, and a plurality of which (e.g., from 1 to 100 or from 1 to 50, or from 1 to 10) may be selected for inclusion in an sRNA predictor panel. In some embodiments, from 4 to 100, or from 10 to 100, or from 20 to 100 positive sRNA predictors are selected for inclusion in a panel.
  • the negative sRNA predictors are present in at least about 5% of the comparator cohort samples, or at least 10% of the comparator cohort samples, or at least about 15% of the comparator cohort samples, or at least about 20% of comparator cohort samples, or at least about 30% of comparator cohort samples, or at least about 40% or at least about 50% of comparator cohort samples.
  • At least 1, or at least about 5, or at least about 10, or at least about 20, or at least about 30, or at least about 40, or at least about 50, or at least about 100 negative sRNA predictors are identified in the comparator cohort, and a plurality of which (e.g., from 1 to 100, or from 1 to 50, or from 1 to 10) may be selected for inclusion in an sRNA predictor panel. In some embodiments, from 4 to 100, or from 10 to 100, or from 20 to 100 negative sRNA predictors are selected for inclusion in a panel.
  • a panel of sRNA predictors is selected for validation or detection of the condition in independent samples. For example, a panel of from 2 to about 100 sRNA predictors can be selected, where the presence of any one positive predictor, and the absence of all of the negative predictors is predictive of the condition that defines the experimental cohort. In some embodiments, the presence of any 2, 3, 4, 5, 6, 7, 8, 9 or 10 positive sRNA predictors is predictive of the condition, optionally with the absence of the negative predictors. In some embodiments, a panel of from 2 to about 40 sRNA predictors are selected, or from 2 to about 30, or from 2 to about 20, or from 2 to about 10 sRNA predictors are selected for inclusion in a panel.
  • the panel may optionally comprise at least 5, or at least 10, or at least 20 sRNA predictors. While not each experimental sample will be positive for each positive predictor, the panel is large enough to provide at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, or about 100% coverage for the condition in the experimental cohort or in independent samples. That is, the presence of from 1 to 10 positive sRNA predictors (e.g., any one or two) in a sample may be predictive of the condition that defines the experimental cohort. The sample may further be negative for the panel of negative predictors (e.g., from 1 to 10 or from 1 to 5 negative predictors). Validation samples can be evaluated by sRNA sequencing, or alternatively by RT-PCR or other assay.
  • detection of the sRNA predictors is migrated to one of various detection platforms (e.g., other than RNA sequencing), which can employ reverse-transcription, amplification, and/or hybridization of a probe, including quantitative or qualitative PCR, or RealTime PCR.
  • PCR detection formats can employ stem-loop primers for RT-PCR in some embodiments, and optionally in connection with fluorescently-labeled probes.
  • a real-time polymerase chain reaction monitors the amplification of a targeted DNA molecule during the PCR, i.e. in real-time.
  • Real-time PCR can be used quantitatively, and semi-quantitatively.
  • Two common methods for the detection of PCR products in real-time PCR are: (1) non-specific fluorescent dyes that intercalate with any double-stranded DNA (e.g., SYBR Green (I or II)), and (2) sequence-specific DNA probes consisting of oligonucleotides that are labelled with a fluorescent reporter which permits detection only after hybridization of the probe with its complementary sequence (e.g. TAQMAN).
  • the assay format is TAQMAN real-time PCR.
  • TAQMAN probes are hydrolysis probes that are designed to increase the specificity of quantitative PCR.
  • the TAQMAN probe principle relies on the 5' to 3' exonuclease activity of Taq polymerase to cleave a dual-labeled probe during hybridization to the complementary target sequence, with fluorophore-based detection.
  • TAQMAN probes are dual labeled with a fluorophore and a quencher, and when the fluorophore is cleaved from the oligonucleotide probe by the Taq exonuclease activity, the fluorophore signal is detected (e.g., the signal is no longer quenched by the proximity of the labels). As in other quantitative PCR methods, the resulting fluorescence signal permits quantitative measurements of the accumulation of the product during the exponential stages of the PCR.
