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Dynamic Risk Profiling Using Serial Tumor Biomarkers for Personalized Outcome Prediction.
- Source :
-
Cell [Cell] 2019 Jul 25; Vol. 178 (3), pp. 699-713.e19. Date of Electronic Publication: 2019 Jul 04. - Publication Year :
- 2019
-
Abstract
- Accurate prediction of long-term outcomes remains a challenge in the care of cancer patients. Due to the difficulty of serial tumor sampling, previous prediction tools have focused on pretreatment factors. However, emerging non-invasive diagnostics have increased opportunities for serial tumor assessments. We describe the Continuous Individualized Risk Index (CIRI), a method to dynamically determine outcome probabilities for individual patients utilizing risk predictors acquired over time. Similar to "win probability" models in other fields, CIRI provides a real-time probability by integrating risk assessments throughout a patient's course. Applying CIRI to patients with diffuse large B cell lymphoma, we demonstrate improved outcome prediction compared to conventional risk models. We demonstrate CIRI's broader utility in analogous models of chronic lymphocytic leukemia and breast adenocarcinoma and perform a proof-of-concept analysis demonstrating how CIRI could be used to develop predictive biomarkers for therapy selection. We envision that dynamic risk assessment will facilitate personalized medicine and enable innovative therapeutic paradigms.<br /> (Copyright © 2019. Published by Elsevier Inc.)
- Subjects :
- Algorithms
Antineoplastic Agents therapeutic use
Biomarkers, Tumor blood
Breast Neoplasms drug therapy
Breast Neoplasms mortality
Circulating Tumor DNA blood
Female
Humans
Kaplan-Meier Estimate
Lymphoma, Large B-Cell, Diffuse drug therapy
Lymphoma, Large B-Cell, Diffuse mortality
Neoadjuvant Therapy
Prognosis
Progression-Free Survival
Proportional Hazards Models
Risk Assessment
Treatment Outcome
Biomarkers, Tumor metabolism
Breast Neoplasms pathology
Lymphoma, Large B-Cell, Diffuse pathology
Precision Medicine
Subjects
Details
- Language :
- English
- ISSN :
- 1097-4172
- Volume :
- 178
- Issue :
- 3
- Database :
- MEDLINE
- Journal :
- Cell
- Publication Type :
- Academic Journal
- Accession number :
- 31280963
- Full Text :
- https://doi.org/10.1016/j.cell.2019.06.011