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Annals of Oncology
1668P SYSTEMS BIOLOGY IN TRANSLATIONAL ONCOLOGY:
COMPUTATIONAL AND EXPERIMENTAL STUDY OF EGFR
AND IGF1R PATHWAYS IN NSCLC CELL LINES
F. Bianconi 1 , K. Perruccio 2 , V. Ludovini 3 , E. Baldelli 3 , G. Bellezza 4 , A. Flacco 2 ,
P. Valigi 5 , L. Crinò 3
1 Department of Surgical and Medical Specialties, and Public Health, University of
Perugia, Perugia, ITALY, 2 Medical Oncology Division, S. Maria della Misericordia
Hospital, Perugia, ITALY, 3 Medical Oncology, S. Maria della Misericordia
Hospital, Perugia, ITALY, 4 University of Perugia, Institute of Pathological Anatomy
and Histology, University of Perugia, Perugia, Italy, Perugia, ITALY, 5 Department
of Electronic and Information Engineering, University of Perugia, Perugia, ITALY
Background: The epidermal growth factor receptor (EGFR) and type 1
insulin-like growth factor receptor (IGF1R) pathways are complex networks
involving interactions between membrane-bound receptors, ligands, binding
proteins, downstream effectors and mutual interactions. In this study we
have designed a computational model of EGFR and IGF1R pathways in
NSCLC and we validated their dynamic responses in several tumor cell
lines.
Materials and methods: EGFR and IGF1R pathways and the downstream MAPK
and PIK3 networks have been modeled by means a mathematical model based on
ODE. A549, H1299, H1675 and H1650 tumor cell lines were stimulated by EGF,
IGF-I, EGF/IGF-I and without ligands. For these induction conditions we quantified
at 0, 2, 5, 10, 20, 30, 40, 50, 60 minutes by western blotting the following proteins:
phospho-AKT, AKT, phospho-EGFR, EGFR, phospho-p44/42 MAPK(ERK 1/2), p44/
42 MAPK(ERK1/2), phospho-IGF1R and IGF1R. Model simulations and experiment
data have been combined together.
Results: The time series performed in all cell lines under the four induction
conditions allowed us to characterize the signal transduction through EGFR and
IGF1R pathways. Our model has reproduced proteins behavior of A549, H1299,
H1675 and H1650 cell lines and also the robustness of the network has been
confirmed.
Conclusions: We propose a Systems Biology approach, combined with Translational
Oncology tolls, to understand the interaction between EGFR and IGF1R pathways in
NSCLC cell lines and validate model predictions. Future work will investigate the
effect of drugs action on cell lines.
Disclosure: All authors have declared no conflicts of interest.
1669P ONCO-I2B2 PROJECT: A BIOINFORMATICS TOOL
INTEGRATING –OMICS AND CLINICAL DATA TO SUPPORT
TRANSLATIONAL RESEARCH
A. Zambelli 1 , D. Segagni 2 , V. Tibollo 2 , A. Dagliati 2 , A. Malovini 2 , V. Fotia 1 ,
S. Manera 1 , R. Bellazzi 3
1 Oncology, Fondazione Salvatore Maugeri, Pavia, ITALY, 2 Information
Technology, Fondazione Salvatore Maugeri, Pavia, ITALY, 3 Industrial and
Information Engineering, University of Pavia, Pavia, ITALY
The ONCO-i2b2 project, supported by the University of Pavia and the Fondazione
Salvatore Maugeri (FSM), aims at supporting translational research in oncology and
exploits the software solutions implemented by the Informatics for Integrating
Biology and the Bedside (i2b2) research centre, an initiative funded by the NIH
Roadmap National Centres for Biomedical Computing. The ONCO-i2b2 software is
designed to integrate the i2b2 infrastructure with the FSM hospital information
system and the Bruno Boerci Biobank, in order to provide well-characterized cancer
specimens along with an accurate patients clinical data-base. The i2b2 infrastructure
provides a web-based access to all the electronic medical records of cancer patients,
and allow researchers analyzing the vast amount of biological and clinical
information, relying on a user-friendly interface. Data coming from multiple sources
are integrated and jointly queried.
In 2011 at AIOM Meeting we reported the preliminary experience of the
ONCO-i2b2 project, now we’re able to present the up and running platform and the
Table: 1670P
extended data set. Currently, more than 4400 specimens are stored and more than
600 of breast cancer patients give the consent for the use of specimens in the context
of clinical research, in addition, more than 5000 histological reports are stored in
order to integrate clinical data.
