Methods for detecting disease using analysis of rna

Abstract

Methods for measuring subpopulations of ribonucleic acid (RNA) molecules are provided. In some embodiments, methods of generating a sequencing library from a plurality of RNA molecules in a test sample obtained from a subject are provided, as well as methods for analyzing the sequencing library to detect, e.g., the presence or absence of a disease.

1 .- 36 . (canceled) 
     
     
         37 . A method of detecting cancer in a subject, the method comprising:
 (a) measuring a plurality of target cell-free RNA (cfRNA) molecules in a sample of the subject, wherein the plurality of target cfRNA molecules are selected from one or more transcripts of Tables 1-7; and   (b) detecting the cancer, wherein detecting the cancer comprises detecting one or more of the target cfRNA molecules above a threshold level.   
     
     
         38 . The method of  claim 37 , wherein
 (a) the plurality of target cfRNA molecules are selected from at least 5, 10, 15, or 20 transcripts of Tables 1-7;   (b) the plurality of target cfRNA molecules comprise a plurality of transcripts from Table 1, from each of Table 2 and 5, or from each of Tables 3-4 and 6;   (c) the plurality of target cfRNA molecules comprise all of the transcripts of one or more of Tables 1, 2, 3, 4, 5, or 6; or (d) the plurality of target cfRNA molecules comprise transcripts from one or more of Tables 1-6 and one or more transcripts from Table 7.   
     
     
         39 - 41 . (canceled) 
     
     
         42 . The method of  claim 37 , wherein the plurality of target cfRNA molecules detected above a threshold are cfRNA molecules derived from a plurality of genes selected from the group consisting of: AGR2, BPIFA1, CASP14, CSN1S1, DISP2, EIF2D, FABP7, GABRG1, GNAT3, GRHL2, HOXC10, IDI2-AS1, KRT16P2, LALBA, LINC00163, NKX2-1, OPN1SW, PADI3, PTPRZ1, ROS1, S100A7, SCGB2A2, SERPINB5, SFTA3, SFTPA2, SLC34A2, TFF1, VTCN1, WFDC2, MUC5B, SMIM22, CXCL17, RNU1-1, and KLK5. 
     
     
         43 . The method of  claim 37 , wherein
 (a): (i) the cancer is lung cancer, and (ii) the plurality of target cfRNA molecules detected above a threshold are cfRNA molecules derived from a plurality of genes selected from the group consisting of: ROS1, NKX2-1, GGTLC1, SLC34A2, SFTPA2, BPIFA1, SFTA3, GABRG1, AGR2, GNAT3, MUC5B, SMIM22, CXCL17, and WFDC2;   (b): (i) the cancer is breast cancer, and (ii) the plurality of target cfRNA molecules detected above a threshold are cfRNA molecules derived from a plurality of genes selected from the group consisting of: SCGB2A2, CSN1S1, VTCN1, FABP7, LALBA, RNU1-1, OPN1SW, CASP14, KLK5, and WFDC2; or   (c): (i) the cancer is breast cancer, and (ii) the plurality of target cfRNA molecules detected above a threshold are cfRNA molecules derived from a plurality of genes selected from the group consisting of: CASP14, CRABP2, FABP7, SCGB2A2, SERPINB5, TRGV10, VGLL1, TFF1, and AC007563.5.   
     
     
         44 - 45 . (canceled) 
     
     
         46 . The method of  claim 37 , wherein the measuring comprises sequencing, microarray analysis, reverse transcription PCR, real-time PCR, quantitative real-time PCR, digital PCR, digital droplet PCR, digital emulsion PCR, multiplex PCR, hybrid capture, oligonucleotide ligation assays, or any combination thereof. 
     
     
         47 . The method of  claim 37 , wherein the measuring comprises sequencing the cfRNA molecules to produce cfRNA sequence reads, and the sequencing the cfRNA molecules optionally comprises (i) whole transcriptome sequencing; (ii) reverse transcription to produce cDNA molecules, followed by sequencing the cDNA molecules to produce the cfRNA sequence reads; or (iii) enriching for the target cfRNA molecules or cDNA molecules thereof. 
     
     
         48 - 50 . (canceled) 
     
     
         51 . The method of  claim 37 , wherein the sample comprises a biological fluid, wherein the biological fluid is optionally blood, a blood fraction, plasma, serum, urine, saliva, pleural fluid, pericardial fluid, cerebrospinal fluid (CSF), peritoneal fluid, or a combination thereof. 
     
     
         52 - 53 . (canceled) 
     
     
         54 . The method of  claim 37 , wherein detecting one or more of the target cfRNA molecules above a threshold level comprises:
 (a): (i) detection, (ii) detection above background, or (iii) detection at a level that is greater than a level of the target cfRNA molecules in subjects that do not have the condition;   (b): detecting the one or more target cfRNA molecules at a level that is at least about 10 times greater than a level in subjects that do not have the condition; or   (c): detection above a threshold value of 0.5 to 5 reads per million (RPM).   
     
