Dataset Information

A New Method to Model and Predict Progression Free Survival Based on Tumor Growth Dynamics.

ABSTRACT: Progression-free survival (PFS) has been increasingly used as a primary endpoint for early clinical development. The aim of the present work was to develop a model where target lesion dynamics and risk for nontarget progression are jointly modeled for predicting PFS. The model was developed based on a pooled platinum-resistant ovarian cancer dataset comprising four different treatments and a wide range of dose levels. The target lesion progression was derived from tumor growth dynamics based on the Response Evaluation Criteria in Solid Tumors (RECIST) criteria. The nontarget progression hazard was correlated to the first derivative of target lesion tumor size with respect to time. The PFS time was determined by the first occurring event, target lesion progression, or nontarget progression. The final joint model not only captured target lesion tumor growth dynamics but also predicted PFS well. A similar approach can potentially be used to predict PFS in future oncology studies.

SUBMITTER: Yu J

PROVIDER: S-EPMC7080535 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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A New Method to Model and Predict Progression Free Survival Based on Tumor Growth Dynamics.

Yu Jiajie J Wang Nina N Kågedal Matts M

CPT: pharmacometrics & systems pharmacology 20200312 3

Progression-free survival (PFS) has been increasingly used as a primary endpoint for early clinical development. The aim of the present work was to develop a model where target lesion dynamics and risk for nontarget progression are jointly modeled for predicting PFS. The model was developed based on a pooled platinum-resistant ovarian cancer dataset comprising four different treatments and a wide range of dose levels. The target lesion progression was derived from tumor growth dynamics based on ...[more]

PMID: 32036626

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Project description:BackgroundRecent studies have shown patient-derived tumor organoids can predict the drug response of patients with cancer. However, the prognostic value of patient-derived tumor organoid-based drug tests in predicting the progression-free survival of patients with stage IV colorectal cancer after surgery remains unknown.ObjectiveThis study aimed to explore the prognostic value of patient-derived tumor organoid-based drug tests in patients with stage IV colorectal cancer after surgery.DesignRetrospective cohort study.SettingsSurgical samples were obtained from patients with stage IV colorectal cancer at the Nanfang Hospital.PatientsA total of 108 patients who underwent surgery with successful patient-derived tumor organoid culture and drug testing were recruited between June 2018 and June 2019.InterventionsPatient-derived tumor organoid culture and chemotherapeutic drug testing.Main outcomes measuresProgression-free survival.ResultsAccording to the patient-derived tumor organoid-based drug test, 38 patients were drug sensitive and 76 patients were drug resistant. The median progression-free survival was 16.0 months in the drug-sensitive group and 9.0 months in the drug resistant group ( p < 0.001). Multivariate analyses showed that drug resistance (HR, 3.38; 95% CI, 1.84-6.21; p < 0.001), right-sided colon (HR, 3.50; 95% CI, 1.71-7.15; p < 0.001), mucinous adenocarcinoma (HR, 2.47; 95% CI, 1.34-4.55; p = 0.004), and non-R0 resection (HR, 2.70; 95% CI, 1.61-4.54; p < 0.001) were independent predictors of progression-free survival. The new patient-derived tumor organoid-based drug test model, which includes the patient-derived tumor organoid-based drug test, primary tumor location, histological type, and R0 resection, was more accurate than the traditional clinicopathological model in predicting progression-free survival ( p = 0.001).LimitationsA single-center cohort study.ConclusionsPatient-derived tumor organoids can predict progression-free survival in patients with stage IV colorectal cancer after surgery. Patient-derived tumor organoid drug resistance is associated with shorter progression-free survival, and the addition of patient-derived tumor organoid drug tests to existing clinicopathological models improves the ability to predict progression-free survival.

Project description:Prognostic biomarkers that can reliably predict early disease progression of non-small cell lung cancer (NSCLC) are needed for identifying those patients at high risk for progression, who may benefit from more intensive treatment. In this work, we aimed to identify an imaging signature for predicting progression-free survival (PFS) of locally advanced NSCLC. Methods: This retrospective study included 82 patients with stage III NSCLC treated with definitive chemoradiotherapy for whom both baseline and mid-treatment PET/CT scans were performed. They were randomly placed into two groups: training cohort (n=41) and testing cohort (n=41). All primary tumors and involved lymph nodes were delineated. Forty-five quantitative imaging features were extracted to characterize the tumors and involved nodes at baseline and mid-treatment as well as differences between two scans performed at these two points. An imaging signature was developed to predict PFS by fitting an L1-regularized Cox regression model. Results: The final imaging signature consisted of three imaging features: the baseline tumor volume, the baseline maximum distance between involved nodes, and the change in maximum distance between the primary tumor and involved nodes measured at two time points. According to multivariate analysis, the imaging model was an independent prognostic factor for PFS in both the training (hazard ratio [HR], 1.14, 95% confidence interval [CI], 1.04-1.24; P = 0.003), and testing (HR, 1.21, 95% CI, 1.10-1.33; P = 0.048) cohorts. The imaging signature stratified patients into low- and high-risk groups, with 2-year PFS rates of 61.9% and 33.2%, respectively (P = 0.004 [log-rank test]; HR, 4.13, 95% CI, 1.42-11.70) in the training cohort, as well as 43.8% and 22.6%, respectively (P = 0.006 [log-rank test]; HR, 3.45, 95% CI, 1.35-8.83) in the testing cohort. In both cohorts, the imaging signature significantly outperformed conventional imaging metrics, including tumor volume and SUVmax value (C-indices: 0.77-0.79 for imaging signature, and 0.53-0.73 for conventional metrics). Conclusions: Evaluation of early treatment response by combining primary tumor and nodal imaging characteristics may improve the prediction of PFS of locally advanced NSCLC patients.

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Dataset Information

A New Method to Model and Predict Progression Free Survival Based on Tumor Growth Dynamics.

Publications

A New Method to Model and Predict Progression Free Survival Based on Tumor Growth Dynamics.

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