Identifying targetable metabolic dependencies across colorectal cancer progression

  1. Danny N. Legge
  2. Ewelina Stanko
  3. Amy K. Holt
  4. Caroline J. Bull
  5. Tracey J. Collard
  6. Madhu Kollareddy
  7. Jake Bellamy
  8. Sarah Groves
  9. Eric H. Ma
  10. Emma Hazelwood
  11. David Qualtrough
  12. Borko Amulic
  13. Karim Malik
  14. Ann C. Williams
  15. Nicholas Jones
  16. Emma E. Vincent

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Abstract

Colorectal cancer (CRC) is a multi-stage process initiated through the formation of a benign adenoma, progressing to an invasive carcinoma and finally metastatic spread. Tumour cells must adapt their metabolism to support the energetic and biosynthetic demands associated with disease progression. As such, targeting cancer cell metabolism is a promising therapeutic avenue in CRC. However, to identify tractable nodes of metabolic vulnerability specific to CRC stage, we must understand how metabolism changes during CRC development. Here, we use a unique model system – comprising human early adenoma to late adenocarcinoma. We show that adenoma cells transition to elevated glycolysis at the early stages of tumour progression but maintain oxidative metabolism. Progressed adenocarcinoma cells rely more on glutamine-derived carbon to fuel the TCA cycle, whereas glycolysis and TCA cycle activity remain tightly coupled in early adenoma cells. Adenocarcinoma cells are more flexible with respect to fuel source, enabling them to proliferate in nutrient-poor environments. Despite this plasticity, we identify asparagine (ASN) synthesis as a node of metabolic vulnerability in late-stage adenocarcinoma cells. We show that loss of asparagine synthetase (ASNS) blocks their proliferation, whereas early adenoma cells are largely resistant to ASN deprivation. Mechanistically, we show that late-stage adenocarcinoma cells are dependent on ASNS to support mTORC1 signalling and maximal glycolytic and oxidative capacity. Resistance to ASNS loss in early adenoma cells is likely due to a feedback loop, absent in late-stage cells, allowing them to sense and regulate ASN levels and supplement ASN by autophagy. Together, our study defines metabolic changes during CRC development and highlights ASN synthesis as a targetable metabolic vulnerability in later stage disease.

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  1. Version published to 10.1101/2022.03.23.485483v5 on bioRxiv
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    Reply to the reviewers

    We thank for reviewers for their feedback and were pleased they think that the manuscript is “of great interest to the scientific community”. The reviewers agree that the manuscript addresses an important question and that the identification of ASNS as a potential vulnerability of late-stage colorectal cancer is significant. The reviewers agree that our findings would be substantially strengthened by validation in state-of-the-art organoid model systems. We agree with this and are currently liaising with collaborators (Owen Sansom, Beatson Institute and Laura Thomas, Swansea University) to replicate our findings in both mouse and human colorectal organoid models. We will determine the sensitivity of colorectal organoid models to ASNS inhibition across a range of tumorigenicities and mutational profiles representing different stages of the adenoma-carcinoma progression. We believe these experiments will substantially strengthen the manuscript and lend weight to our finding that late-stage adenocarcinoma cells are vulnerable to ASNS inhibition.

    This is the predominant concern across reviewers, we are confident we can address this and all other, relatively minor, concerns as detailed below.

    Please find below a point-by-point reply to the reviewer’s comments. Reviewer comments are in italicized text and our responses follow.

    Reviewer #1

    • All of the findings in this manuscript are limited to in vitro observations, we know that most of the in vitro findings can not be translated in vivo. The manuscript would significantly benefit from in vivo experiments using the cells described in Fig.1 A and confirming the in vitro findings.*

    We agree that validation of our results in a more physiological context would significantly elevate our manuscript. In order to address this, we intend to use both human and mouse colorectal organoid models (please see detailed description of this in response to reviewer 2). We have decided to take this approach rather than conduct in vivoexperiments using the AA series (C1, SB, 10C and M) for two main reasons. Firstly, the C1 and SB cell lines do not form tumours in mice, consistent with them representing early colorectal adenoma cells. As such, we are not able to use the entire series in *in vivo *experiments. Secondly, we are keen to demonstrate replication of our findings in an alternative model. An organoid model would offer increased functional relevance, whilst allowing us to retain the ability to validate our observed metabolic dependencies across the adenoma to carcinoma sequence. We hope the reviewer agrees that these experiments would address their concerns.

    • The authors should provide proliferation data for the cell lines they used in this manuscript (C1, SB, 10C and M). In Fig. 1 B they show clear differences in EACR, can the authors provide data on glucose uptake differences in these analyzed cell lines.*

    We agree that proliferation and glucose uptake data would be a useful addition to the manuscript. We will provide doubling times for the cell lines used in this study and will measure glucose uptake by analysing extracellular glucose levels in the cell culture media from each of the cell lines.

    • In Figure 2 C the authors should provide isotope tracing data for the upper glycolysis (e.g. glucose and glucose-6-P) and alanine. In Figure 2 D the authors should provide the isotope tracing data for glutamine and glutamate.*

    We have data for glycolytic intermediates; glycerol-3-phosphate and dihydroxyacetone phosphate (DHAP) and alanine and will add them to the figures as requested.

    • Do the authors see any sign of reductive carboxylation in their U-13C glutamine experiments?*

    We observe only a low level of reductive carboxylation across the AA series cell lines (

    • Can the authors speculate how the C1, SB, 10C and M cell lines would react when glucose would be replaced with galactose in the culture environment and forcing the cells to increase oxidative phosphorylation (OXPHOS).*

    We would speculate that the cells would react similarly to our experiments in low glucose conditions displayed in Fig 3A-K. Given that M cells were shown to be the most flexible with regards to fuel source, we would expect them to be able to survive and proliferate more efficiently than the other cell lines in challenging culture conditions. Additionally, we would expect the C1s to survive well in galactose conditions, given that they rely less on glycolysis for ATP production and have significantly higher spare respiratory capacity compared to the more progressed cell lines.

    • Can the authors comment whether C1, SB, 10C and M cell lines show differences in coping with oxidative stress?*

    Again, we would speculate that the M cells would cope with exposure to oxidative stress best, given their metabolic flexibility. However, we would aim to test this by measuring the cellular response to hydrogen peroxide (which would induce oxidative stress) across all cell lines.

    • In the ASNS knockdown experiments do the authors detect an increase in glucose uptake in ASNS deficient cells.*

    This is an interesting question; we will address it by comparing extracellular glucose levels in C1 and M cells transfected with control and siRNA targeting ASNS.

    • Can the authors provide gene expression data that would explain the metabolic switch between early and late-stage adenocarcinoma? Do the authors detect any differences in mTORC1 activation among the C1, SB, 10C and M cell lines? ASNS is an ATF4 target, can the authors provide any expression data on ATF4 in their cell lines.*

    To address the first question, using our proteomics data, we have generated heatmaps showing protein abundance data from key metabolic pathways including glycolysis, the TCA cycle and the electron transport chain in the C1, SB and M cell lines. These data show an array of variation in protein expression of these pathways between the C1, SB and M cells, with no clear up or downregulation of these pathways as a whole, but rather more intricate regulation of clusters of proteins within the pathways. These data align well with the metabolomic data presented in Figure 2 and will allow us to investigate the mechanisms underlying the metabolic switch. These heat maps will be incorporated into the manuscript. Using the heatmaps we will identify and discuss key nodes we predict to explain the metabolic switch between early and late-stage adenocarcinoma. We will then determine whether manipulation of these nodes impact the metabolic phenotype of the cells experimentally. For example, the heat maps have highlighted that ENO3 or enolase 3 is strongly upregulated in the SB and M cells in comparison to the C1 cells and may be involved in driving the metabolic switch. Indeed, ENO3 has previously been found to promote colorectal cancer progression by enhancing glycolysis (Chen et al, Med Oncol, 2022), consistent with what we see here. To test this, we will knock down ENO3 across the cell line series and determine the impact on cellular phenotype and metabolism (using Seahorse extracellular flux analysis).

    With regards to mTORC1 activation, we have further analysed our proteomics data from C1, SB and M cells and have found that the M cells show significantly higher serine 235/236 phosphorylation of ribosomal S6 protein, a common readout for mTORC1 activation, compared to C1 and SB cells. Further, we aim to carry out immunoblotting across the four cell lines to analyse phospho-S6 (relative to total S6), 4E-BP1 and phospho-ULK-1 (relative to total ULK-1) levels.

    With regards to ATF4, using our proteomics data we have generated a heatmap of gene expression changes of ATF4 target genes in C1, SB and M cells that we will provide in supplementary material . These data suggest that there does not appear to be any clear pattern of enhanced or reduced ATF4 transcriptional activity across the cell lines, with different clusters of genes within this signature up or downregulated across the series. Moreover, Ingenuity Pathway Analysis (IPA) revealed that the ATF4 pathway showed an activation z-score of -0.41 (p=0.0134) in SB versus C1 cells, and 0.35 (p=0.00051) in M versus C1 cells (where a threshold of +/- 2 indicates activation/suppression of the pathway, respectively), confirming there is no clear regulation of this pathway between the cell lines. In addition, we will carry out immunoblotting for ATF4 expression levels across the cell line series.

    Reviewer #2

    *Major comments: *

    *Early CRC *

    *Molecular understanding of CRC is obviously of great interest and importance for the clinics. However, tumors of early stages are almost exclusively resected and not treated with systemic agents. Hence, the argument by the authors that the metabolic understanding of early CRC is of clinical relevance is somewhat misleading. Overall, it would have been much more clinically relevant to investigate the multiple steps of later stages during CRC progression. How about metabolic changes during metastasis. Deep mechanistic understanding of process during metastasis has striking clinical relevance. *

    We agree with the reviewer that understanding metastatic progression is of clinical relevance and should indeed be investigated in more detail. Using our model, we do shed light on a vulnerability of late-stage adenocarcinoma cells (sensitivity to asparagine synthetase (ASNS) inhibition). Indeed, we show that ASNS expression is elevated in both colorectal tumour and metastatic tissue in comparison to normal suggesting that our study may have revealed a vulnerability with utility for treating late stage (and potentially metastatic) tumours. The reviewer raises an important issue with the way we frame the utility of the model in the manuscript text. We will rewrite this to emphasise its utility in identifying late-stage vulnerabilities and the clinical value of this approach. We maintain that the molecular understanding of colorectal cancer across all stages of its progression will provide a valuable contribution to the field but agree that we should be more specific with regards to the clinical utility of our findings.

    *Model system *

    The cell lines used in this study are not state-of-the-art to investigate the complex process during CRC progression. The original paper is from 1993 in which the cell lines were generated does not allow understanding of the characteristics of these cell lines. Recently, multiple models have been established, for example in organoids, to investigate the progression of CRC much more reliably. There are systems that use CRISPR/CAS9 edited human organoids that follow the genetic alterations of CRC progression with accompanied phenotypes. Further, extensive biobanks of organoids from patients are available (also commercially) which better represent the stages of CRC. Similarly, the question raised above of how representative this progression cell line set is needs to addressed. The mutagen-induced progression could generate various alterations that are not detected in patients, hence create an artificial system. Overall, biological replicates are missing.

    We thank the reviewer for their critique and agree that our manuscript would be significantly strengthened if we were able to replicate our key findings in another model. We agree that the cell line series we have used here has limitations and we will make sure these are discussed by adding a ‘Limitations’ section to the ‘Discussion’. We maintain that the cell line series is a valuable tool in which to effectively identify metabolic vulnerabilities for further research. A key advantage of this system is that it is a human cell line series of the same lineage. In addition, we can easily conduct metabolomics and stable isotope tracer analysis allowing us to investigate cellular metabolic activity and manipulate any identified pathways easily. As such, the cell line series is an effective tool in which to identify potential vulnerabilities, but we agree that these vulnerabilities need to be validated in state-of-the-art organoid systems for the impact of the work to be clearer.

    To address this, in collaboration with Owen Sansom (Beatson Institute) and Laura Thomas (Swansea University), we aim to validate our identified metabolic dependency in mouse and human colorectal organoids respectively. We will determine the sensitivity of colorectal organoid models across a range of tumorigenicities and mutational profiles representing different stages of the adenoma-carcinoma progression to asparagine synthetase (ASNS) inhibition. We believe these experiments will substantially strengthen the manuscript and lend weight to our finding that late-stage adenocarcinoma cells are vulnerable to ASNS inhibition.

    *Gene Expression analysis *

    In Figure 5 C and D is the expression of ASNS to stages and overall survival from online available datasets correlated. Its unclear what the difference between tumor and metastatic in C means. The labelling in D is too small. Is the difference between the two groups significant? Are these patients only at a specific stage? It seems not that ASNS is a strong prognosticator; further stratification is needed to clarify the role of ASNS in CRC.

    The data displayed in Fig 5C and 5D are from separate datasets so are not correlated. In Fig 5C ‘Tumour’ refers to gene expression from the primary tumour site (in this case the colorectum), whereas ‘Metastatic’ refers to gene expression from a metastatic tumour (from which the primary tumour was of colorectal origin). We will make this clearer in the text and figure legend. We will also make the labelling on the survival plot in D clearer, indicating that the difference between the two groups is significant and displaying the p value clearly.

    The data included in the survival plots in 5D encompass all tumour stages. We have further analysed these data, adjusting for tumour stage. We found that high ASNS expression in later stage tumours (stage 3 and 4) is associated with poorer overall survival, whereas there is no significant difference in overall survival in earlier stage tumours (stage 1 and 2) in relation to ASNS expression. We plan to add this to the supplementary materials and discuss in the main text as it is consistent with our findings from the AA cell line series.

    *Western Blot controls *

    For the Western Blots in Figure 6 A and C the total S6 and ULK1 controls are missing what is needed to assess the effect on pS6 and pULK1 correctly.

    We will add total S6 and ULK1 controls to these figures.

    In the same panels, the KO efficacy is not very high in A (-ASN). However, this is crucial to make the conclusion that this cell line (C1) is not dependent on ASNS.

    The average knockdown efficiency in the C1 cells is 72% across n=3 experiments. Therefore, levels of ASNS are significantly reduced. However, to further validate this finding, we will use L-Albizziine, a competitive inhibitor of ASNS, at the same concentration in both C1 and M cells to eliminate any issues surrounding variation in knockdown efficiency and to replicate the results obtained using ASNS siRNA. These data will be included in supplementary material.

    *Minor comments: *

    *Statistical analysis of proliferation assays *

    The statistical significance for proliferation assays are missing.

    The statistical significance at the final timepoints of the proliferation assays are displayed on bar graphs in Supplementary Figure 5 (Figure S5B and C). We will add these to the proliferation curves in the main figure.

    Reviewer #3

    *A major concern is the model used in this study: *

    Sodium butyrate and the carcinogen N-methyl-N-nitro-nitrosoguanidine (MNNG) were used for the transformation. I believe this model was developed by one of the co-authors of the study, A.C. Williams in the 1990s. The relevance of the model for in vivo colon carcinogenesis is not entirely clear to me and I miss information why in particular sodium butyrate and MNNG were used. I am not an expert on colon carcinogenesis but I did not have the impression that this model has been widely adopted and I miss detailed information on the model itself as well as a critical discussion of its limitations.

    We thank the reviewer for raising these concerns and will include a ‘Limitations’ section in the manuscript ‘Discussion’ to elaborate on both the utility and the limitations of this model system. As described in response to concerns raised by reviewer #1 and reviewer #2, we plan to validate our findings in organoid models of colorectal tumourigenesis to strengthen the discoveries made using the AA cell line series.

