To determine prognostic and predictive clinical outcomes in metastatic hormone-sensitive prostate cancer (mHSPC) and metastatic castrate-resistant prostate cancer (mCRPC) on the basis of a combination of plasma-derived genomic alterations and lipid features in a longitudinal cohort of patients with advanced prostate cancer.
A multifeature classifier was constructed to predict clinical outcomes using plasma-based genomic alterations detected in 120 genes and 772 lipidomic species as informative features in a cohort of 71 patients with mHSPC and 144 patients with mCRPC. Outcomes of interest were collected over 11 years of follow-up. These included in mHSPC state early failure of androgen-deprivation therapy (ADT) and exceptional responders to ADT; early death (poor prognosis) and long-term survivors in mCRPC state. The approach was to build binary classification models that identified discriminative candidates with optimal weights to predict outcomes. To achieve this, we built multi-omic feature-based classifiers using traditional machine learning (ML) methods, including logistic regression with sparse regularization, multi-kernel Gaussian process regression, and support vector machines.
The levels of specific ceramides (d18:1/14:0 and d18:1/17:0), and the presence of CHEK2 mutations, AR amplification, and RB1 deletion were identified as the most crucial factors associated with clinical outcomes. Using ML models, the optimal multi-omics feature combination determined resulted in AUC scores of 0.751 for predicting mHSPC survival and 0.638 for predicting ADT failure; and in mCRPC state, 0.687 for prognostication and 0.727 for exceptional survival. The models were observed to be superior than using a limited candidate number of features for developing multi-omic prognostic and predictive signatures.
Using a ML approach that incorporates multiple omic features improves the prediction accuracy for metastatic prostate cancer outcomes significantly. Validation of these models will be needed in independent data sets in future.
JCO clinical cancer informatics. 2023 Jul [Epub]
Shikai Fang, Shandian Zhe, Hui-Ming Lin, Arun A Azad, Heidi Fettke, Edmond M Kwan, Lisa Horvath, Blossom Mak, Tiantian Zheng, Pan Du, Shidong Jia, Robert M Kirby, Manish Kohli
University of Utah, The School of Computing, Scientific Computing and Imaging Institute, Salt Lake City, UT., The School of Computing, University of Utah, Salt Lake City, UT., Garvan Institute for Medical Research, Darlinghurst, Sydney, New South Wales, Australia., Sir Peter MacCallum Department of Oncology, Department of Medical Oncology, University of Melbourne, Melbourne, Australia., Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, Canada., Predicine Inc, Hayward, CA., The School of Computing, Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT., Division of Oncology, Department of Internal Medicine, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT.