54 research outputs found
On the reporting and analysis of a cancer evolutionary adaptive dosing trial
No full textThere is growing interest in combining expertise from different academic fields when it comes to treating cancer. The combination of mathematics and cancer has led to the birth of mathematical oncology. In this short article the reporting of results of the first clinical mathematical oncology trial exploring a novel dosing algorithm based on evolutionary principles is discussed. In particular the lack of details on patient characteristics, acknowledgement of known confounders and improper comparisons to historical controls are discussed.
The study by Zhang et al.1 reports on the initial results of the first cancer clinical trial employing an evolutionary based adaptive dosing algorithm within the clinic. The study was conducted using the drug Abiraterone within the metastatic castration resistant prostate cancer (mCRPC) setting. The initial results were based on 11 patients who had their treatment stopped and started based on a simple rule relating to Prostate Specific Antigen (PSA) falls and rises as follows. Patients had to have experienced a PSA fall to be eligible for the study, once a >50% fall was seen, patients had their treatment stopped and re-started once their PSA levels reached pre-treatment levels. The final conclusion from their initial analysis was that “The outcomes show significant improvement over published studies and a contemporaneous population.” This conclusion was based on comparing their results against a 16 patient contemporaneous cohort and a clinical trial cohort of 546 patients. This short article will highlight key issues with these comparisons which can be used to dispute the key conclusion from the paper.
The authors compare the radiological progression times of their 11 patients on an adaptive dosing schedule with 16 patients who have received continuous therapy. However, the authors do not provide any information on key prognostic factors of Abiraterone for either their 11 patients or the 16 patients they compare to. In fact, there is no information on the patient characteristics of the 16 contemporaneous cohort. There are numerous prognostic factors for Abiraterone, lactate dehydrogenase, alkaline phosphatase, hemoglobin to name but a few, which were released in the statistical review held at the FDA. Therefore, without information on these prognostic factors the authors cannot exclude that the difference in progression times was not attributable to differences in prognostic factors between the two cohorts.
The second comparison made by the authors is with the results of a Phase III clinical trial.2 In the authors study patients were only eligible for the study if they experienced a PSA fall >50%. However, this was not the case in the trial they compare their results too where patients recruited had not yet had any doses of Abiraterone. Therefore, the comparison made by the authors is incorrect as the two cohorts of patients are not necessarily the same.
In summary, the points raised within this short article highlight that the evidence for adaptive therapy being superior to continuous therapy still does not exist. By highlighting the flaws in reporting and comparisons it is hoped the community raises its standards for future trials of this type, especially when making comparisons with historical data.
References
1. Zhang, J., Cunningham, J. J., Brown, J. S. & Gatenby, R. A. Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer. Nature Communications 8, 1–9 (2017).
2. Ryan, C. J. et al. Abiraterone acetate plus prednisone versus placebo plus prednisone in chemotherapy-naive men with metastatic castration-resistant prostate cancer (COU-AA-302): final overall survival analysis of a randomised, double-blind, placebo-controlled phase 3 study. Lancet Oncol. 16, 152–160 (2015)
On the relationship between tumour growth rate and survival in non-small cell lung cancer
No full textA recurrent question within oncology drug development is predicting phase III outcome for a new treatment using early clinical data. One approach to tackle this problem has been to derive metrics from mathematical models that describe tumour size dynamics termed re-growth rate and time to tumour re-growth. They have shown to be strong predictors of overall survival in numerous studies but there is debate about how these metrics are derived and if they are more predictive than empirical end-points. This work explores the issues raised in using model-derived metric as predictors for survival analyses. Re-growth rate and time to tumour re-growth were calculated for three large clinical studies by forward and reverse alignment. The latter involves re-aligning patients to their time of progression. Hence, it accounts for the time taken to estimate re-growth rate and time to tumour re-growth but also assesses if these predictors correlate to survival from the time of progression. I found that neither re-growth rate nor time to tumour re-growth correlated to survival using reverse alignment. This suggests that the dynamics of tumours up until disease progression has no relationship to survival post progression. For prediction of a phase III trial I found the metrics performed no better than empirical end-points. These results highlight that care must be taken when relating dynamics of tumour imaging to survival and that bench-marking new approaches to existing ones is essential
