Multi-omic assessment

Abstract

Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

1 . A multi-omic method, comprising:
 obtaining multi-omic data from a biofluid sample of a subject suspected of having cancer, the multi-omic data comprising proteomic measurements and nucleic acid sequencing measurements; and   applying a classifier to the multi-omic data to evaluate the cancer, wherein the classifier comprises protein and nucleic acid features, and wherein the classifier distinguishes between biofluid samples of subjects with and without cancer with a performance characterized by a receiver operating characteristic (ROC) curve having an average or median area under the curve (AUC) of at least 0.9.   
     
     
         2 . The method of  claim 1 , wherein the ROC curve comprises a true positive rate of at least 0.8 when a false positive rate of the ROC curve is less than 0.05. 
     
     
         3 . The method of  claim 1 , wherein the ROC curve comprises a true positive rate of at least 0.9 when a false positive rate of the ROC curve is less than 0.2. 
     
     
         4 . The method of  claim 1 , wherein the classifier distinguishes between biofluid samples of subjects with and without cancer with greater accuracy than a classifier based on either the proteomic measurements or the nucleic acid sequencing measurements alone. 
     
     
         5 . The method of  claim 1 , wherein the proteomic measurements and the nucleic acid sequencing measurements each comprise at least 500 measurements. 
     
     
         6 . The method of  claim 1 , wherein the proteomic measurements are generated from proteins adsorbed to nanoparticles. 
     
     
         7 . The method of  claim 1 , wherein the proteomic measurements are generated upon contacting the biofluid sample with internal standard proteins. 
     
     
         8 . The method of  claim 1 , wherein the nucleic acid sequencing measurements comprise mRNA sequencing measurements. 
     
     
         9 . The method of  claim 1 , wherein the nucleic acid sequencing measurements comprise microRNA sequencing measurements. 
     
     
         10 . The method of  claim 1 , wherein applying the classifier to the multi-omic data to evaluate the cancer comprises:
 applying a first classifier to the proteomic measurements to generate a first label corresponding to a presence, absence, or likelihood of the cancer,   applying a second classifier to the nucleic acid sequencing measurements to generate a second label corresponding to a presence, absence, or likelihood of the cancer, and   evaluating the cancer based on (a), (b) or (c):
 (a) a non-weighted average of the first and second labels, 
 (b) a weighted average of the first and second labels, or 
 (c) a majority voting score based on the first and second labels. 
   
     
     
         11 . The method of  claim 10 , further comprising evaluating the cancer based on the weighted average of the first and second labels, wherein the weighted average is generated by assigning weights to results of the first and second classifiers based on area under a ROC curve, area under a precision-recall curve, accuracy, precision, recall, sensitivity, F1-score, specificity, or a combination thereof. 
     
     
         12 . The method of  claim 1 , wherein applying the classifier to the multi-omic data to evaluate the cancer comprises:
 obtaining a subset of features from among the proteomic measurements;   obtaining at least a subset of features from among the nucleic acid sequencing measurements;   pooling the subset of features from among the proteomic measurements and the at least the subset of features from among the nucleic acid sequencing measurements to obtain pooled features; and   evaluating the cancer based on the pooled features.   
     
     
         13 . The method of  claim 12 , wherein obtaining the subset of features from among the proteomic measurements or obtaining at least the subset of features from among the nucleic acid sequencing measurements comprises obtaining top features based on univariate data. 
     
     
         14 . The method of  claim 1 , wherein the classifier is trained using deep learning, a random forest classification analysis, a support vector machine analysis, a naive Bayes analysis, or a hidden Markov analysis. 
     
     
         15 . The method of  claim 1 , wherein the cancer is selected from the group consisting of: lung cancer, pancreatic cancer, colon cancer, liver cancer, breast cancer, and ovarian cancer. 
     
     
         16 . The method of  claim 1 , wherein the classifier identifies the multi-omic data as indicative of cancer, and wherein the method further comprises administering a chemotherapy, pharmaceutical, radiation or surgical cancer treatment to the subject. 
     
     
         17 . The method of  claim 1 , wherein the multi-omic data further comprise lipid or metabolite measurements, and wherein the classifier further comprises lipid or metabolite features. 
     
     
         18 . A multi-omic method, comprising:
 obtaining multi-omic data from a biofluid sample of a subject suspected of having cancer, the multi-omic data comprising proteomic measurements and RNA sequencing measurements; and   applying a classifier to the multi-omic data to evaluate the cancer, wherein the classifier comprises protein and RNA features, wherein the classifier distinguishes between biofluid samples of subjects with and without cancer with at least 5% greater accuracy than a classifier based on the proteomic measurements alone and with at least 5% greater accuracy than a classifier based on the RNA sequencing measurements alone.   
     
     
         19 . The method of  claim 18 , wherein the accuracy comprises an area under the curve of a receiver operating characteristic curve. 
     
     
         20 . The method of  claim 18 , wherein the accuracy comprises a sensitivity at a given specificity. 
     
     
         21 . The method of  claim 18 , wherein the proteomic measurements are generated from proteins adsorbed to nanoparticles. 
     
     
         22 . The method of  claim 18 , wherein the proteomic measurements are generated upon contacting the biofluid sample with internal standard proteins. 
     
     
         23 . The method of  claim 18 , wherein the RNA sequencing measurements comprise mRNA sequencing measurements. 
     
     
         24 . The method of  claim 18 , wherein the RNA sequencing measurements comprise microRNA sequencing measurements. 
     
     
         25 . The method of  claim 18 , wherein the classifier that is applied to the multi-omic data identifies the multi-omic data as indicative of cancer, and wherein the method further comprises administering a chemotherapy, pharmaceutical, radiation or surgical cancer treatment to the subject.