Dynamic Risk Profiling Using Serial Tumor Biomarkers for Personalized Outcome Prediction

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 asses...

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Bibliographic Details
Published in:	Cell Vol. 178; no. 3; pp. 699 - 713.e19
Main Authors:	Kurtz, David M., Esfahani, Mohammad S., Scherer, Florian, Soo, Joanne, Jin, Michael C., Liu, Chih Long, Newman, Aaron M., Dührsen, Ulrich, Hüttmann, Andreas, Casasnovas, Olivier, Westin, Jason R., Ritgen, Matthais, Böttcher, Sebastian, Langerak, Anton W., Roschewski, Mark, Wilson, Wyndham H., Gaidano, Gianluca, Rossi, Davide, Bahlo, Jasmin, Hallek, Michael, Tibshirani, Robert, Diehn, Maximilian, Alizadeh, Ash A.
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 25-07-2019
Subjects:	Algorithms Antineoplastic Agents - therapeutic use biomarkers Biomarkers, Tumor - blood Biomarkers, Tumor - metabolism Breast Neoplasms - drug therapy Breast Neoplasms - mortality Breast Neoplasms - pathology cancer Circulating Tumor DNA - blood Female Humans Kaplan-Meier Estimate liquid biopsy Lymphoma, Large B-Cell, Diffuse - drug therapy Lymphoma, Large B-Cell, Diffuse - mortality Lymphoma, Large B-Cell, Diffuse - pathology Neoadjuvant Therapy personalized medicine Precision Medicine predictive modeling Prognosis Progression-Free Survival Proportional Hazards Models Risk Assessment Treatment Outcome cancer biomarkers predictive modeling liquid biopsy personalized medicine
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Summary:	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. [Display omitted] •Current cancer biomarkers are obtained throughout a disease or treatment course•Prognostic biomarkers can be integrated over time similar to “win probability” models•Dynamic risk profiling produces a personal risk model and outperforms traditional methods•Dynamic risk profiling can potentially inform personalized therapy selection A framework for the integration of cancer-patient biomarker data over time improves prognostic accuracy and could inform personalized therapy selection.
Bibliography:	These authors contributed equally to this work. Conceptualization, D.M.K, M.S.E., F.S., M.D., and A.A.A. Methodology, D.M.K., M.S.E., A.M.N., and R.T. Investigation, D.M.K., M.S.E., F.S, J.S., and M.C.J.. Software, D.M.K, M.S.E., and C.L.L. Resources, U.D., A.H., O.C., J.R.W., M.R., S.B., A.W.L., M.R., W.H.W., G.G., D.R., J.B., and M.H. Writing - Original Draft, D.M.K. and M.S.E. Writing - Review & Editing, all authors. Current address: Department of Hematology, Oncology and Stem Cell Transplantation, Freiburg University Medical Center, Albert-Ludwigs-University, Freiburg, Germany Author Contributions
ISSN:	0092-8674 1097-4172
DOI:	10.1016/j.cell.2019.06.011