Implementing Systems Modelling and Molecular Imaging to Predict the Efficacy of BCL-2 Inhibition in Colorectal Cancer Patient-Derived Xenograft Models

Alice C. O'Farrell; Monika A. Jarzabek; Andreas U. Lindner; Steven Carberry; Emer Conroy; Ian S. Miller; Kate Connor; Liam Shiels; Eugenia R. Zanella; Federico Lucantoni; Adam Lafferty; Kieron White; Mariangela Meyer Villamandos; Patrick Dicker; William M. Gallagher; Simon A. Keek; Sebastian Sanduleanu; Philippe Lambin; Henry C. Woodruff; Andrea Bertotti; Livio Trusolino; Annette T. Byrne; Jochen H. M. Prehn

doi:10.3390/cancers12102978

Implementing Systems Modelling and Molecular Imaging to Predict the Efficacy of BCL-2 Inhibition in Colorectal Cancer Patient-Derived Xenograft Models

Alice C. O'Farrell, Monika A. Jarzabek, Andreas U. Lindner, Steven Carberry, Emer Conroy, Ian S. Miller, Kate Connor, Liam Shiels, Eugenia R. Zanella, Federico Lucantoni, Adam Lafferty, Kieron White, Mariangela Meyer Villamandos, Patrick Dicker, William M. Gallagher, Simon A. Keek, Sebastian Sanduleanu, Philippe Lambin, Henry C. Woodruff, Andrea BertottiLivio Trusolino, Annette T. Byrne, Jochen H. M. Prehn^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Simple Summary

Drugs that sensitise tumours to chemotherapy by enhancing cell death signalling are of significant clinical interest. However, it is challenging to determine which colorectal cancer patients may benefit from such sensitisers. The ability to predict this would be advantageous. Here we show that protein profiling combined with mathematical modelling identifies responsive tumours. Using our modelling method, we predicted the effect of adding a sensitizer drug to chemotherapy in two patient-derived colorectal tumours. We grew the tumours in mice, treated animals with these drugs and performed PET/CT imaging. The predicted "sensitive" tumours were smaller when the sensitising drug was added to chemotherapy whilst it did not further reduce tumour size in "non-sensitive" tumours, thus validating our prediction. PET imaging also supported our predictions. CT analysis (radiomics) revealed features that distinguished the two tumours. This was the first application of radiomic analyses to PDX derived CT data.

Resistance 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. F-18-Fluorodeoxyglucose positron emission tomography/computed tomography (F-18-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 F-18-FDG-PET could independently support such predictions.

Original language	English
Article number	2978
Number of pages	19
Journal	Cancers
Volume	12
Issue number	10
DOIs	https://doi.org/10.3390/cancers12102978
Publication status	Published - Oct 2020

Keywords

ABT-199
Venetoclax
colorectal cancer
BCL-2
FOLFOX
PDX
preclinical imaging
radiomics
systems biology
deterministic modelling
CELL-DEATH
COLON-CANCER
RADIOMICS
APOPTOSIS
BCL-X(L)
MITOCHONDRIA
RESISTANCE
RESPONSES
PROTEINS
GROWTH

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O'Farrell, A. C., Jarzabek, M. A., Lindner, A. U., Carberry, S., Conroy, E., Miller, I. S., Connor, K., Shiels, L., Zanella, E. R., Lucantoni, F., Lafferty, A., White, K., Meyer Villamandos, M., Dicker, P., Gallagher, W. M., Keek, S. A., Sanduleanu, S., Lambin, P., Woodruff, H. C., ... Prehn, J. H. M. (2020). Implementing Systems Modelling and Molecular Imaging to Predict the Efficacy of BCL-2 Inhibition in Colorectal Cancer Patient-Derived Xenograft Models. Cancers, 12(10), Article 2978. https://doi.org/10.3390/cancers12102978

@article{b5f902b2b3bd479a81cc970ad316a4f6,

title = "Implementing Systems Modelling and Molecular Imaging to Predict the Efficacy of BCL-2 Inhibition in Colorectal Cancer Patient-Derived Xenograft Models",

abstract = "Simple SummaryDrugs that sensitise tumours to chemotherapy by enhancing cell death signalling are of significant clinical interest. However, it is challenging to determine which colorectal cancer patients may benefit from such sensitisers. The ability to predict this would be advantageous. Here we show that protein profiling combined with mathematical modelling identifies responsive tumours. Using our modelling method, we predicted the effect of adding a sensitizer drug to chemotherapy in two patient-derived colorectal tumours. We grew the tumours in mice, treated animals with these drugs and performed PET/CT imaging. The predicted {"}sensitive{"} tumours were smaller when the sensitising drug was added to chemotherapy whilst it did not further reduce tumour size in {"}non-sensitive{"} tumours, thus validating our prediction. PET imaging also supported our predictions. CT analysis (radiomics) revealed features that distinguished the two tumours. This was the first application of radiomic analyses to PDX derived CT data.Resistance 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. F-18-Fluorodeoxyglucose positron emission tomography/computed tomography (F-18-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 F-18-FDG-PET could independently support such predictions.",

keywords = "ABT-199, Venetoclax, colorectal cancer, BCL-2, FOLFOX, PDX, preclinical imaging, radiomics, systems biology, deterministic modelling, CELL-DEATH, COLON-CANCER, RADIOMICS, APOPTOSIS, BCL-X(L), MITOCHONDRIA, RESISTANCE, RESPONSES, PROTEINS, GROWTH",

author = "O'Farrell, {Alice C.} and Jarzabek, {Monika A.} and Lindner, {Andreas U.} and Steven Carberry and Emer Conroy and Miller, {Ian S.} and Kate Connor and Liam Shiels and Zanella, {Eugenia R.} and Federico Lucantoni and Adam Lafferty and Kieron White and {Meyer Villamandos}, Mariangela and Patrick Dicker and Gallagher, {William M.} and Keek, {Simon A.} and Sebastian Sanduleanu and Philippe Lambin and Woodruff, {Henry C.} and Andrea Bertotti and Livio Trusolino and Byrne, {Annette T.} and Prehn, {Jochen H. M.}",

note = "Funding Information: Funding: This research was supported by a grant from the Health Research Board, Ireland (HRA-POR-2014-547) to J.H.M.P. and A.T.B., and by Investigator grants from Science Foundation Ireland (13/IA/1881; 14/IA/2582) to J.H.M.P. A.T.B. received funding from Science Foundation Ireland (SFI) Career Development Award {\textquoteleft}Coloforetell{\textquoteright} (grant agreement number: 13/CD?/2183). L.T., A.T.B. and J.H.M.P. are further supported by the European Union{\textquoteright}s Horizon 2020 Health Research and Innovation award {\textquoteleft}Colossus{\textquoteright} (grant agreement number: 754923). A.T.B., I.S.M., L.T. and A.B. are members of the EurOPDX Consortium, and receive funding from the European Union{\textquoteright}s Horizon 2020 research and innovation programme, grant agreement no. #731105 (EurOPDX Research Infrastructure, www.europdx.eu). W.M.G. and E.C. are supported by the Irish Cancer Society Collaborative Cancer Research Centre BREAST-PREDICT (CCRC13GAL), the Science Foundation Ireland Investigator Programme OPTi-PREDICT (grant code 15/IA/3104) and the Science Foundation Ireland Strategic Partnership Programme Precision Oncology Ireland (grant code 18/SPP/3522). A.T.B., I.S.M., W.M.G. and E.C. are further supported by, and this publication has emanated from, research supported in part by RCSI, UCD and NUIG pursuant to a grant from Science Foundation Ireland (SFI) under grant number 18/RI/5759 and a grant from Science Foundation Ireland (SFI) and the European Regional Development Fund (ERDF) under grant number 13/RC/2073.” H.C.W. is supported by the Dutch Cancer Society (KWF Kankerbestrijding), Project number 12085/2018–2 and TR?NSC?N Joint Transnational Call 2016 (JTC2016 “CLE?RLY”-no UM 2017–8295). Funding Information: We thank AbbVie (Dublin, Ireland) for providing ABT-199 and M2i Limited (Dublin, Ireland) for supplying [18F]FDG. We thank members of Livio Trusolino?s group (IRCCS, University of Torino) who provided PDX models. Further, we thank our colleagues from the Centre for Cancer Research and Cell Biology (Queen?s University Belfast) for providing DLD-1 and HCT-116 cells used in this study. We thank Orna Bacon (Beaumont Hospital, Dublin) for providing FOLFOX. Publisher Copyright: {\textcopyright} 2020 by the authors. Licensee MDPI, Basel, Switzerland.",

year = "2020",

month = oct,

doi = "10.3390/cancers12102978",

language = "English",

volume = "12",

journal = "Cancers",

issn = "2072-6694",

publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",

number = "10",

}

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 2020, 'Implementing Systems Modelling and Molecular Imaging to Predict the Efficacy of BCL-2 Inhibition in Colorectal Cancer Patient-Derived Xenograft Models', Cancers, vol. 12, no. 10, 2978. https://doi.org/10.3390/cancers12102978

TY - JOUR

T1 - Implementing Systems Modelling and Molecular Imaging to Predict the Efficacy of BCL-2 Inhibition in Colorectal Cancer Patient-Derived Xenograft Models

AU - O'Farrell, Alice C.

AU - Jarzabek, Monika A.

AU - Lindner, Andreas U.

AU - Carberry, Steven

AU - Conroy, Emer

AU - Miller, Ian S.

AU - Connor, Kate

AU - Shiels, Liam

AU - Zanella, Eugenia R.

AU - Lucantoni, Federico

AU - Lafferty, Adam

AU - White, Kieron

AU - Meyer Villamandos, Mariangela

AU - Dicker, Patrick

AU - Gallagher, William M.

AU - Keek, Simon A.

AU - Sanduleanu, Sebastian

AU - Lambin, Philippe

AU - Woodruff, Henry C.

AU - Bertotti, Andrea

AU - Trusolino, Livio

AU - Byrne, Annette T.

AU - Prehn, Jochen H. M.

N1 - Funding Information: Funding: This research was supported by a grant from the Health Research Board, Ireland (HRA-POR-2014-547) to J.H.M.P. and A.T.B., and by Investigator grants from Science Foundation Ireland (13/IA/1881; 14/IA/2582) to J.H.M.P. A.T.B. received funding from Science Foundation Ireland (SFI) Career Development Award ‘Coloforetell’ (grant agreement number: 13/CD?/2183). L.T., A.T.B. and J.H.M.P. are further supported by the European Union’s Horizon 2020 Health Research and Innovation award ‘Colossus’ (grant agreement number: 754923). A.T.B., I.S.M., L.T. and A.B. are members of the EurOPDX Consortium, and receive funding from the European Union’s Horizon 2020 research and innovation programme, grant agreement no. #731105 (EurOPDX Research Infrastructure, www.europdx.eu). W.M.G. and E.C. are supported by the Irish Cancer Society Collaborative Cancer Research Centre BREAST-PREDICT (CCRC13GAL), the Science Foundation Ireland Investigator Programme OPTi-PREDICT (grant code 15/IA/3104) and the Science Foundation Ireland Strategic Partnership Programme Precision Oncology Ireland (grant code 18/SPP/3522). A.T.B., I.S.M., W.M.G. and E.C. are further supported by, and this publication has emanated from, research supported in part by RCSI, UCD and NUIG pursuant to a grant from Science Foundation Ireland (SFI) under grant number 18/RI/5759 and a grant from Science Foundation Ireland (SFI) and the European Regional Development Fund (ERDF) under grant number 13/RC/2073.” H.C.W. is supported by the Dutch Cancer Society (KWF Kankerbestrijding), Project number 12085/2018–2 and TR?NSC?N Joint Transnational Call 2016 (JTC2016 “CLE?RLY”-no UM 2017–8295). Funding Information: We thank AbbVie (Dublin, Ireland) for providing ABT-199 and M2i Limited (Dublin, Ireland) for supplying [18F]FDG. We thank members of Livio Trusolino?s group (IRCCS, University of Torino) who provided PDX models. Further, we thank our colleagues from the Centre for Cancer Research and Cell Biology (Queen?s University Belfast) for providing DLD-1 and HCT-116 cells used in this study. We thank Orna Bacon (Beaumont Hospital, Dublin) for providing FOLFOX. Publisher Copyright: © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2020/10