  • the TAQMAN probe format provides high sensitivity and specificity of the detection.
  • sRNA predictors present in the sample are converted to cDNA using specific primers, e.g., a stem-loop primer. Amplification of the cDNA may then be quantified in real time, for example, by detecting the signal from a fluorescent reporting molecule, where the signal intensity correlates with the level of DNA at each amplification cycle.
  • sRNA predictors in the panel, or their amplicons are detected by hybridization.
  • exemplary platforms include surface plasmon resonance (SPR) and microarray technology.
  • Detection platforms can use microfluidics in some embodiments, for convenient sample processing and sRNA detection.
  • any method for determining the presence of sRNAs in samples can be employed. Such methods further include nucleic acid sequence based amplification (NASBA), flap endonuclease-based assays, as well as direct RNA capture with branched DNA (QuantiGeneTM), Hybrid CaptureTM (Digene), or nCounterTM miRNA detection (nanostring).
  • the assay format in addition to determining the presence of miRNAs and other sRNAs may also provide for the control of, inter alia, intrinsic signal intensity variation.
  • Such controls may include, for example, controls for background signal intensity and/or sample processing, and/or hybridization efficiency, as well as other desirable controls for detecting sRNAs in patient samples (e.g., collectively referred to as“normalization controls”).
  • the assay format is a flap endonuclease-based format, such as the InvaderTM assay (Third Wave Technologies).
  • an invader probe containing a sequence specific to the region 3' to a target site, and a primary probe containing a sequence specific to the region 5' to the target site of a template and an unrelated flap sequence are prepared. Cleavase is then allowed to act in the presence of these probes, the target molecule, as well as a FRET probe containing a sequence complementary to the flap sequence and an auto complementary sequence that is labeled with both a fluorescent dye and a quencher.
  • RNA is extracted from the sample prior to sRNA processing for detection. RNA may be purified using a variety of standard procedures as described, for example, in RNA Methodologies, A laboratory guide for isolation and characterization. 2nd edition, 1998, Robert E. Farrell, Jr., Ed., Academic Press.
  • RNAs may be isolated by organic extraction followed by purification on a glass fiber filter.
  • Alternative methods for isolating miRNAs include hybridization to magnetic beads.
  • miRNA processing for detection e.g., cDNA synthesis
  • assays can be constructed such that each assay is at least 80%, or at least 85%, or at least 90%, or at least 95%, or at least 98% specific for the sRNA (e.g., iso-miR) over an annotated sequence and/or other non-predictive iso-miRs.
  • Annotated sequences can be determined with reference to miRBase.
  • PCR primers and fluorescent probes can be prepared and tested for their level of specificity.
  • Bicyclic nucleotides e.g., LNA, cET, and MOE
  • other nucleotide modifications including base modifications
  • the invention provides a kit comprising a panel of from 2 to about 100 sRNA predictor assays, or from about 2 to about 75 sRNA predictor assay, or from 2 to about 40 sRNA predictor assays, or from 2 to about 30, or from 2 to about 20, or from 2 to about 10 sRNA predictor assays.
  • the kit may comprise at least 5, at least 10, at least 20 sRNA predictor assays (e.g., reagents for such assays).
  • the kit may comprise at least one positive predictor and at least one negative predictor.
  • the kit comprises at least 5 positive predictors and at least 2 negative predictors.
  • the kit comprises a panel of from 4 to about 20, or from 4 to about 15, or from 4 to about 10 sRNA predictor assays.
  • Such assays may comprise reverse transcription (RT) primers, amplification primers and probes (such as fluorescent probes or dual labeled probes) specific for the sRNA predictors over annotated sequences as well as other (non- predictive) 5’- and/or 3’-templated and/or non-templated variations.
  • the kit is in the form of an array or other substrate containing probes for detection of sRNA predictors by hybridization.
  • the invention provides a method for determining a condition of a cell or organism (including with respect to animals, plants, and microbes).
  • the invention provides a method for evaluating the condition of an subject or patient.
