in silico oncology: building and validating oncosimulators ... · 8/29/2019 · crucial clinical...
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G.S. Stamatakos - Lecture at the Korea Institute of Science and Technology 1
Georgios S. Stamatakos
In Silico Oncology and In Silico Medicine Group,
Institute of Communication and Computer Systems,
School of Electrical and Computer Engineering,
National Technical University of Athens, Greece
& Medical School, University of Saarland, Germany
https://www.in-silico-oncology.iccs.ntua.gr/
29 Aug. 2019
In Silico Oncology:
Building and Validating Oncosimulators and Oncosimulator Based
Hypermodels as Clinical Decision Support Systems
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Acknowledgements
• Prof. Norbert Graf is greatly acknowledged for the clinical drive, the clinical positioning, the provision of
crucial clinical data and the clinical overview of the work concerning nephroblastoma modelling, an
excellent Oncosimulator development paradigm.
• All my collaborators at the In Silico Oncology & In Silico Medicine Group, (ISO&ISM_G) ICCS, SECE,
NTUA are greatly acknowledged for their enthusiasm, commitment and hard and efficient work. Special
thanks are due to : Dr D. Dionysiou, Dr V. Antipas, Dr E. Kolokotroni, Dr E. Georgiadi, Dr S. Giatili, Dr E.
Ouzounoglou, Ms K. Argyri, Mr N. Christodoulou, Mr C. Antonopoulos, Mr C. Kyroudis, and Mr N.
Tousert.
• Prof. Uzunoglu is duly acknowledged for his crucial encouragement and support during the initial steps
of the endeavour.
• All partners of the 17 organizations who participated in the European Commission (EC) funded EC-US
project CHIC as well all partners involved in the Oncosimulator & Hypermodelling development and
their clinical adaptation and validation for the past 22 years are greatly acknowledged for their
important contributions.
• All partners involved in the Oncosimulator development and validation of the European Commission
funded projects ACGT, ContraCancrum, TUMOR, p-medicine, Dr Tharapat, MyHealthAvatar are duly
acknowledged.
• All external collaborators of ISO&ISM_G since 1997 are duly acknowledged.
• The European Commission, the Greek and the German States are duly acknowledged for their crucial
financial support
29 Aug. 2019 G.S. Stamatakos - Lecture at the Korea Institute of Science and
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The CHIC Project at a
glance
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The CHIC Project at a glance
• The large scale EU-US integrating research project CHIC has been entitled:
“CHIC: Developing meta- and hyper-multiscale models and repositories for in silico oncology”
• Website: http://www.chic-vph.eu/ • Funded by the European Commission with a grant of
10,582,000 €.
• Seventeen academic, research and industrial partner organizations across Europe and US participated in CHIC.
• The CHIC project underwent its final review on 23 and 24 May 2017 and was assessed as "Excellent" by the Board of (five) External Reviewers appointed the European Commission.
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The CHIC Project at a glance (cont.)
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The CHIC Project at a glance (cont.)
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The CHIC Project at a glance (cont.)
• THE CHIC PROJECT COORDINATION
SCHEME
• Overall and Scientific Coordinator: Research
Professor G. Stamatakos, ICCS-National
Technical University of Athens, Greece
• Assistant Clinical Coordinator: Professor
Norbert Graf, University Hospital of Saarland,
Germany
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Structure of the presentation
• A brief outline of the purpose, methods and
results
• Examples from the methods and the results
• Conclusions
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A brief outline of the purpose,
methods and results
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Purpose of CHIC
• to develop, clinically adapt and partly clinically
validate meta- and hyper-multiscale models and
repositories for in silico oncology
• to develop advanced technological cloud based
infrastructures supporting the process of
hypermodel development and the clinical
translation of hypermodels
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Methods
• A host of clinical, experimental, mathematical, computational and software engineering strategies, methods and techniques have been devised and/or utilized in order to both develop and test multiscale hypermodels.
• A hypermodel is a complex mathematical and computational model consisting of more than one elementary component model.
• Each component model or “hypomodel” simulates a crucial biological mechanism of tumour growth and response to treatment.
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Methods (cont.)
• Hypomodels are connected together in
several ways dictated by the current biological
and clinical knowledge.
• Both mechanistic and machine learning
based hypermodels have been developed
driven by clinically relevant questions
formulated by the clinical partners of the
CHIC consortium.
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Methods (cont.)
• The overarching idea of the project was to exploit the accumulated quantitative experimental and clinical knowledge concerning several spatiotemporal scales of cancer biocomplexity in order to produce treatment response predictions as precise as possible based on the patient’s individual multiscale data (e.g. – imaging
– Histological
– molecular,
– Clinical
data
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Methods (cont.)
