Dynamic Risk Profiling Using Serial Tumor Biomarkers for Personalized Outcome Prediction

David M Kurtz; Mohammad S Esfahani; Florian Scherer; Joanne Soo; Michael C Jin; Chih Long Liu; Aaron M Newman; Ulrich Dührsen; Andreas Hüttmann; Olivier Casasnovas; Jason R Westin; Matthais Ritgen; Sebastian Böttcher; Anton W Langerak; Mark Roschewski; Wyndham H Wilson; Gianluca Gaidano; Davide Rossi; Jasmin Bahlo; Michael Hallek; Robert Tibshirani; Maximilian Diehn; Ash A Alizadeh

doi:10.1016/j.cell.2019.06.011

Dynamic Risk Profiling Using Serial Tumor Biomarkers for Personalized Outcome Prediction

Cell. 2019 Jul 25;178(3):699-713.e19. doi: 10.1016/j.cell.2019.06.011. Epub 2019 Jul 4.

Authors

David M Kurtz¹, Mohammad S Esfahani², Florian Scherer², Joanne Soo², Michael C Jin², Chih Long Liu², Aaron M Newman³, Ulrich Dührsen⁴, Andreas Hüttmann⁴, Olivier Casasnovas⁵, Jason R Westin⁶, Matthais Ritgen⁷, Sebastian Böttcher⁸, Anton W Langerak⁹, Mark Roschewski¹⁰, Wyndham H Wilson¹⁰, Gianluca Gaidano¹¹, Davide Rossi¹², Jasmin Bahlo¹³, Michael Hallek¹⁴, Robert Tibshirani¹⁵, Maximilian Diehn¹⁶, Ash A Alizadeh¹⁷

Affiliations

¹ Division of Oncology, Department of Medicine, Stanford University, Stanford, CA, USA; Division of Hematology, Department of Medicine, Stanford University, Stanford, CA, USA; Department of Bioengineering, Stanford University, Stanford, CA, USA.
² Division of Oncology, Department of Medicine, Stanford University, Stanford, CA, USA.
³ Division of Oncology, Department of Medicine, Stanford University, Stanford, CA, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA.
⁴ Department of Hematology, University Hospital Essen, Essen, Germany.
⁵ Department of Hematology, Hopital F. Mitterrand, CHU Dijon and INSERM 1231, Dijon, France.
⁶ Department of Lymphoma/Myeloma, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
⁷ Department II of Internal Medicine, Campus Kiel, University of Schleswig-Holstein, Kiel, Germany.
⁸ Department III of Internal Medicine, University Hospital Rostock, Rostock, Germany.
⁹ Department of Immunology, Laboratory Medical Immunology, Erasmus MC, Rotterdam, the Netherlands.
¹⁰ Lymphoid Malignancies Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, USA.
¹¹ Division of Hematology, Department of Translational Medicine, University of Eastern Piedmont, Novara, Italy.
¹² Hematology, Oncology Institute of Southern Switzerland and Institute of Oncology Research, Bellinzona, Switzerland.
¹³ German CLL Study Group, Department I of Internal Medicine and Center of Integrated Oncology Cologne Bonn, University Hospital of Cologne, Cologne, Germany.
¹⁴ German CLL Study Group, Department I of Internal Medicine and Center of Integrated Oncology Cologne Bonn, University Hospital of Cologne, Cologne, Germany; Cologne Cluster of Excellence on Cellular Stress Responses in Aging-Related Diseases (CECAD), University of Cologne, Cologne, Germany.
¹⁵ Department Statistics, Stanford University, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
¹⁶ Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA; Stanford Cancer Institute, Stanford University, Stanford, CA, USA; Department of Radiation Oncology, Stanford University, Stanford, CA, USA. Electronic address: diehn@stanford.edu.
¹⁷ Division of Oncology, Department of Medicine, Stanford University, Stanford, CA, USA; Division of Hematology, Department of Medicine, Stanford University, Stanford, CA, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA; Stanford Cancer Institute, Stanford University, Stanford, CA, USA. Electronic address: arasha@stanford.eduSummary.

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.

Keywords: biomarkers; cancer; liquid biopsy; personalized medicine; predictive modeling.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Antineoplastic Agents / therapeutic use
Biomarkers, Tumor / blood
Biomarkers, Tumor / metabolism*
Breast Neoplasms / drug therapy
Breast Neoplasms / mortality
Breast Neoplasms / pathology*
Circulating Tumor DNA / blood
Female
Humans
Kaplan-Meier Estimate
Lymphoma, Large B-Cell, Diffuse / drug therapy
Lymphoma, Large B-Cell, Diffuse / mortality
Lymphoma, Large B-Cell, Diffuse / pathology*
Neoadjuvant Therapy
Precision Medicine*
Prognosis
Progression-Free Survival
Proportional Hazards Models
Risk Assessment
Treatment Outcome

Substances

Antineoplastic Agents
Biomarkers, Tumor
Circulating Tumor DNA

Abstract

Publication types

MeSH terms

Substances

Grants and funding