Dynamic Risk Profiling Using Serial Tumor Biomarkers for Personalized Outcome Prediction.
Fiche publication
Date publication
juillet 2019
Journal
Cell
Auteurs
Membres identifiés du Cancéropôle Est :
Dr CASASNOVAS Olivier
Tous les auteurs :
Kurtz DM, Esfahani MS, Scherer F, Soo J, Jin MC, Liu CL, Newman AM, Dührsen U, Hüttmann A, Casasnovas O, Westin JR, Ritgen M, Böttcher S, Langerak AW, Roschewski M, Wilson WH, Gaidano G, Rossi D, Bahlo J, Hallek M, Tibshirani R, Diehn M, Alizadeh AA
Lien Pubmed
Résumé
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.
Mots clés
biomarkers, cancer, liquid biopsy, personalized medicine, predictive modeling
Référence
Cell. 2019 Jul 1;: