MP60-08 DEEP TRANSCRIPTOMIC PROFILING OF PROSTATE CANCER WITH QUANTITATIVE MEASURES: A SPECTRA APPROACH

You have accessJournal of UrologyProstate Cancer: Markers (MP60)1 Sep 2021MP60-08 DEEP TRANSCRIPTOMIC PROFILING OF PROSTATE CANCER WITH QUANTITATIVE MEASURES: A SPECTRA APPROACH Heidi Hanson, Jacob Ambrose, Brock O'Neil, Greg Lee, Claire Leiser, Rosalie Waller, Joemy Ramsay, Michael Madsen, Rupam Das, Christopher Dechet, Brian Avery, and Nicola Camp Heidi HansonHeidi Hanson More articles by this author , Jacob AmbroseJacob Ambrose More articles by this author , Brock O'NeilBrock O'Neil More articles by this author , Greg LeeGreg Lee More articles by this author , Claire LeiserClaire Leiser More articles by this author , Rosalie WallerRosalie Waller More articles by this author , Joemy RamsayJoemy Ramsay More articles by this author , Michael MadsenMichael Madsen More articles by this author , Rupam DasRupam Das More articles by this author , Christopher DechetChristopher Dechet More articles by this author , Brian AveryBrian Avery More articles by this author , and Nicola CampNicola Camp More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002095.08AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Methods that embrace the complexity of prostate cancer (CaP) gene expression are necessary for accurate phenotypic characterization. We previously presented a novel agnostic computational framework, SPECTRA, to describe RNA sequencing (RNAseq) data using multiple quantitative expression variables, or transcriptomic spectra (TrS). Here, we implement this technique to derive a set of TrS variables for CaP tumors from The Cancer Genome Atlas (TCGA). We compare the identified TrS with clinical and sample characteristics, progression-free interval, and previously established CaP subtypes. METHODS: Gene-level RNAseq data were downloaded from the Genomic Data Commons (GDC) Data Portal. Data were preprocessed using the SPECTRA protocol (nsamples=480, ngenes = 10,438) and matrix factorization was used to derive quantitative measures for each TrS. Linear, logistic, multinomial logistic, and Cox regression were used to assess the relationship between TrS and age, Gleason Risk Score, tumor stage, and progression free interval. Two approaches were used to select TrS associated with demographic, clinical, and molecular features of the tumors; “hard-thresholding” (Bonferonni corrected p-value) and lasso regularization. We compare model fit statistics to determine the difference in predictive power between our TrS and previously defined molecular subtypes defined by fusion of ETS family genes. RESULTS: We identified 21 TrS in our data that together explain 65.5% of the variance in global gene expression across CaP tumors in TCGA. Many of the TrS were associated with demographic and clinical characteristics of the patients and progression-free interval, with TrS3 having the strongest associations (Figure 1). The difference in predictive power of the TrS vs the previously identified molecular subtypes was substantial, with the difference in pseudo R-square in the range of 30% on average. This suggests that more information can be gained from quantitative vs. qualitative measures of gene expression. CONCLUSIONS: This approach had substantially better model fit than traditional qualitative tumor classifications and could lead to innovative ways to understanding gene expression patterns driving individual risk, treatment response and survival. Source of Funding: 5K07CA230150-03 © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e1044-e1044 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Heidi Hanson More articles by this author Jacob Ambrose More articles by this author Brock O'Neil More articles by this author Greg Lee More articles by this author Claire Leiser More articles by this author Rosalie Waller More articles by this author Joemy Ramsay More articles by this author Michael Madsen More articles by this author Rupam Das More articles by this author Christopher Dechet More articles by this author Brian Avery More articles by this author Nicola Camp More articles by this author Expand All Advertisement Loading ...

Topic: Prostate Cancer Treatment and Research
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences