Author response:
The following is the authors’ response to the original reviews.
Public Reviews:
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.
We appreciate your positive remarks on the use of NetBox, GSEA, and human curation for predicting anti-resistance effects of second drugs. Regarding the weaknesses you identified:
Mechanistic Insight: We agree that our current work interprets findings using prior published knowledge and does not attempt to infer detailed mechanisms of drug resistance of the nominated drug combinations. Our primary goal with this study was to establish a robust, unbiased proteomic and computational pipeline for proposing anti-resistance drug combinations, rather than to fully characterize the downstream molecular effects for each combination or to prove causation. To get closer to mechanistic insight, meaning detailed hypotheses of causative interactions, one would need to investigate anti-resistance effects in other pre-clinical materials as a crucial next step for the most promising combinations identified. This was out of scope for us. We assume the proposed combinations are useful for focussed follow-up in the community.
Discovery Phase on a Single Cell Line: Our discovery phase was focused solely on the OVSAHO cell line due to its resemblance to surgical ovarian cancer samples. Including additional cell lines in the initial proteomic-response discovery phase plausibly would have enhanced the generalizability. But this was not done due to resource constraints. However, we did perform more extensive validation of the effect of drug combinations on proliferation in several cell lines to explore broader applicability.
2D Culture Limitations: We are fully aware of the limitations of 2D cell culture models, especially in the context of ovarian cancer, where in clinical reality interactions with the microenvironment and other effects can have significant roles in therapeutic resistance. Adn we recognize that in lab experiments 2D culture does not fully recapitulate the complexities of 3D tumors, PDX models, or primary patient tumors. We have added citations to the relevant literature (including the reference you provided), and have emphasized in the Discussion that our findings serve as a strong foundation for future experimental tests (validation) in more physiologically relevant experimental model systems.
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.
Thank you for your summary and positive comments. Regarding the weaknesses you identified:
Initial Analysis in a Single Cell Line: We concur with your assessment that performing the initial analysis in a single HGSC cell line (OVSAHO) is a limitation. As mentioned in our response to Reviewer #1, resource limitations caused this decision, and we acknowledge that a broader initial screen would have strengthened generalizability. We added this limitation in the discussion section, emphasizing use of diverse cell lines in the initial protein response profiling as an area for future work.
Challenges in Predicting Drug Combinations and Variability: We thank the observation regarding the challenges in predicting the effect of drug combinations and the variability of antiproliferative effects observed in different HGSC cell lines (Table 2). As with any predictive method, our computational-experimental pipeline is not guaranteed to identify with absolute certainty additive or synergistic interactions, but generates data-informed hypotheses to be considered in the presence of other available observations. We now emphasize in the Discussion that while our computational pipeline provides plausible anti-resistance candidates, the precise results (extent of additivity or synergy) differ in different cell lines. This underscores that experimental validation across diverse physiological models, such as PDXs or organoids (not just additional cell lines) is an essential criterion of validity of the generated hypotheses. And we underscore the (obvious) challenge of the ultimate translation of pre-clinical experiments to therapeutic effects in humans.
In revision, we have clarified in detail the expectation of predicted synergy implied by the reviewer’s comment, “the majority of the drug combinations predicted for the initial cell line OVSAHO did not result in the predicted effect”. This reflects a misunderstanding of our goals. The predictions are for drug effects that are anti-resistant, such that the proteomic response to one drug is counteracted by the second drug. The predicted effect is not synergy. Indeed, useful anti-resistance effect does not require synergy - additivity is sufficient: if cells are resistant to the original drug, the second drug plausibly still has antiproliferative effect, as it targets the cellular processes that are increased in activity (upregulated) in response to the first drug. So we deleted the red synergy color in Table 2 to avoid the potential conclusion from our results that without synergy, there is no benefit to a drug combination. In fact, additive drug combination effects are in themselves beneficial. For clarity on this point, added coloring in Table 2 to highlight the small number of combinations that did not work well in that the combination was clearly antagonistic, using a combination index CI >= 2.0 cutoff; we clarify this point in the Discussion.
Recommendations for the authors:
Reviewer #1 (Recommendations for the authors):
(1) Figure 2b. This figure would be more impactful if presented as an upset plot with the same Venn diagram embedded. I am not sure Figure 2C accurately supports the statement : "Frequently affected proteins generally had expression level changes in the same direction across all drug perturbations (Figure 2c), indicating a potential general stress response. ". It would be beneficial if the authors could present the data in a way that shows the number of genes with similar directional groupings. Likewise, the color scheme for this figure is hard to interpret as grey is the most negative value and values are preselected for absolute fold-change. Please consider colors with a stronger contrast.
Authors should consider uploading MS files to the PRIDE or MASSIVE repository.
We have addressed these very useful suggestions. We have edited Figure 2b to include the requested upset plot. It serves to illustrate the intersection of proteins responding to different perturbation conditions; due to figure space constraints, we limit the figure to entries with counts of at least 15. We have added the number of proteins with consistent directional changes in the figure 2c caption and the text.
For Figure 2c, we have edited the color bar legend to better reflect the colors that appear in the heatmap.
We have added our mass-spectrometry drug-response dataset to the ProteomeXchange Consortium via PRIDE with accession number PXD066316.