  • the method comprises obtaining a biological sample (such as a biological fluid sample from a subject or patient), and identifying the presence or absence of one or more sRNA predictors (identified according to the method described above), thereby determining the condition of the cell or organism (e.g., the condition of the patient).
  • the condition identified is the condition that defines the experimental cohort, with respect to the comparator cohort.
  • the sRNA predictor(s) are identified in a subject or patient sample by a detection technology that involves amplification and/or probe hybridization, such as RT-PCR or TAQMAN assays, or other detection formats.
  • the sample is a biological fluid sample from a patient, and is selected from blood, serum, plasma, urine, saliva, or cerebrospinal fluid.
  • the sample may be a blood sample or samples derived therefrom.
  • at least two biological samples are tested, which may be selected from blood, serum, plasma, urine, saliva, and cerebrospinal fluid.
  • the patient is suspected of having a neurodegenerative disease, a cardiovascular disease, an inflammatory and/or immunological disease, or a cancer.
  • the patient may be displaying one or more symptoms thereof.
  • the patient is suspected of having a neurodegenerative disease selected from Amyotrophic Lateral Sclerosis (ALS), Parkinson’s Disease (PD), Alzheimer’s Disease (AD), Huntington’s Disease (HD), or Multiple Sclerosis (MS).
  • ALS Amyotrophic Lateral Sclerosis
  • PD Parkinson’s Disease
  • AD Alzheimer’s Disease
  • HD Huntington’s Disease
  • MS Multiple Sclerosis
  • the patient has signs of dementia or a movement disorder, or CNS lesions.
  • the patient has or is suspected of having or is at risk of a cardiovascular disease (CYD) optionally selected from coronary artery disease (CAD) such as angina and myocardial infarction, stroke, congestive heart failure, hypertensive heart disease, rheumatic heart disease, cardiomyopathy, heart arrhythmia, congenital heart disease, valvular heart disease, carditis, aortic aneurysms, peripheral artery disease, and venous thrombosis.
  • CYD cardiovascular disease
  • CAD coronary artery disease
  • the patient has a high risk score for heart attack or stroke.
  • the patient displays symptoms of an immune or inflammatory disorder, such as Lupus (SLE), Scleroderma, Vasculitis, Diabetes mellitus (e.g., Type 1 or Type 2), Graves’ Disease, Rheumatoid Arthritis, Multiple Sclerosis, Fibromyalgia, Psoriasis, Crohn’s Disease, Celiac Disease, COPD, or pulmonary fibrosis (e.g., IPF).
  • the condition is an inflammatory condition, which may manifest as type I hypersensitivity, type II hypersensitivity, type III hypersensitivity, and/or type IV hypersensitivity.
  • the patient has cancer, is suspected of having cancer, or is being screened for cancer.
  • the cancer may be bowel cancer, lung cancer, skin cancer, ovarian cancer, breast cancer among others.
  • the cancer is stage I or stage II cancer.
  • the cancer is stage III or stage IV.
  • the patient is a candidate for treatment with a checkpoint inhibitor or CAR-T therapy, chemotherapy, neoadjuvant therapy, or radiation therapy.
  • Illustrative cancers include, but are not limited to, basal cell carcinoma, biliary tract cancer; bladder cancer; bone cancer; brain and central nervous system cancer; breast cancer; cancer of the peritoneum; cervical cancer; choriocarcinoma; colon and rectum cancer; connective tissue cancer; cancer of the digestive system; endometrial cancer; esophageal cancer; eye cancer; cancer of the head and neck; gastric cancer (including gastrointestinal cancer); glioblastoma; hepatic carcinoma; hepatoma; intra epithelial neoplasm; kidney or renal cancer; larynx cancer; leukemia; liver cancer; lung cancer (e.g., small-cell lung cancer, non-small cell lung cancer, adenocarcinoma of the lung, and squamous carcinoma of the lung); melanoma; myeloma; neuroblastoma; oral cavity cancer (lip, tongue, mouth, and pharynx); ovarian cancer; pancreatic cancer; prostate cancer
  • the sample is tested for the presence or absence of at least about 2, or at least about 5, or at least about 10, or at least about 20, or at least about 30, or at least about 40, or at least about 50 sRNA predictors (e.g., from 4 to 50 sRNA predictors), where the presence of from 1 to about 10 positive predictors (or from 1 to 5 sRNA positive predictors) is indicative of the condition.