• To this end several candidate treatment schemes can be simulated using detailed hypermodels fed with the actual multiscale data of the patient.
• The treatment scheme performing best in silico will serve as the optimal suggestion to the clinician to consider for their final treatment strategy decision.
• Most hypomodels or component models have been developed by different leading cancer modelling groups participating in the CHIC project scattered across EU and US.
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Methods (cont.)
• A clinician friendly technological platform for hypermodel creation and execution (CRAF) has also been developed and successfully tested.
• Four paradigmatic cancer types have been considered:
– nephroblastoma,
– non small cell lung cancer
– glioblastoma (treated with immunotharepy in conjunction with radiotherapy and chemotherapy)
– prostate cancer.
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Results
• Both the hypermodels and the technological platforms developed by CHIC have been documented, disseminated and demonstrated in real time and in detail to the appointed independent scientific evaluators of the European Commission.
• The overall project outcome has been finally assessed as Excellent and worth further translational development and multifaceted exploitation.
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Examples from the methods
and the results
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Dimensions of cancer manifestation and treatment 29 Aug. 2019
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The Oncosimulator: a functional diagram
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Basic architecture of a cancer
multimodeller hypermodel
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Mathematics hidden behind each
constituent hypomodel
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Nephroblastoma
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Nephroblastoma
(Part of the whole table of diagrams / nephroblastoma )
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Nephroblastoma
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Multiscale Cancer Modelling Paradigms
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The Wilms Tumour Branch of the
Oncosimulator
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Chemotherapy treatment protocol. The simulated Wilms Tumour preoperative
chemotherapy treatment protocol of the SIOP/ GPOH clinical trial.
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• Wilms Tumour Oncosimulator: – Tumor Free Growth - Tumor Chemotherapy
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Cytokinetic Model for
Free Tumour Growth
• stem cells: cells assumed to possess unlimited proliferative potential
• limp cells: progenitor cells with limited proliferative potential
• diff cells: terminally differentiated cells
Cell Local reoxygenation Local reoxygenation
Cell Disappearance
Apoptosis
Spontaneous apoptosis
Necrosis
disappearance
G0 G1 S G2 M G
G0 M G2 S G1
Asymmetric division
DIFF STEM LIMP
Symmetric division
Spontaneous apoptosis
After n mitoses
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Cytokinetic Model Treatment Response
• When cells are hit by chemo (treatment session) they enter a separate cell cycle at
which they remain till they are led to apoptotic death from a point of the cell
cycle specified by the mechanism of action of the drug (in the case of Epirubicin S
phase is considered to be that point).
chemo
G1hit Shit G2hit Mhit
Cell disappearance
G0hit
A (Apoptosis incl.
time delay)
Spontaneous apoptosis
N (Necrosis)
Cell
disappearance
G0 G1 S G2 M G0
chemo
Mhit G2hit Shit G1hit
M G2 S G1
G0hit
Asymmetric Division
DIFF
STEM LIMP
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Clinical Adaptation and Validation
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Successful Clinical Model Adaptation
• Case 1: [1]
Highly malignant, blastemal type of tumor
Time evolution of tumor volume and selected tumor
subpopulations. Panel A: Time evolution of tumor volume
for the four virtual scenarios of Table 1. Panels Bi and Bii,
Ci and Cii, Di and Dii, Ei and Eii: Evolution over time of
selected subpopulations of the tumors.The
chemotherapeutic scheme of Figure 2 has been
simulated. The drug administration instants are: day 3, day
10, day 17, day 24. Day 0: first MRI data set. Day 28:
second MRI data set.
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Adaptation of Several Clinical Cases
No Patient Histology Risk
1 11570 Mixed Intermediate
2 11590 Mixed with focal anaplasia Intermediate
3 11627 Mixed Intermediate
4 11628 Stromal Intermediate
5 11639 Regressive Intermediate
6 11803 Stromal Intermediate
7 11813 Mixed Intermediate
8 11537 Stromal Intermediate
9 11613 Regressive Intermediate
10 11616 Stromal Intermediate
11 11714 Mixed Intermediate
12 11733 Blastemal High
13 11736 Mixed Intermediate
14 11788 Regressive Intermediate
15 11813 Mixed Intermediate
16 11823 Regressive Intermediate
17 11845 Diffuse Anaplasia High
18 11862 Epithelial Intermediate
19 11873 Mixed Intermediate
20 11881 Regressive Intermediate
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Imaging & Clinical Data
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Nephroblastoma Bilateral Case
Imaging Data
1st Imaging Set 2nd Imaging Set 3rd Imaging Set
R L R R L L
DVR=90%
DVL=89%
DVR=94%
DVL=95%
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Nephroblastoma Case Clinical Data
Histopathological data
• Nephroblastomatosis consists primarily of blastemal
cells which are actively cycling.