    With regards to the use of sodium butyrate and MNNG for transformation of the C1 cells, justification was provided in the original paper describing generation of the cell line model series (Williams et al, Cancer Research. 1990). Sodium butyrate is naturally occurring in the gut and was used for the transformation of the C1 cells as it had been proposed to play a role in promoting colorectal tumorigenesis through upregulating carcinoembryonic antigen (CEA) expression and enhancing proliferation in adenoma cells able to resist growth arrest following treatment (Berry et al, Carcinogenesis. 1988). At the time of generating the cell line series, few reagents were known to induce transformation in human epithelial cells. However, MNNG was one of those and had been previously used to transform keratinocytes (Rhim et al, Science. 1986). Crucially, tumours formed in mice from xenografted 10C cells were found to be heterogeneous, displaying areas of differentiation with glandular organisation, the presence of functional goblet cells enabling mucin production, as well as areas of poorly or undifferentiated cells. Furthermore, cytogenetic analyses revealed that genetic changes in the cell line progression model such as chromosome 18q loss and KRAS activation replicate those seen in CRC patients (Williams et al, Oncogene. 1993). Together, these characteristics recapitulate human tumours in vivo, validating the use of sodium butyrate and MNNG in generating an in vitro CRC cell line model that represents human colorectal tumorigenesis.

    Figure 6: total levels of ribosomal S6 protein and ULK1 should be detected, quantified and used for normalization.

    We agree with the reviewer, we will add total S6 and ULK1 controls to these figures.

    Can you measure ASN upon inhibition of autophagy? Does it go down further?

    This is an interesting question, and we will address this experimentally by measuring ASN levels following treatment with chloroquine in the C1 and M cell lines. We will do this using stable isotope labelling and mass spectrometry and include the results in supplementary material.

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    Referee #3

    Evidence, reproducibility and clarity

    Legge and colleagues metabolically characterize an in vitro colon carcinogenesis model based on the development of the adenoma cell line PC/AA to a tumorigenic phenotype. They find differences in the use of glucose and glutamine in the different cell lines and a dependency of what they call late-stage adenocarcinoma cells on asparagine (ASN) synthesis, which is not present in their early stage cell line. The study is well-written and interesting.

    A major concern is the model used in this study:

    Sodium butyrate and the carcinogen N-methyl-N-nitro-nitrosoguanidine (MNNG) were used for the transformation. I believe this model was developed by one of the co-authors of the study, A.C. Williams in the 1990s. The relevance of the model for in vivo colon carcinogenesis is not entirely clear to me and I miss information why in particular sodium butyrate and MNNG were used. I am not an expert on colon carcinogenesis but I did not have the impression that this model has been widely adopted and I miss detailed information on the model itself as well as a critical discussion of its limitations.

    The seahorse and the stable isotope labelling experiments appear fine to me.

    Figure 6: total levels of ribosomal S6 protein and ULK1 should be detected, quantified and used for normalization.

    Can you measure ASN upon inhibition of autophagy? Does it go down further?

    Significance

    The identification of ASN synthesis as a potential vulnerability of advanced colon cancer is potentially significant but needs to be confirmed in other models.

    Differences in ASN sensing between early and late stage colon cancer cells as well as the role of autophagy are potentially interesting and merit further investigation.

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    Referee #2

    Evidence, reproducibility and clarity

    The manuscript by Legge and colleagues describes the metabolic rewiring during colorectal cancer (CRC) progression. While earlier stages depend on glycolysis but maintain oxidative metabolism later stages are more plastic and can be maintained in nutrient-poor environments. The study addresses a very important open question towards the progression of CRC, however the stated clinical relevance is relatively little. The strongest limitation is the used model system to describe the step-wise progression of CRC what makes it difficult to understand how important the findings of this study are for the very heterogonous disease CRC.

    Major comments:

    Early CRC

    Molecular understanding of CRC is obviously of great interest and importance for the clinics. However, tumors of early stages are almost exclusively resected and not treated with systemic agents. Hence, the argument by the authors that the metabolic understanding of early CRC is of clinical relevance is somewhat misleading. Overall, it would have been much more clinically relevant to investigate the multiple steps of later stages during CRC progression. How about metabolic changes during metastasis. Deep mechanistic understanding of process during metastasis has striking clinical relevance.

