Stochastic model of contact inhibition and the proliferation of melanoma in situ

Mauro Cesar C Morais, Izabella Stuhl, Alan U Sabino, Willian W Lautenschlager, Alexandre S Queiroga, Tharcisio C Tortelli, Roger Chammas, Yuri Suhov, Alexandre F Ramos

doi: https://doi.org/10.1101/110007

This article is a preprint and has not been peer-reviewed [what does this mean?].

• Abstract
• Info/History
• Metrics
• Supplementary material
•  Preview PDF

Abstract

Contact inhibition is a central feature orchestrating cell proliferation in culture experiments with its loss being associated with malignant transformation and tumorigenesis. We performed a co-culture experiment with human metastatic melanoma cell line (SK-MEL-147) and immortalized keratinocyte cells (HaCaT). After 8 days a spatial pattern was detected, characterized by the formation of clusters of melanoma cells surrounded by keratinocytes constraining their proliferation. In addition, we observed that the proportion of melanoma cells within the total population has increased. To explain our results we propose a spatial stochastic model (following a philosophy of the Widom-Rowlinson model from Statistical Physics and Molecular Chemistry) where we consider cell proliferation, death, migration, and cell-to-cell interaction through contact inhibition. Our numerical simulations demonstrate that loss of contact inhibition is a sufficient mechanism, appropriate for an explanation of the increase in the proportion of tumor cells and generation of spatial patterns established in conducted experiments.

http://biorxiv.org/content/early/2017/03/02/110007

A cautionary tale on using tumour growth rate to predict survival

 View ORCID ProfileHitesh Mistry, Fernando Ortega

doi: https://doi.org/10.1101/109934

This article is a preprint and has not been peer-reviewed [what does this mean?].

• Abstract
• Info/History
• Metrics
•  Preview PDF

Abstract

A 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.

http://biorxiv.org/content/early/2017/02/20/109934