• Therefore the initial tumour is made up mainly of stem
and LIMP (progenitor) cells and fewer differentiated and
dead cells.
•Post-surgery histological data also indicated that the
remaining viable tumour was of blastemal type.
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Simulation Results
Time evolution of bilateral tumour volume and selected tumour subpopulations. A: Time evolution of tumour volume for
the right and left kidney under the two scenarios of table 1. B, C, D, E: Evolution over time of the proliferating, dormant,
differentiated and dead population percentage of the bilateral tumor (respectively). Where: R: Right, L:Left, TT: Typical
tumour, CT: clinical tumour.
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Simulated Clinical Tumors
1st I.S.
2nd
I.S.
3rd I.S.
Right Kidney Left Kidney
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AN EXAMPLE OF USING THE GBM
RADIOTHERAPY ONCOSIMULATOR TWO RTOG STUDY 83-02 BRANCHES SIMULATED
• 1) AHF-48Gy:
accelerated hyperfractionation, 48Gy total dose,
(1.6Gy twice daily to a total dose of 48 Gy)
• 2) HF-81.6Gy:
hyperfractionation, 81.6Gy total dose.(1.2Gy twice
daily to a total dose of 81.6Gy)
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An MRI slice depicting a glioblastoma mutiforme. Both the clinical volume of the tumour and its central necrotic area have been delineated. The present case has been considered for the preliminary checks of the simulation model. [G.Stamatakos, D.Dionysiou, E.Zacharaki, N.Mouravliansky, K.Nikita, and N.Uzunoglu, "In Silico Radiation Oncology: Combining Novel Simulation Algorithms with Current Visualization Techniques,'' Proc. IEEE, Special Issue on "Bioinformatics: Advances and Challenges" Vol.90, No.11, November 2002, pp.1764-1777]
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Spatial Discretization
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Mesh Initialization
NBC
Equivalence Classes
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of Science and Technology
Cytokinetic Model
Cell disappearance
G1 G2 S
M
G0 N A
RI-MAD
RI-MND
SA or RI-ID
The probabilities of the alternative “death paths” due to irradiation depend
primarily on the type of tumour cell.
• In GBM the vast majority of cells undergoes a mitotic necrotic death. SA: Spontaneous Apoptosis, RI-ID: Radiation-Induced Interphase Death, RI-MAD: Radiation-
Induced Mitotic Apoptotic Death, RI-MND: Radiation-Induced Mitotic Necrotic Death
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Simplified flow chart for the response of a single tumour cell to irradiation. Symbol explanation: αP and βP stand for the α and β parameters of the linear quadratic model for the tumour proliferating cells excluding those in phase S. The subscript S denotes cells in the DNA synthesis phase, whereas the subscript G0 denotes cells in the resting (dormant) phase G0.
YES NO
Irradiation (αP,βP ) (αS,βS )
Cell still cycling for a few
(e.g. 3) cell cycles
Cell lysis/apoptosis
PROLIFERATING CELL
Irradiation (αG0,βG0 )
G0- CELL
NO
Has oxygen and nutrient
supply become
adequate?
LQ cell hit
Cell disappearance Tumor shrinkage
Cell death products are diffused
LQ cell survival LQ cell hit
Cell is gradually
disintegrating
LQ cell survival
YES
Is oxygen and nutrient
supply still adequate?
[from G.Stamatakos, D.Dionysiou, E.Zacharaki, N.Mouravliansky, K.Nikita, and N.Uzunoglu, "In Silico Radiation Oncology: Combining Novel Simulation Algorithms with Current Visualization Techniques,'' Proc. IEEE, Special Issue on "Bioinformatics: Advances and Challenges" Vol.90, No.11, November 2002, pp.1764-1777]
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and Technology 45
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Irradiation according to the standard fractionation scheme (2 Gy once a day, 5 days per week, 60 Gy in total). Left panel: three dimensional sections of the tumour shown in the right panel: (a) before the beginning of irradiation, (b) 1 fictitious day after the beginning of irradiation, (c) 2 fictitious days after the beginning of irradiation and (d) 3 fictitious days after the beginning of irradiation. Colour code red: proliferating cell layer, green: dormant cell layer (G0), blue: dead cell layer. The colouring criterion “99.8%” used to visualize the predictions has been defined as follows. “For a geometrical cell of the discretizing mesh, if the percentage of dead cells is lower than 99.8% then { if percentage of proliferating cells > percentage of G0 cells, then paint the geometrical cell red (proliferating cell layer), else paint the geometrical cell green (G0 cell layer) } else paint the geometrical cell blue (dead cell layer)” The values of certain parameters (e.g. cell loss) have been deliberately exaggerated in order to facilitate the demonstration of the ability of the model to simulate the shrinkage effect. [see G.Stamatakos, D.Dionysiou, E.Zacharaki, N.Mouravliansky, K.Nikita, and N.Uzunoglu, "In Silico Radiation Oncology: Combining Novel Simulation Algorithms with Current Visualization Techniques,'' Proc. IEEE, Special Issue on "Bioinformatics: Advances and Challenges" Vol.90, No.11, November 2002, pp.1764-1777]
(a)
(b)
(c)
(d)
29 Aug. 2019 G.S. Stamatakos - Lecture at the Korea Institute of Science and
Technology 46
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Technology
Simulation predictions of the number of total tumour cells (mt p53 and wild type p53) for the standard fractionation scheme. An OER=3.0 has been assumed.
