Early Prediction of Disease Progression in Small Cell Lung Cancer: Toward Model-Based Personalized Medicine in Oncology

Abstract

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. Cancer Res; 75(12); 2416-25. Ó2015 AACR.

Establishing the Quantitative Relationship Between Lanreotide Autogel®, Chromogranin A, and Progression-Free Survival in Patients with Nonfunctioning Gastroenteropancreatic Neuroendocrine Tumors

“…Certainly, there are several recent examples where mathematical models have been used to describe the time course of tumor markers and their link with clinical outcomes in different cancer indications. Some include human chorionic gonadotropin as an early predictor of methotrexate resistance in low-risk gestational trophoblastic neoplasia patients (43), mathematical models to personalize vaccination regimens to stabilize prostate-specific antigen (PSA) levels (42,44), soluble VEGF receptor 3 to monitor adverse events and clinical response in patients with imatinib-resistant gastrointestinal stromal tumors (45,46), a semimechanistic model involving lactate dehydrogenase (LDH) and neuron-specific enolase (NSE) dynamics to individualize disease monitoring in small cell lung cancer patients (27,47), and CA-125 as an early predictive biomarker of recurrent ovarian cancer (48).…”

Neoadjuvant therapy for locally advanced gastric cancer patients. A population pharmacodynamic modeling

“…Mechanistic PK/PD models can describe individual variability in population level trends [23]. These models also provide an individual prediction using Bayesian methodology [11]. A mechanistic model in locally advanced GC able to accurately predict response to neoadjuvant therapy would be of interest in these patients.…”

“…Although genomic landscape of GC has been defined recently, integration of both genotype and phenotype remains an unmet need for GC patients. The use of mechanistic PK/PD models enables the identification of important covariates that determine response with the aim of personalizing treatment [11]. In this setting, there is an increasing interest in using PK/PD models as Bayesian priors, with the observed patient PD endpoint and covariate information being used to construct a posterior set of most likely individual model parameters to be used to predict or adjust future treatment [34].…”

“…This association may not be detected by a simple assessment of correlation. The paradigm has been used previously in other types of tumors such as prostate and small cell lung cancer [10, 11]. Another potential application of this modeling approach is the identification of ‘at risk-patients’, in order to tailor therapeutic strategies on an individual basis [11].…”

Blackboard to Bedside: A Mathematical Modeling Bottom-Up Approach Toward Personalized Cancer Treatments

“…Because of the high variability of cancer displayed across disease incidences, previous research indicates that tumor prognoses and treatment responses may correlate more with molecular tumor specifics than with larger-scale factors, such as anatomic tumor origin 1 or metrics quantified on a patient or population level. Recent advances in biomarker handling, 13,14 biopsy techniques, and medical imaging enable tumor assessment 15 before and throughout treatments regimes. However, current biopsy procedures may in certain cases be infeasible to perform, and furthermore, tumors are highly evolutive systems that may rapidly and drastically change after a biopsy.…”