Within the ONCO-i2b2 project is possible to query and merge data regarding:
• Anonymous patient personal data;
• Diagnosis and therapy ICD9-CM subset from the hospital information system;
• Histological data (tumour SNOMED and TNM codes) and receptor profile testing
(Her2, Ki67) from anatomic pathology database;
• Specimen molecular characteristics (DNA, RNA, blood, plasma and cancer tissues)
from the Bruno Boerci biobank management system.
The research infrastructure will be completed by the development of new set of
components designed to enhance the ability of an i2b2 hive to utilize data generated
by NGS technology, providing a mechanism to apply custom genomic annotations.
The translational tool created at FSM is a concrete example regarding how the
integration of different information from heterogeneous sources could bring scientific
research closer to understand the nature of disease itself and to create novel
diagnostics through handy interfaces.
Disclosure: All authors have declared no conflicts of interest.
1670P EVALUATION OF ALTERNATE TUMOR METRICS AND
CUT-POINTS FOR RESPONSE CATEGORIZATION USING THE
RECIST 1.1 DATA WAREHOUSE
S.J. Mandrekar 1 ,M.An 2 , J. Meyers 1 , A. Grothey 3 , J. Bogaerts 4 , D.J. Sargent 1
1 Health Sciences Research, Mayo Clinic, Rochester, MN, UNITED STATES OF
AMERICA, 2 Mathematics, Vassar College, Poughkeepsie, UNITED STATES OF
AMERICA, 3 Medical Oncology, Mayo Clinic, Rochester, MN, UNITED STATES
OF AMERICA, 4 Statistics Dept, EORTC, Brussels, BELGIUM
Purpose: We previously showed that the trichotomized response metric (TriTR:
Complete or Partial Response (CR or PR) vs. Stable (SD) vs. Progression (PD)) had
better predictive ability than the Response Evaluation Criteria in Solid Tumors
(RECIST) best response (BR) metrics (CR/PR vs. others) (An et al., 2011). We
sought to test and validate TriTR, disease control rate (DCR: CR/PR/SD vs. PD) and
BR metrics utilizing alternate cut-points for PR and PD using the RECIST data
warehouse.
Methods: Data from 13 trials (5994 patients with breast, lung or colorectal
cancer) were randomly split (60:40) into training and test sets stratified by
tumor type, survival and progression status. 21 pairs of cut points for (PR,
PD) were considered: PR (20-50% decrease, by 5% increments) and PD
(10-20% increase, by 5% increments), where the pair (30%, 20%),
corresponds to the RECIST categorization. Cox proportional hazards models
with a flexible landmark analysis at 12- and 24-weeks post-baseline, adjusted
for baseline tumor burden, were used to assess the impact of the metrics
(using alternate cutoffs) on overall survival (OS). Model discrimination was
assessed using the concordance (c-) index [c = 0.5: no association; c = 1.0:
perfect association].
Results: Standard RECIST cutoffs demonstrated similar predictive ability as the
alternate (PR, PD) cutoffs for all metrics at both time points. Regardless of
tumor type, the last TriTR and DCR metrics (assessed at the landmark time
point) had higher (or similar) c-indices to BR, and best TriTR and DCR metrics
(assessed any time before the landmark time point). The 24-week metrics were
no better than those at 12-weeks. None of the metrics did particularly well for
breast cancer.
Conclusions: Alternative cut-points to RECIST standards provided no
meaningful improvement in OS prediction. TriTr and DCR metrics assessed after
12-weeks have good predictive performance. These results are currently being
validated.
Disclosure: All authors have declared no conflicts of interest.
C-Index at 12 and 24 weeks (Training Set)
Breast Colon Lung
Metric (based on RECIST cutoffs) 12 wks (N = 1048) 24 wks (N = 1060) 12 wks (N = 710) 24 wks (N = 727) 12 wks (N = 1816) 24 wks (N = 1819)
BR 0.57 0.58 0.61 0.64 0.62 0.62
Last TriTR 0.58 0.59 0.63 0.66 0.64 0.63
Best TriTR 0.58 0.58 0.62 0.64 0.63 0.62
Last DCR 0.58 0.58 0.62 0.65 0.61 0.61
Best DCR 0.57 0.56 0.59 0.59 0.62 0.59
Volume 23 | Supplement 9 | September 2012 doi:10.1093/annonc/mds417 | ix535