[see V. P Antipas, G. S Stamatakos, N. K Uzunoglu, D. D Dionysiou, R. G Dale, ” A spatio-temporal simulation model of the response of solid tumours to radiotherapy in vivo: parametric validation concerning oxygen enhancement ratio and cell cycle duration,” Phys. Med. Biol. 49 (2004) 1485–1504 [Pubmed Link: http://www.ncbi.nlm.nih.gov/entrez /query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=15152687&query_hl=14] ]
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TWO RTOG STUDY 83-02
BRANCHES SIMULATED
• 1) AHF-48Gy:
accelerated hyperfractionation, 48Gy total dose,
(1.6Gy twice daily to a total dose of 48 Gy)
• 2) HF-81.6Gy:
hyperfractionation, 81.6Gy total dose.(1.2Gy twice
daily to a total dose of 81.6Gy)
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29 Aug. 2019 49 G.S. Stamatakos - Lecture at the Korea Institute of Science and Technology
Number of surviving tumour cells as a function of time for a glioblastoma
tumour with mutant p53 gene. AHF-48Gy: accelerated hyperfractionation,
48Gy total dose, HF-81.6Gy: hyperfractionation, 81.6Gy total dose.
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4D (3D + time)
visualization
AHF-48Gy HF-81.6Gy
GBM with mutant p53
1.0E+00
1.0E+01
1.0E+02
1.0E+03
1.0E+04
1.0E+05
1.0E+06
1.0E+07
1.0E+08
1.0E+09
1.0E+10
1.0E+11
0 1 2 3 4 5 6 7 8
Time (weeks)
Nu
mb
er
of
alive t
um
ou
r cells ...
AHF- 48Gy
HF- 81.6Gy
G.S. Stamatakos - Lecture at the Korea Institute of Science and Technology
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Interactive 2D sampling planes
AHF-48Gy HF-81.6Gy
G.S. Stamatakos - Lecture at the Korea Institute of Science and Technology
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CHIC SURVEY ON HYPERMODELS
• CHIC is running a survey, where patients, physicians and citizens can learn about hypermodels and can give their opinion on the usefulness of such models.
• Your feedback will help us to optimize our research results.
• The survey is available at http://www.chic-vph.eu/ Latest Highlights
or directly at http://chic-vph.eu/highlights/details/article/chic-online-survey-on-hypermodels/
• A video demonstrating the future use of hypemodels is also included in the survey
• Responsible: Prof. Norbert Graf, University Hospital of Saarland
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CHAPTER 18
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65 29 Aug. 2019
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Conclusions
• Based on the partial validation results and analyses that have been reported in CHIC, the highly innovative CHIC hypermodels and Oncosimulators appear to possess a great potential for serving as clinical decision support systems (CDS) and/or cores of future in silico trial platforms.
• However, additional retrospective validation work for the developed hypermodels and Oncosimulators is needed in order to more fully substantiate and support their “candidacy” for undergoing validation through prospective clinical trials.
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Conclusions ( cont.)
• This is a necessary step in order to definitely assess both their clinical validity and clinical value.
• Further retrospective validation work will be carried out by specific former CHIC partners on a bilateral or small partner group basis.
• Regarding the eventual prospective clinical validation of the hypermodels, certain exploratory steps have already been taken, including focused discussions within the framework of the International Society for Pediatric Oncology (SIOP).
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The BOUNCE Project
• In the context of exploitation, it is noted that
several approaches, processes, models and
tools developed in the framework of the
CHIC project have already been recruited for
the implementation needs of the EU funded
project BOUNCE under the title: “Predicting
Effective Adaptation to Breast Cancer to Help
Women to BOUNCE Back” (Grant
Agreement 777167)
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Thank you