  • the absence of from 1 to 10 negative predictors is further indicative of the condition.
  • the presence of positive predictors in the panel, and the absence of negative predictors in the panel is scored to determine a probability that the patient has the condition of interest.
  • Patients that test positive for the condition of interest can then be further diagnosed and/or treated accordingly for the defined condition.
  • positive and/or negative predictors can be employed to classify a mixed population of cells in vivo or ex vivo, through targeted expression of a gene with a detectable or biological impact.
  • a desired protein can be expressed from a gene construct (using a vector such as a plasmid or viral vector) or expressed from mRNA delivered to cells in vivo or ex vivo.
  • the gene is delivered under the regulatory control of target site(s) for the one or more small RNA predictors.
  • the target site(s) can be placed in non-coding segments, such as the 3’ and/or 5’ UTRs, such that the encoded protein is only expressed in biologically significant amounts when the desired predictor(s) are absent in the cell.
  • the protein encoded by the construct may be a reporter protein, a transcriptional activator, a transcriptional repressor, a pro-apoptotic protein, a pro-survival protein, a lytic protein, an enzyme, a cytokine, a toxin, or a cell-surface receptor.
  • the encoded protein can be a fluorescent protein or an enzyme capable of performing a detectable reaction (e.g., b-galactosidase, alkaline phosphatase, luciferase, or horseradish peroxidase).
  • a detectable reaction e.g., b-galactosidase, alkaline phosphatase, luciferase, or horseradish peroxidase.
  • all cells expressing the positive or negative predictor will be differentiated from other cells, allowing a sub population of cells to be accurately identified ex vivo or in vivo.
  • the genetic constructs enable the identification of specific cell populations for isolation, such as a desired immune cell type or cells with a desired stem cell phenotype, e.g., by fluorescent cell sorting.
  • detectable constructs can also be useful in treatment of cancer, by, for example, aiding in precise surgical removal of the cancer or targeted radiation or chemotherapy.
  • the encoded protein can modulate a cellular pathway or activity of the cell.
  • the alteration in cellular activity can cause or alter apoptotic cell death, replication (e.g., DNA or cellular replication), cell differentiation, or cell migration.
  • apoptosis can be the result of the expression of a death receptor (e.g., FasR or TNFR), death receptor ligand (e.g., FasL or TNF), a caspase (e.g., caspase 3 or caspase 9), cytochrome-c, a BFG-containing proapoptotic protein (e.g., BAX, BAD, BID, or BIM), apoptosis inducing factor (AIF), or a protein toxin.
  • growth arrest can be the result of expression of a protein such as p2l, pl9ARF, p53, or RB protein, or tumor suppressor protein.
  • the encoded protein is a growth factor or cytokine, either an inflammatory or anti inflammatory cytokine.
  • the genetic construct (whether DNA or RNA) is administered to a subject having cancer, an immunological disorder such as an autoimmune diseases, a neurodegenerative disorder, a cardiovascular disorder, a metabolic disorder, or an infection (bacterial, viral, or parasitic infection).
  • an immunological disorder such as an autoimmune diseases, a neurodegenerative disorder, a cardiovascular disorder, a metabolic disorder, or an infection (bacterial, viral, or parasitic infection).
  • Administration of the genetic construct targets individual cells with precision based on internal molecular cues (presence or absence of one or more predictors).
  • the construct contains a target site specific for a negative sRNA predictor to avoid expression of the encoded protein in non-diseased cells (where the negative predictor will be present).
  • the encoded protein induces cell death or apoptosis in cells that do not express the negative predictor.
  • the protein is a toxin or protein that induces apoptosis or cell death.
