Design of combination therapeutics from protein response to drugs in ovarian cancer cells

  1. Department of Data Science, Dana-Farber Cancer Institute, Boston, United States
  2. Department of Cell Biology, Harvard Medical School, Boston, United States
  3. Broad Institute of Harvard and MIT, Cambridge, United States
  4. Proteomics Program, NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
  5. Pharmacology Department, Charles River Laboratories, Worcester, United States
  6. Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, United States
  7. Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
  8. Computational Biology Branch, National Library of Medicine and Developmental Therapeutics Branch, National Cancer Institute, Bethesda, United States

Peer review process

Not revised: This Reviewed Preprint includes the authors’ original preprint (without revision), an eLife assessment, and public reviews.

Read more about eLife’s peer review process.

Editors

  • Reviewing Editor
    Lynne-Marie Postovit
    Queens University, Kingston, Canada
  • Senior Editor
    Lynne-Marie Postovit
    Queens University, Kingston, Canada

Reviewer #1 (Public review):

Summary:

The authors provide a simple yet elegant approach to identifying therapeutic targets that synergize to prevent therapeutic resistance using cell lines, data-independent acquisition proteomics, and bioinformatic analysis. The authors identify several combinations of pharmaceuticals that were able to overcome or prevent therapeutic resistance in culture models of ovarian cancer, a disease with an unmet diagnostic and therapeutic need.

Strengths:

The manuscript utilizes state-of-the-art proteomic analysis, entailing data-independent acquisition methods, an approach that maximizes the robustness of identified proteins across cell lines. The authors focus their analysis on several drugs under development for the treatment of ovarian cancer and utilize straightforward thresholds for identifying proteomic adaptations across several drugs on the OVSAHO cell line. The authors utilized three independent and complementary approaches to predicting drug synergy (NetBox, GSEA, and Manual Curation). The drug combination with the most robust synergy across multiple cell lines was the inhibition of MEK and CDK4/6 using PD-0325901+Palbociclib, respectively. Additional combinations, including PARPi (rucaparib) and the fatty acid synthase inhibitor (TVB-2640). Collectively, this study provides important insight and exemplifies a solid approach to identifying drug synergy without large drug library screens.

Weaknesses:

The manuscript supports their findings by describing the biological function(s) of targets using referenced literature. While this is valuable, the number of downstream targets for each initial target is extensive, thus, the current work does not attempt to elucidate the mechanism of their drug synergy. Responses to drugs are quantified 72 hours after treatment and exclusively focused on cell viability and protein expression levels. The discovery phase of experimentation was solely performed on the OVSAHO cell line. An additional cell line(s) would increase the impact of how the authors went about identifying synergistic targets using bioinformatics. Ovarian cancer is elusive to treatment as primary cancer will form spheroids within ascites/peritoneal fluids in a state of pseudo-senescence to overcome environmental stress. The current manuscript is executed in 2D culture, which has been demonstrated to deviate from 3D, PDX, and primary tumours in terms of therapeutic resistance (DOI: 10.3390/cancers13164208). Collectively, the manuscript is insufficient in providing additional mechanistic insight beyond the literature, and its interpretation of data is limited to 2D culture until further validated.

Reviewer #2 (Public review):

Summary:

Franz and colleagues combined proteomics analysis of OVSAHO cell lines treated with 6 individual drugs. The quantitative proteomics data were then used for computational analysis to identify candidates/modules that could be used to predict combination treatments for specific drugs.

Strengths:

The authors present solid proteomics data and computational analysis to effectively repeat at the proteomics level analysis that have previously been done predominantly with transcriptional profiling. Since most drugs either target proteins and/or proteins are the functional units of cells, this makes intuitive sense.

Weaknesses:

Considering the available resources of the involved teams, performing the initial analysis in a single HGSC cell is certainly a weakness/limitation.

The data also shows how challenging it is to correctly predict drug combinations. In Table 2 (if I read it correctly), the majority of the drug combinations predicted for the initial cell line OVSAHO did not result in the predicted effect. It also shows how variable the response was in the different HGSC cell lines used for the combination treatment. The success rate will most likely continue to drop as more sophisticated models are being used (i.e., PDX). Human patients are even more challenging.

It would most likely be useful to more directly mention/discuss these caveats in the manuscript.

  1. Howard Hughes Medical Institute
  2. Wellcome Trust
  3. Max-Planck-Gesellschaft
  4. Knut and Alice Wallenberg Foundation