SYSTEMS AND METHODS FOR PREDICTING PATHOGENIC STATUS OF FUSION CANDIDATES DETECTED IN NEXT GENERATION SEQUENCING DATA
A method of categorizing fusions is provided by the present disclosure. The method includes receiving labeled fusion data including at least one of DNA data or RNA data including at least one detected fusion associated with a specimen, providing the labeled fusion data to a classifier trained to generate a pathogenicity metric corresponding to pathogenicity of each detected fusion, receiving at least one pathogenicity metric from the classifier, and generating a report including one or more detected fusions included in the at least one detected fusion based on the pathogenicity metrics.
1 . A method of categorizing fusions, comprising:
receiving labeled fusion data comprising at least one of DNA data or RNA data comprising at least one detected fusion associated with a specimen;
providing the labeled fusion data to a classifier trained to generate a pathogenicity metric corresponding to pathogenicity of each detected fusion;
receiving at least one pathogenicity metric from the classifier; and
generating a report comprising one or more detected fusions included in the at least one detected fusion based on the pathogenicity metrics.
2 . The method of claim 1 , wherein the pathogenicity metric is a numeric risk score in a range of 0 to 1.
3 . The method of claim 2 further comprising:
generating a pathogenicity categorization corresponding to the labeled fusion data by comparing the pathogenicity metric to at least one predetermined threshold.
4 . The method of claim 3 , wherein the pathogenicity categorization is one of a low likelihood of pathogenicity, a medium likelihood of pathogenicity, or a high likelihood of pathogenicity.
5 . The method of claim 1 , wherein the pathogenicity metric is a pathogenicity categorization.
6 . The method of claim 1 , wherein the pathogenicity metric is one of a low likelihood of pathogenicity, a medium likelihood of pathogenicity, or a high likelihood of pathogenicity.
7 . The method of claim 1 , wherein the labeled fusion data comprises read data, and wherein the method further comprises:
generating a pathogenicity categorization corresponding to the labeled fusion data by:
comparing the pathogenicity metric to a first predetermined threshold; and
comparing the read data to a second predetermined threshold.
8 . The method of claim 7 , wherein the read data comprises at least one of a number of reads spanning a fusion breakpoint or a number of high quality reads spanning the fusion breakpoint.
9 . The method of claim 1 , wherein the labeled fusion data comprises at least one fusion having a 5′ partner sequence and a 3′ partner sequence, and for each fusion included in the at least one fusion, the labeled fusion data further comprises as least one of a Human Genome Organisation (HUGO) Gene Nomenclature Committee (HGNC) gene symbol for the 5′ partner sequence, a HGNC gene symbol for the 3′ partner sequence, an Ensembl ID for the 5′ partner sequence, an Ensembl ID for the 3′ partner sequence, a strandedness of the 5′ partner sequence, a strandedness of the 3′ partner sequence, a number or letter denoting a human chromosome on which the 5′ partner sequence is located, a number or letter denoting the human chromosome on which the 3′ partner sequence is located, a genomic coordinate start of the 5′ partner sequence, a genomic coordinate end of the 5′ partner sequence, a genomic coordinate start of the 3′ partner sequence, or a genomic coordinate end of the 3′ partner sequence.
10 . The method of claim 9 , wherein the strandedness of the 5′ partner sequence is one of forward or reverse.
11 . The method of claim 9 , wherein the strandedness of the 3′ partner sequence is one of forward or reverse.
12 . The method of claim 1 , wherein the classifier comprises a machine learning model.
13 . The method of claim 1 , wherein the classifier comprises at least one of a gradient boosting model, a random forest model, a neural network, a regression model, or a Naive Bayes model.
14 . The method of claim 1 , wherein the classifier is trained based on training data comprising a group of positive control fusions and a group of negative control fusions, each fusion included in the group of positive control fusions comprises a canonical fusion, and each fusion included in the group of negative control fusions is associated with healthy tissue.
15 . The method of claim 1 , wherein the classifier is previously trained by:
sequentially providing, for each of a number of fusions, a matrix of feature vectors to the classifier; and
sequentially updating, for each fusion included in the number of fusions, weights included in the classifier based on the matrix of feature vectors and a label associated with the fusion.
16 . The method of claim 15 further comprising:
receiving, for each fusion included in the number of fusions, a pathogenicity score from the classifier;
determining model performance based on the pathogenicity score associated with each fusion, the label associated with each fusion, and a threshold; and
updating the threshold based on the model performance.
17 . The method of claim 1 further comprising:
outputting the report to a physician.
18 . The method of claim 1 further comprising:
flagging a fusion included in the labeled fusion data for review based on at least one of physician review or biological validation based on the labeled fusion data and the pathogenicity metric.
19 . The method of claim 1 , wherein the labeled fusion data is derived from next generation sequencing data.
20 . The method of claim 1 further comprising:
categorizing a plurality of nucleic acid fusion events as oncogenic or not oncogenic based on at least one of a read level or an actionable fusion based on a knowledge database.
21 . The method of claim 1 , wherein the specimen is derived from a patient, and the report further comprises at least one therapy matched to the patient based on the detected fusions.
22 . The method of claim 1 , wherein the labeled fusion data comprises whole transcriptome RNA sequencing data generated by sequencing the specimen.
23 . The method of claim 22 , wherein the specimen comprises at least a portion of a tumor.
24 . A fusion categorization system comprising at least one processor and at least one memory, the system configured to:
receive labeled fusion data comprising at least one of DNA data or RNA data comprising at least one detected fusion associated with a specimen;
provide the labeled fusion data to a classifier trained to generate a pathogenicity metric corresponding to pathogenicity of each detected fusion;
receive at least one pathogenicity metric from the classifier; and
generate a report comprising one or more detected fusions included in the at least one detected fusion based on the pathogenicity metrics.
25 . A method of categorizing fusions, comprising:
receiving labeled fusion data comprising at least one of DNA data or RNA data comprising at least one detected fusion associated with a patient;
providing the labeled fusion data to a classifier trained to generate a pathogenicity metric corresponding to pathogenicity of each detected fusion;
receiving at least one pathogenicity metric from the classifier; and
generating a report comprising one or more detected fusions included in the at least one detected fusion based on the pathogenicity metrics.