Dynamic Rendering of the Heterogeneous Cell Response to Anticancer Treatments

Abstract

The antiproliferative response to anticancer treatment is the result of concurrent responses in all cell cycle phases, extending over several cell generations, whose complexity is not captured by current methods. In the proposed experimental/computational approach, the contemporary use of time-lapse live cell microscopy and flow cytometric data supported the computer rendering of the proliferative process through the cell cycle and subsequent generations during/after treatment. The effects of treatments were modelled with modules describing the functional activity of the main pathways causing arrest, repair and cell death in each phase. A framework modelling environment was created, enabling us to apply different types of modules in each phase and test models at the complexity level justified by the available data. We challenged the method with time-course measures taken in parallel with flow cytometry and time-lapse live cell microscopy in X-ray-treated human ovarian cancer cells, spanning a wide range of doses. The most suitable model of the treatment, including the dose-response of each effect, was progressively built, combining modules with a rational strategy and fitting simultaneously all data of different doses and platforms. The final model gave for the first time the complete rendering in silico of the cycling process following X-ray exposure, providing separate and quantitative measures of the dose-dependence of G1, S and G2M checkpoint activities in subsequent generations, reconciling known effects of ionizing radiations and new insights in a unique scenario.

Integrated experimental and simulation study of the response to sequential treatment with erlotinib and gemcitabine in pancreatic cancer

“…We look into the complexity of the antiproliferative response to treatment using different techniques (principally FC and TL microscopy) and decoding the components of the response by simulation. This experimental/computational approach, able to quantify the activity of the checkpoints involved in the treatment, has been already applied in studies of the effects induced by cisplatin [ 13 ], taxol [ 14 ], topotecan [ 15 ], doxorubicin [ 17 ], melphalan [ 16 ], trabectedin [ 33 ] and radiation [ 20 ].…”

“…We believe that deeper knowledge, not only of the molecular processes but also of cell proliferation in response to treatment challenges, is vital to optimize the treatment schedules. The approach we propose has already been used to decode the dose- and time-responses to different anticancer agents [ 12 – 20 ]. It allows a detailed analysis of the dynamics of cell proliferation, reconstructing in silico the fluxes of the cells in the cycle, while interacting with the checkpoints in G 1 , S and G 2 M phases, and separating cytostatic from cytotoxic effects.…”

Overexpression of cysteine cathepsin L is a marker of invasion and metastasis in ovarian cancer

“…Since ovarian cancer tissue is highly heterogeneous, multiple biopsies are necessary for careful examination (23,24). This means that the quantitation of cathepsins in biological fluids from ovarian cancer patients has several clinical advantages over measurements from ovarian cancer tissue.…”

Modeling Cytostatic and Cytotoxic Responses to New Treatment Regimens for Ovarian Cancer

“…Proliferation after the start of treatment was modeled by introducing perturbative parameters into the basal model. We adapted the modular modeling framework developed for analysis of proliferation in vitro, maintaining the wide choice of options to simulate in detail different cytotoxic and cytostatic effects and their time courses (16). Several types of perturbations were preliminarily tested, eventually converging on a four-parameter model, trading between the need to provide separate information on the different phenomena in play while avoiding overparameterization.…”

“…Integration of data with mathematical modeling of the relevant processes in principle would allow to extract the full information conveyed by each growth curve and to frame the comparisons between treatment groups on a common and objective ground. Moving in that direction, in the present study, we reproduced the growth curves of individual tumors, in a wide preclinical trial, in terms of a computer model rendering the proliferation process, exploiting methods previously developed in our laboratory (12)(13)(14)(15)(16) to extract a few parameters measuring separately the main drug effects. The approach provided a new insight into the response to single and combined treatments and allowed an indepth comparison of the different treatment options, contributing to the controversial issues on the use of dose-dense versus conventional PTX treatment with the addition of an angiogenesis inhibitor.…”