  • the construct contains a target site specific for a positive sRNA predictor to avoid expression of the encoded protein in diseased cells.
  • the encoded protein may protect the cells from insult (e.g., a pro-survival protein), such as an insult in the form of chemotherapy, radiation, or immunooncology.
  • the encoded protein may be under the regulatory control of a target site for a small RNA predictor only present in diseased cells (positive predictor).
  • the construct would be expressed and limit damage and toxicity in non-diseased cells.
  • the conventional approach to miRNA sequence analysis for diagnostic use involves identifying up- or down-regulated miRNAs, typically with reference to an annotated sequence.
  • the goal is to identify dysregulated miRNAs (up or down-regulated) for validation in larger cohorts using targeted assays such as TAQMAN-based qPCR.
  • RNA fraction is extracted/isolated from samples, 3’ and 5’ adapters are ligated to sRNAs, and sRNAs are reverse transcribed, amplified, and sequenced.
  • adapter sequences are trimmed (typically using a Smith- Waterman Algorithm or close derivative thereof), and reads are aligned to a reference sequence. Residual sequences are sometimes analyzed by predictive programs to identify new miRNAs. Read numbers are quantified for each reference miRNA. See Figures 1A illustrating the conventional approach. Current data analysis methods analyze fold-changes between samples ( Figure 1B). Typically, deltas are around 1.8 to 5-fold, which is insufficient for a meaningful diagnostic test.
  • miRNA is a misnomer. For any given miRNA there are multiple iso-miRs that harbor templated and/or non-templated nucleotides at the 5’- and/or 3’-end (see Figure 2 and Figure 3).
  • the conventional method for analyzing miRNA sequence data’masks’ iso-miR data, since trimmed sequence reads are aligned back to a reference list of miRNA sequences (e.g. a comprehensive list of all cloned miRNAs, from whatever species the research is being performed in), typically sourced from miRBase, a miRNA sequence depository).
  • TAQMAN assays used in down-stream validation are highly-specific for the sequences they are designed to detected, and they are designed against the same reference list of miRNAs from miRBase. Thus, these TAQMAN assays only detect annotated miRNAs, and not closely related sequence variants of the annotated miRNA, including iso-miRs. See, Chen C, et al, Real-time quantification of microRNAs by stem-loop RT-PCR Nucleic Acids Res. 2005, 33(20) el79. Also, see Figure 5 showing specificity of TAQMAN assays against closely related variants. In embodiments of the process described herein, raw sequencing data is trimmed by identifying and removing the 3’ adapter sequences.
  • the 3’ adapter sequence to be trimmed is user-specified, and thus RNA sequencing data generated from any RNA-sequence platform can be used.
  • the software can employ ‘pattern matching’ to identify regular expressions (i.e. the user-specified 3’ adapter), and if desired a defined level of variation to the user-specified 3’ adapter, and then deletes them.
  • regular expressions i.e. the user-specified 3’ adapter
  • a defined level of variation to the user-specified 3’ adapter if desired a defined level of variation to the user-specified 3’ adapter, and then deletes them.
  • no‘fuzzy trimming’ as is seen with a Smith- Waterman Algorithm, because here only regular expressions, and if desired, the level of user-specified variation to the regular expression, is trimmed.
  • the 5’ most nucleotide (i.e.
  • nucleotide that defines the junction between the small RNA and the 3’ adapter must be present in a read in order for the regular expression to be recognized by the software program and trimmed.
  • Embodiments of the software accommodate up to: 5 wild cards, 1 insertion, 2 deletions, 1 insertion + 1 wild card, and 1 deletion + 1 wild card.
  • the program can trim nearly 100% of the sequence data, whereas most programs only trim around 80 to 85%.
  • Trimmed sequence data is not aligned to a reference, thereby retaining the individual iso-miR data, as well as many other small RNA families that would otherwise be eliminated, such as: miRNAs not listed in the reference, Piwi -interacting RNA (piRNA), small interfering RNA (siRNA), vault RNA (vtRNA), small nucleolar RNA (snoRNA), transfer RNA-derived small RNA (tsRNA), ribosomal RNA-derived small RNA fragments (rsRNA), small rDNA-derived RNA (srRNA), and small nuclear RNA (U-RNA).