Y1 - 2020/10

N2 - Simple SummaryDrugs that sensitise tumours to chemotherapy by enhancing cell death signalling are of significant clinical interest. However, it is challenging to determine which colorectal cancer patients may benefit from such sensitisers. The ability to predict this would be advantageous. Here we show that protein profiling combined with mathematical modelling identifies responsive tumours. Using our modelling method, we predicted the effect of adding a sensitizer drug to chemotherapy in two patient-derived colorectal tumours. We grew the tumours in mice, treated animals with these drugs and performed PET/CT imaging. The predicted "sensitive" tumours were smaller when the sensitising drug was added to chemotherapy whilst it did not further reduce tumour size in "non-sensitive" tumours, thus validating our prediction. PET imaging also supported our predictions. CT analysis (radiomics) revealed features that distinguished the two tumours. This was the first application of radiomic analyses to PDX derived CT data.Resistance 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. F-18-Fluorodeoxyglucose positron emission tomography/computed tomography (F-18-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 F-18-FDG-PET could independently support such predictions.

AB - Simple SummaryDrugs that sensitise tumours to chemotherapy by enhancing cell death signalling are of significant clinical interest. However, it is challenging to determine which colorectal cancer patients may benefit from such sensitisers. The ability to predict this would be advantageous. Here we show that protein profiling combined with mathematical modelling identifies responsive tumours. Using our modelling method, we predicted the effect of adding a sensitizer drug to chemotherapy in two patient-derived colorectal tumours. We grew the tumours in mice, treated animals with these drugs and performed PET/CT imaging. The predicted "sensitive" tumours were smaller when the sensitising drug was added to chemotherapy whilst it did not further reduce tumour size in "non-sensitive" tumours, thus validating our prediction. PET imaging also supported our predictions. CT analysis (radiomics) revealed features that distinguished the two tumours. This was the first application of radiomic analyses to PDX derived CT data.Resistance 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. F-18-Fluorodeoxyglucose positron emission tomography/computed tomography (F-18-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 F-18-FDG-PET could independently support such predictions.

KW - ABT-199

KW - Venetoclax

KW - colorectal cancer

KW - BCL-2

KW - FOLFOX

KW - PDX

KW - preclinical imaging

KW - radiomics

KW - systems biology

KW - deterministic modelling

KW - CELL-DEATH

KW - COLON-CANCER

KW - RADIOMICS

KW - APOPTOSIS

KW - BCL-X(L)

KW - MITOCHONDRIA

KW - RESISTANCE

KW - RESPONSES

KW - PROTEINS

KW - GROWTH

U2 - 10.3390/cancers12102978

DO - 10.3390/cancers12102978

M3 - Article

C2 - 33066609

SN - 2072-6694

VL - 12

JO - Cancers

JF - Cancers

IS - 10

M1 - 2978

ER -

Implementing Systems Modelling and Molecular Imaging to Predict the Efficacy of BCL-2 Inhibition in Colorectal Cancer Patient-Derived Xenograft Models

Abstract

Keywords

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