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 journalArticleAcademicpeer-review

1 Citation (Web of Science)

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 languageEnglish
Article number2978
Number of pages19
JournalCancers
Volume12
Issue number10
DOIs
Publication statusPublished - 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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