Complex versus simple models: ion-channel cardiac toxicity prediction
Full text linkThere is growing interest in applying detailed mathematical models of the heart for ion-channel related cardiac toxicity prediction. However, a debate as to whether such complex models are required exists. Here an assessment in the predictive performance between two established large-scale biophysical cardiac models and a simple linear model Bnet was conducted. Three ion-channel data-sets were extracted from literature. Each compound was designated a cardiac risk category using two different classification schemes based on information within CredibleMeds. The predictive performance of each model within each data-set for each classification scheme was assessed via a leave-one-out cross validation. Overall the Bnet model performed equally as well as the leading cardiac models in two of the data-sets and outperformed both cardiac models on the latest. These results highlight the importance of benchmarking complex versus simple models but also encourage the development of simple models
Complex versus simple models: ion-channel cardiac toxicity prediction
No full textAbstractThere is growing interest in applying detailed mathematical models of the heart for ion-channel related cardiac toxicity prediction. However, a debate as to whether such complex models are required exists. Here an assessment in the predictive performance between two established cardiac models, gold-standard and cardiac safety simulator, and a simple linear model Bnet was conducted. Three ion-channel data-sets were extracted from literature. Each compound was designated a cardiac risk category based on information within CredibleMeds. The predictive performance of each model within each data-set was assessed via a leave-one-out cross validation. In two of the data-sets Bnet performed equally as well as the leading cardiac model, cardiac safety simulator, both of these outperformed the gold-standard model. In the 3rd data-set, which contained the most detailed ion-channel pharmacology, Bnet outperformed both cardiac models. These results highlight the importance of benchmarking models but also encourage the development of simple models.</jats:p
On the relationship between tumour growth rate and survival in non-small cell lung cancer
No full textAbstractA recurrent question within oncology drug development is predicting phase III outcome for a new treatment using early clinical data. One approach to tackle this problem has been to derive metrics from mathematical models that describe tumour size dynamics termed re-growth rate and time to tumour re-growth. They have shown to be strong predictors of overall survival in numerous studies but there is debate about how these metrics are derived and if they are more predictive than empirical end-points. This work explores the issues raised in using model-derived metric as predictors for survival analyses. Re-growth rate and time to tumour re-growth were calculated for three large clinical studies by forward and reverse alignment. The latter involves re-aligning patients to their time of progression. Hence it accounts for the time taken to estimate re-growth rate and time to tumour re-growth but also assesses if these predictors correlate to survival from the time of progression. We found that neither re-growth rate nor time to tumour re-growth correlated to survival using reverse alignment. This suggests that the dynamics of tumours up until disease progression has no relationship to survival post progression. For prediction of a phase III trial we found the metrics performed no better than empirical end-points. These results highlight that care must be taken when relating dynamics of tumour imaging to survival and that bench-marking new approaches to existing ones is essential.</jats:p
Simulating RTOG 0617 using Genomic Adjusted Radiation Dose:Comparing Apples With Oranges
No full text
A simple model of a growing tumour
No full textThis paper presents the CellCycler, a model of a growing tumour which aims to simulate and predict the effect of treatment on xenograft studies or in the clinic. The model, which is freely available as a web application, uses ordinary differential equations (ODEs) to simulate cells as they pass through the phases of the cell cycle. However the guiding philosophy of the model is that it should only use parameters that can be observed or reasonably well approximated. There is no representation of the complex internal dynamics of each cell; instead the level of analysis is limited to cell state observables such as cell phase, apoptosis, and damage. We show that this approach, while limited in many respects, still naturally accounts for a heteregenous cell population with varying doubling time, and closely captures the dynamics of a growing tumour as it is exposed to treatment. The program is demonstrated using three case studies
- …
