Is Topotecan Effective at Killing Cancer Cells?

In this paper, we analyse the behaviour of osteosarcoma cancer cells which are either exposed to the anticancer agent Topotecan or not exposed to any external agent. For the analyses of cell lineage data encoded from time lapse microscopy, we choose data mining tools that generate interpretable models of the data, and we address their statistical significance. We consider the mortality of unexposed cancer cells, the static and dynamic cytotoxic effects of the anticancer agent, the prediction of the clonal potential of resistant populations, and the differences between exposed and unexposed populations. We find that the anticancer agent affects the cells dynamics and events ratios i.e. (death/division, etc.) proportionately to its concentration, but it is ineffective at stopping the proliferation of the cancer at all dosages considered. In addition, we observe that cells exposed to the anticancer agent have greater displacements over time, indicating a putative relationship between cytotoxic effect and cell motility.

[1]  Guy Shani,et al.  Estimating false discovery rates for contingency tables , 2009 .

[2]  John Healey,et al.  Upfront window trial of topotecan in previously untreated children and adolescents with poor prognosis metastatic osteosarcoma , 2007, Cancer.

[3]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[4]  C. Bailly.  Topoisomerase I poisons and suppressors as anticancer drugs. , 2000, Current medicinal chemistry.

[5]  William W. Cohen.  Fast Effective Rule Induction , 1995, ICML.

[6]  Y. Pommier.  Topoisomerase I inhibitors: camptothecins and beyond , 2006, Nature Reviews Cancer.

[7]  M. Chappell,et al.  A NOVEL INTEGRATIVE BIOINFORMATICS ENVIRONMENT FOR ENCODING AND INTERROGATING TIMELAPSE MICROSCOPY IMAGES , 2006 .

[8]  G. Heppner.  Tumor heterogeneity. , 1984, Cancer research.

[9]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[10]  Lee Campbell,et al.  ProgeniDB: A Novel Cell Lineage Database for Generation Associated Phenotypic Behavior in Cell-based Assays , 2007, Cell cycle.

[11]  Y. Benjamini,et al.  THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY , 2001 .

[12]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[13]  J. Ross Quinlan,et al.  Improved Use of Continuous Attributes in C4.5 , 1996, J. Artif. Intell. Res..

[14]  Rachel J Errington,et al.  Cytomics and cellular informatics--coping with asymmetry and heterogeneity in biological systems. , 2009, Drug discovery today.

[15]  Usama M. Fayyad,et al.  Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning , 1993, IJCAI.

[16]  S. Fraser,et al.  Tracing the lineage of tracing cell lineages , 2001, Nature Cell Biology.

[17]  I. Campbell.  Chi‐squared and Fisher–Irwin tests of two‐by‐two tables with small sample recommendations , 2007, Statistics in medicine.

[18]  Robert A. Weinberg,et al.  Metastasis genes: A progression puzzle , 2002, Nature.

[19]  D. J. Clarke,et al.  DNA Topoisomerases , 2009, Methods in Molecular Biology™.