     
         55 - 56 . (canceled) 
     
     
         57 . The method of  claim 37 , wherein detecting one or more of the target cfRNA molecules above a threshold level comprises:
 (a) determining an indicator score for each target cfRNA molecule by comparing the expression level of each of the target cfRNA molecules to an RNA tissue score matrix;   (b) aggregating the indicator scores for each target cfRNA molecule; and,   (c) detecting the cancer when the indicator score exceeds a threshold value.   
     
     
         58 . The method of  claim 47 , wherein detecting one or more of the target cfRNA molecules above a threshold level comprises inputting the sequence reads into a machine learning or deep learning model; optionally wherein (a) the machine learning or deep learning model comprises logistic regression, random forest, gradient boosting machine, Naïve Bayes, neural network, or multinomial regression, or (b) the machine learning or deep learning model optionally transforms values of one or more features to a disease state prediction for the subject through a function comprising learned weights. 
     
     
         59 - 60 . (canceled) 
     
     
         61 . The method of  claim 37 , wherein the cancer comprises:
 (i) a carcinoma, a sarcoma, a myeloma, a leukemia, a lymphoma, a blastoma, a germ cell tumor, or any combination thereof;   (ii) a carcinoma selected from the group consisting of adenocarcinoma, squamous cell carcinoma, lung cancer, nasopharyngeal, colorectal, anal, liver, urinary bladder, testicular, cervical, ovarian, gastric, esophageal, head-and-neck, pancreatic, prostate, renal, thyroid, melanoma, and breast carcinoma;   (iii) hormone receptor negative breast carcinoma or triple negative breast carcinoma;   (iv) a sarcoma selected from the group consisting of: osteosarcoma, chondrosarcoma, leiomyosarcoma, rhabdomyosarcoma, mesothelial sarcoma (mesothelioma), fibrosarcoma, angiosarcoma, liposarcoma, glioma, and astrocytoma;   (v) a leukemia selected from the group consisting of myelogenous, granulocytic, lymphatic, lymphocytic, and lymphoblastic leukemia; or   (vi) a lymphoma selected from the group consisting of: Hodgkin's lymphoma and Non-Hodgkin's lymphoma.   
     
     
         62 . The method of  claim 37 , wherein detecting the cancer comprises determining a cancer stage, determining cancer progression, determining a cancer type, determining cancer tissue of origin, or a combination thereof. 
     
     
         63 . The method of  claim 37 , further comprising selecting a treatment based on the cancer detected, and optionally treating the subject with the selected treatment. 
     
     
         64 . The method of  claim 63 , wherein the treatment comprises surgical resection, radiation therapy, or administering an anti-cancer agent. 
     
     
         65 . (canceled) 
     
     
         66 . A method of identifying cancer biomarkers in samples collected from one or more subjects, the method comprising:
 (a) sequencing cfRNA of a biological fluid collected from subjects without cancer to produce non-cancer sequencing reads;   (b) for a plurality of matched samples collected from one or more subjects with a cancer:
 (i) sequencing DNA and RNA collected from a cancer tissue of a matched sample to produce sequencing reads for the cancer tissue; 
 (ii) sequencing cfDNA and cfRNA collected from a matched biological fluid of the matched sample to produce sequencing reads for the matched biological fluid; 
 (iii) measuring a tumor fraction by relating counts of cfDNA sequencing reads for the matched biological fluid to corresponding counts of DNA sequencing reads for the cancer tissue; and 
 (iv) measuring tumor content for one or more candidate biomarkers by multiplying a count of the RNA sequencing reads for the one or more candidate biomarkers by the tumor fraction, wherein the one or more candidate biomarkers are expressed at a higher level in the matched biological fluid than in the biological fluid collected from the subjects without cancer; 
   (c) modeling expression of the one or more candidate biomarkers in cfRNA using the tumor content as a covariate; and   (d) identifying one or more cfRNA cancer biomarkers from among the one or more candidate biomarkers based on the modeling.   
     
     
         67 . The method of  claim 66 , wherein the method further comprises: (a) selectively measuring expression of the one or more cancer biomarkers in a biological fluid of a test subject; or (b) sequencing cfRNA from a biological fluid of a test subject, and generating an output for the test subject based on levels of the one or more cancer biomarkers, wherein the output indicates: a presence of cancer, determines a cancer stage, monitors a cancer progression, or determines a cancer type. 
     
     
         68 . (canceled) 
     
     
         69 . The method of  claim 67 , wherein a machine learning or deep learning model transforms values for sequencing reads of the cfRNA of the test subject to the output for the test subject through a function comprising learned weights. 
     
     
         70 . The method of  claim 67 , further comprising selecting a cancer treatment for the test subject, and optionally administering the treatment to the test subject. 
     
     
         71 . The method of  claim 66 , wherein the modeling
 (i) comprises negative binomial general linear model analysis (NB-GLM);   (ii) is performed using a computer-implemented classification model which applies at least one of a leave-one-out (LOO) or k-fold cross validation classification to classify different cancer features, wherein k-fold is at least 5-fold; or   (iii) comprises inputting the one or more DNA, RNA cfDNA or cfRNA sequences into a machine learning or deep learning model, wherein the machine learning or deep learning model optionally comprises logistic regression, random forest, gradient boosting machine, Naïve Bayes, neural network, or multinomial regression.   
     
     
         72 - 76 . (canceled)