Implementing systems modelling and molecular imaging to predict the efficacy of BCL-2 inhibition in colorectal cancer patient-derived xenograft models.
journal contribution
posted on 2020-10-23, 13:03 authored by Alice O'FarrellAlice O'Farrell, Monika Jarzabek, Andreas Ulrich Lindner, Steven Carberry, Emer Conroy, Ian Miller, Kate Connor, Liam ShielsLiam Shiels, Eugenia R. Zanella, Federico Lucantoni, Adam Lafferty, Kieron White, Mariangela Meyer Villamandos, Patrick DickerPatrick Dicker, William M Gallagher, Simon A. Keek, Sebastian Sanduleanu, Philippe Lambin, Henry C. Woodruff, Andrea Bertotti, Livio Trusolino, Annette ByrneAnnette Byrne, Jochen PrehnJochen PrehnResistance to chemotherapy often results from dysfunctional apoptosis, however multiple proteins with overlapping functions regulate this pathway. We sought to determine whether an extensively validated, deterministic apoptosis systems model, ‘DR_MOMP’, could be used as a stratification tool for the apoptosis sensitiser and BCL-2 antagonist, ABT-199 in patient-derived xenograft (PDX) models of colorectal cancer (CRC). Through quantitative profiling of BCL-2 family proteins, we identified two PDX models which were predicted by DR_MOMP to be sufficiently sensitive to 5-fluorouracil (5-FU)-based chemotherapy (CRC0344), or less responsive to chemotherapy but sensitised by ABT-199 (CRC0076). Treatment with ABT-199 significantly improved responses of CRC0076 PDXs to 5-FU-based chemotherapy, but showed no sensitisation in CRC0344 PDXs, as predicted from systems modelling. 18F-Fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG-PET/CT) scans were performed to investigate possible early biomarkers of response. In CRC0076, a significant post-treatment decrease in mean standard uptake value was indeed evident only in the combination treatment group. Radiomic CT feature analysis of pre-treatment images in CRC0076 and CRC0344 PDXs identified features which could phenotypically discriminate between models, but were not predictive of treatment responses. Collectively our data indicate that systems modelling may identify metastatic (m)CRC patients benefitting from ABT-199, and that 18F-FDG-PET could independently support such predictions.
Funding
A systems-based patient stratification tool of Bcl-2 family protein interactions to evaluate acute treatment responses in rectal cancer patients
Health Research Board
Find out more...BCL-2 family proteins and cellular bioenergetics in the control of cell survival: Towards novel predictive and prognostic markers for disease progression and therapy responses in colorectal cancer patients
Science Foundation Ireland
Find out more...Development of personalised medicine approaches for the clinical application of IAP antagonists in metastatic and high risk early stage colorectal cancer
Science Foundation Ireland
Find out more...ColoForetell: A Xenopatient Discovery Platform for the integrated Systems based Identification of Predictive Biomarkers for Targeted Therapies in Metastatic Colorectal Cancer
Science Foundation Ireland
Find out more...Advancing a Precision Medicine Paradigm in metastatic Colorectal Cancer: Systems based patient stratification solutions
European Commission
Find out more...EurOPDX Distributed Infrastructure for Research on patient-derived cancer Xenografts
European Commission
Find out more...Irish Cancer Society Collaborative Cancer Research Centre BREAST-PREDICT (CCRC13GAL)
Science Foundation Ireland Strategic Partnership Programme Precision Oncology Ireland (18/SPP/3522)
Science Foundation Ireland (18/RI/5759)
Science Foundation Ireland and European Regional Development Fund (ERDF) (13/RC/2073)
Dutch Cancer Society (KWF Kankerbestrijding) (12085/2018-2)
TRANSCAN Joint Transnational Call 2016 (JTC2016 "CLEARLY" no UM 2017-8295)
Optimal Management of Gender- Specific Cancers via Efficient Use of Protein Profiling, Digital Pathology and Systems Medicine Tools (OPTi- PREDICT)
Science Foundation Ireland
Find out more...History
Data Availability Statement
https://www.mdpi.com/2072-6694/12/10/2978/s1Comments
The original article is available at https://www.mdpi.comPublished Citation
O’Farrell AC, Jarzabek MA, Lindner AU, Carberry S, Conroy E. Miller IS, Connor K, Shiels L, Zanella ER, Lucantoni F, Lafferty A, White K, Meyer Villamandos M, Dicker P, Gallagher WM, Keek SA, Sanduleanu S, Lambin P, Woodruff HC, Bertotti A, Trusolino L, Byrne AT, Prehn JHM. Implementing systems modelling and molecular imaging to predict the efficacy of BCL-2 inhibition in colorectal cancer patient-derived xenograft models. Cancers 2020;12: 2978.Publication Date
14 October 2020External DOI
PubMed ID
33066609Department/Unit
- Physiology and Medical Physics
- Public Health and Epidemiology
Research Area
- Cancer
Publisher
MDPIVersion
- Published Version (Version of Record)
