Abstract Number:
2415
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Jadon Wagstaff (1), K-T Varley (1)
Institutions:
(1) Huntsman Cancer Institute, Salt Lake City
Co-Author:
First Author:
Presenting Author:
Abstract Text:
The goal of this study is to develop an assay to detect and differentiate methylated circulating tumor DNA (ctDNA) from 8 common cancer types using blood plasma. Tumor and normal tissue sample 450k CG DNA methylation data from The Cancer Genome Atlas (TCGA, n = 9,423) are used to select genomic regions where CGs are hypermethylated in cancer tissue. CGs are selected using a multinomial elastic net model on 70% of the TCGA data where hyperparameters are selected using the harmonic mean of model accuracy and variable stability. A model built on training data using the selected CGs correctly classify cancer types and normal tissue with an average of 93% accuracy on the remaining 30% of data. A final set of 341 genomic loci are selected for use in the assay. Preliminary assay results show that each locus yields an average of 1000 uniquely sequenced DNA molecules per sample which is critical to detect low levels of ctDNA expected in blood plasma in early stages of cancer. We plan to build a classification model using in silico titrations of methylated DNA into data characterized from healthy donor blood plasma, then test the model accuracy using blood plasma from cancer patients.
Keywords:
cancer|liquid biopsy|bioinformatics|feature selection|classification|penalized regression
Sponsors:
Section on Statistics in Genomics and Genetics
Tracks:
Miscellaneous
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