Multi-Omic Integration of Blood-Based Tumor-Associated Genomic and Lipidomic Profiles Using Machine Learning Models in Metastatic Prostate Cancer
- Author(s)
- Fang, S; Zhe, S; Lin, HM; Azad, AA; Fettke, H; Kwan, EM; Horvath, L; Mak, B; Zheng, T; Du, P; Jia, S; Kirby, RM; Kohli, M;
- Journal Title
- JCO Clinical Cancer Informatics
- Publication Type
- Research article
- Abstract
- PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
- Publisher
- American Society of Clinical Oncology
- Keywords
- Male; Humans; *Prostatic Neoplasms, Castration-Resistant/diagnosis/genetics/therapy; Androgen Antagonists/therapeutic use; Lipidomics; Multiomics; Retrospective Studies; Genomics
- Department(s)
- Laboratory Research; Medical Oncology
- PubMed ID
- 37490642
- Publisher's Version
- https://doi.org/10.1200/CCI.23.00057
- Open Access at Publisher's Site
https://doi.org/10.1200/cci.23.00057- Terms of Use/Rights Notice
- Refer to copyright notice on published article.
Creation Date: 2023-10-19 04:37:31
Last Modified: 2023-10-19 04:37:56