  • miRNAs not listed in the reference Piwi -interacting RNA (piRNA), small interfering RNA (siRNA), vault RNA (vtRNA), small nucleolar RNA (snoRNA), transfer RNA-derived small RNA (tsRNA), ribosomal RNA-derived small RNA fragments (rsRNA), small rDNA-derived RNA (srRNA), and
  • Data is sorted based on individual sequence reads, and each sequence read is condensed to a single line and quantified.
  • the process uses a program to look for‘unique’ or‘binary’ RNA sequences that are only present in the cohort of interest. For example, to identify positive predictors, the sequence read content of Group B (i.e. the comparator cohort) is compiled into a dictionary, and the sequence read content of each sample in Group A (i.e. the experimental cohort) is compared against the dictionary and the following equation is executed: Group A - Group B. Positive predictors (i.e. unique/binary reads) found in cohort A are output to a new file and quantified.
  • the sequence read content of Group A i.e. the experimental cohort
  • Group B i.e. the comparator cohort
  • Negative predictors i.e. unique/binary reads found in cohort B are output to a new file and quantified.
  • stem-loop-RT based TAQMAN qPCR assays may be designed against any of the sequences of interest. Stem-loop-RT based TAQMAN qPCR assays are ultra-specific and give single nucleotide resolution ( Figure 5).
  • stem-loop-RT primer and/or qPCR primers, and/or TAQMAN probe can increase the base-pairing specificity and/or increase the melting temperature (Tm) of annealing.
  • Tm melting temperature
  • Stem-loop-RT-based TAQMAN qPCR assays can detect as few as 7 copies of a small RNA in a sample.
  • RNA sequencing data was trimmed using the methods described with the following adapter sequence: TGGAATTCTCGGGTGCCAAGGAACTC (SEQ ID NO: l). Resulting biomarkers had to be equal to or greater than twenty nucleotides after trimming to be considered for downstream analysis.
  • RNA predictors Eight positive small RNA predictors (only found in Huntington’s Disease patients) were selected for experimental validation.
  • Reverse transcription (RT) hairpin- based TaqMan quantitative polymerase chain reaction (qPCR) assays (ThermoFisher Scientific) were designed to specifically target those small RNAs.
  • cDNA libraries were multiplex-reverse transcribed from lOOOng of total RNA using the TaqMan MicroRNA Reverse Transcription Kit (ThermoFisher Scientific, Catalog Number: 4366596) and pooled RT primers, according to the manufacturer’s protocol. Resultant cDNA libraries were diluted 1:500 with lOmM Tris pH 8.0 (Millipore, Catalog Number: 648314).
  • RNA predictors were analyzed from 2ul of cDNA in triplicate, by TaqMan qPCR using targeted primers and probes, and Universal Master Mix II (ThermoFisher Scientific, Catalog Number: 4440043), in a 5ul reaction, thermocycled 50-times, in an ABI 7900HT Fast Real-Time PCR System fitted with a 384-well heat block.
  • Small RNA sequencing data from GSE72962 and GSE64977 was obtained from the GEO Database.
  • Small RNA sequencing data from phs000727.vl.pl was obtained from the dbGAP Database.
  • Sequence Read Archive (.sra) files were converted to .fastq format using the SRA Toolkit v2.8.0.
  • Raw small RNA sequencing data was trimmed using the methods described with the following adapter sequence: T GGAATT CTCGGGT GC C AAGGAACT C (SEQ ID NO: l). Resulting biomarkers had to be equal to or greater than twenty nucleotides after trimming to be considered for downstream analysis.
  • Biomarkers had to be equal to or greater than twenty nucleotides, and had to occur at a frequency of equal to or greater than 10% of the population to be considered.
