Early Prediction of Disease Progression in Small Cell Lung Cancer: Toward Model-Based Personalized Medicine in Oncology.
Predictive biomarkers can play a key role in individualized disease monitoring. Unfortunately, the use of biomarkers in clinical settings has thus far been limited. We have previously shown that mechanism-based pharmacokinetic/pharmacodynamic modeling enables integration of nonvalidated biomarker data to provide predictive model-based biomarkers for response classification. The biomarker model we developed incorporates an underlying latent variable (disease) representing (unobserved) tumor size dynamics, which is assumed to drive biomarker production and to be influenced by exposure to treatment. Here, we show that by integrating CT scan data, the population model can be expanded to include patient outcome. Moreover, we show that in conjunction with routine medical monitoring data, the population model can support accurate individual predictions of outcome. Our combined model predicts that a change in disease of 29.2% (relative standard error 20%) between two consecutive CT scans (i.e., 6-8 weeks) gives a probability of disease progression of 50%. We apply this framework to an external dataset containing biomarker data from 22 small cell lung cancer patients (four patients progressing during follow-up). Using only data up until the end of treatment (a total of 137 lactate dehydrogenase and 77 neuron-specific enolase observations), the statistical framework prospectively identified 75% of the individuals as having a predictable outcome in follow-up visits. This included two of the four patients who eventually progressed. In all identified individuals, the model-predicted outcomes matched the observed outcomes. This framework allows at risk patients to be identified early and therapeutic intervention/monitoring to be adjusted individually, which may improve overall patient survival.
Pharmacometrics & Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, IdiSNA Navarra Institute for Health Research, Pamplona, Spain.
Núria
N
Buil-Bruna
Clinical Pharmacology Modelling and Simulation, GlaxoSmithKline, London, United Kingdom.
Tarjinder
T
Sahota
Department of Medical Oncology, University Clinic of Navarra, University of Navarra, IdiSNA Navarra Institute for Health Research, Pamplona, Spain.
José-María
JM
López-Picazo
Department of Radiation Oncology, University Clinic of Navarra, University of Navarra, IdiSNA Navarra Institute for Health Research, Pamplona, Spain.
Marta
M
Moreno-Jiménez
Department of Medical Oncology, University Clinic of Navarra, University of Navarra, IdiSNA Navarra Institute for Health Research, Pamplona, Spain.
Salvador
S
Martín-Algarra
INRIA, Ecole Normale Supérieure de Lyon, Lyon, France.
Benjamin
B
Ribba
Pharmacometrics & Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, IdiSNA Navarra Institute for Health Research, Pamplona, Spain. itroconiz@unav.es.
Iñaki F
IF
Trocóniz
D000368:Aged; D014408:Biomarkers, Tumor / Q000032:analysis; D018450:Disease Progression; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis / Q000473:pathology* / Q000628:therapy; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015233:Models, Statistical; D057285:Precision Medicine / Q000379:methods*; D011237:Predictive Value of Tests; D055752:Small Cell Lung Carcinoma / Q000175:diagnosis / Q000473:pathology* / Q000628:therapy
D014408:Biomarkers, Tumor