  • Biomarkers had to be equal to or greater than twenty nucleotides, and had to occur at a frequency of equal to or greater than 10% of the population to be considered.
  • Biomarkers had to be equal to or greater than twenty nucleotides, and had to occur at a frequency of equal to or greater than 10% of the population to be considered.
  • RNA predictors can be found in multiple tisues and biological fluids including serum, and thus can be developed as convenient markers for neurodegenerative diseases such as PD.
  • Small RNA sequencing data from GSE46579 was obtained from the GEO Database.
  • Small RNA sequencing data from phs000727.vl.pl was obtained from the dbGAP Database.
  • Sequence Read Archive (.sra) files were converted to .fastq format using the SRA Toolkit v2.8.0.
  • Raw small RNA sequencing data was trimmed using the methods described with the following adapter sequence: T GGAATT CTCGGGT GC C AAGGAACT C (SEQ ID NO: l). Resulting biomarkers had to be equal to or greater than twenty nucleotides after trimming to be considered for downstream analysis.
  • Biomarkers had to be equal to or greater than twenty nucleotides, and had to occur at a frequency of equal to or greater than 10% of the population to be considered.
  • Biomarkers had to be equal to or greater than twenty nucleotides, and had to occur at a frequency of equal to or greater than 10% of the population to be considered.
  • Biomarkers had to be equal to or greater than twenty nucleotides, and had to occur at a frequency of equal to or greater than 10% of the population to be considered.
  • RNA sequencing data from GSE29173 was obtained from the GEO Database. Sequence Read Archive (.sra) files were converted to .fastq format using the SRA Toolkit v2.8.0. Raw small RNA sequencing data was trimmed using the methods described with the following adapter sequence:
  • Example 5 Use of binary small RNA predictors to develop targeted therapeutics
  • a therapeutic drug in the form of a plasmid, gene cassehe, or mRNA encoding for a protein that induces cell death (“Protein X”) could be developed that is under the regulatory control of a binary small RNA predictor. If the therapeutic drug were introduced into a system where healthy cells expressed a binary small RNA predictor, and diseased cells did not, Protein X would be specifically expressed in the diseased cells resulting in a therapeutic benefit ( Figure 14).
  • a therapeutic drug in the form of a plasmid, gene cassehe, or mRNA encoding for Protein Y could be developed that is under the regulatory control of a binary small RNA predictor. If the therapeutic drug were introduced into a system in combination with high-dose chemotherapy, radiation, immuno-oncology, or other agents to treat an underlying disease, this would result in an enhanced therapeutic benefit with limited or reduced normal tissue toxicity (Figure 15).

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Abstract

L'invention concerne un procédé d'identification ou de détection de petits ARN (pARN) prédicteurs d'une maladie ou d'une affection. Le procédé consiste consiste à identifier une ou plusieurs séquences de pARN qui sont présentes dans un ou plusieurs échantillons d'une cohorte expérimentale, et qui ne sont pas présentes dans une cohorte comparative ; et éventuellement à identifier une ou plusieurs séquences de pARN qui sont présentes dans un ou plusieurs échantillons d'une cohorte comparative, et qui ne sont pas présentes dans une cohorte expérimentale. Contrairement à l'identification de pARN non codants dérégulés (tels que des miR qui sont régulés à la hausse ou à la baisse), l'invention permet d'identifier des pARN qui sont des prédicteurs binaires, à savoir présents dans une cohorte (par exemple, une cohorte expérimentale) et non dans une autre (par exemple, une cohorte comparative). En outre, en quantifiant des lectures de séquences individuelles (par exemple, iso-miRs), sans consolider les lectures en séquences de référence annotées, l'invention déverrouille l'utilité diagnostique de miRs et d'autres sARN.
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WO2020023789A3 (fr) * 2018-07-25 2020-02-27 Srnalytics, Inc. Petits prédicteurs d'arn pour la maladie d'alzheimer

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2020023789A3 (fr) * 2018-07-25 2020-02-27 Srnalytics, Inc. Petits prédicteurs d'arn pour la maladie d'alzheimer

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