    Model system

    The cell lines used in this study are not state-of-the-art to investigate the complex process during CRC progression. The original paper is from 1993 in which the cell lines were generated does not allow understanding of the characteristics of these cell lines. Recently, multiple models have been established, for example in organoids, to investigate the progression of CRC much more reliably. There are systems that use CRISPR/CAS9 edited human organoids that follow the genetic alterations of CRC progression with accompanied phenotypes. Further, extensive biobanks of organoids from patients are available (also commercially) which better represent the stages of CRC. Similarly, the question raised above of how representative this progression cell line set is needs to addressed. The mutagen-induced progression could generate various alterations that are not detected in patients, hence create an artificial system. Overall, biological replicates are missing.

    Gene Expression analysis

    In Figure 5 C and D is the expression of ASNS to stages and overall survival from online available datasets correlated. Its unclear what the difference between tumor and metastatic in C means. The labelling in D is too small. Is the difference between the two groups significant? Are these patients only at a specific stage? It seems not that ASNS is a strong prognosticator; further stratification is needed to clarify the role of ASNS in CRC.

    Western Blot controls

    For the Western Blots in Figure 6 A and C the total S6 and ULK1 controls are missing what is needed to assess the effect on pS6 and pULK1 correctly. In the same panels, the KO efficacy is not very high in A (-ASN). However, this is crucial to make the conclusion that this cell line (C1) is not dependent on ASNS

    Minor comments:

    Statistical analysis of proliferation assays

    The statistical significance for proliferation assays are missing.

    Significance

    The described topic is very relevant and of great interest to the field. However, I see the major limitation in the applied system to decode metabolic dependencies. If the key points were validated for example in state-of-the-art organoid systems, the impact of the work would be much clearer. I have to note that my expertise in the field of metabolism is relative little. My expertise lays in modelling CRC and its plasticity/heterogeneity with a strong asset to the translational space.

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    Referee #1

    Evidence, reproducibility and clarity

    Legge et al. provide novel metabolic liabilities in colorectal cancer progression. They identified that during colorectal tumor progression cancer cells shift from glycolytic to oxidative metabolism that enables cancer cells to advance. Using stable isotope tracing experiments with U-13C glucose and U-13C glutamine they showed that the contribution of these carbon sources changes from colorectal adenoma to carcinoma progression. In their in vitro model they identified that late-stage colorectal adenocarcinoma cells (M) display metabolic plasticity which leads to resistance to changes in nutrient supply compared to early stage adenoma cells. Proteomic analysis revealed that amino acid metabolism is highly dysregulated during carcinoma progression. The authors identified ASNS and regulation of asparagine availability as novel metabolic target for late-stage adenocarcinoma.

    Overall, this is a very interesting and important manuscript describing a previously unknown metabolic liability in late-stage adenocarcinoma. The authors also nicely demonstrate how cancer metabolism is adapting and changing during cancer progression. These findings are clearly of high relevance in the context of better understanding of how metastatic cancer cells gain abilities to metastasize. Therefore, the findings presented here would advance the field. However, there are some open questions in the study design and the current set of data as listed below.

    1. All of the findings in this manuscript are limited to in vitro observations, we know that most of the in vitro findings can not be translated in vivo. The manuscript would significantly benefit from in vivo experiments using the cells described in Fig.1 A and confirming the in vitro findings.
    2. The authors should provide proliferation data for the cell lines they used in this manuscript (C1, SB, 10C and M). In Fig. 1 B they show clear differences in EACR, can the authors provide data on glucose uptake differences in these analyzed cell lines.
    3. In Figure 2 C the authors should provide isotope tracing data for the upper glycolysis (e.g. glucose and glucose-6-P) and alanine. In Figure 2 D the authors should provide the isotope tracing data for glutamine and glutamate.
    4. Do the authors see any sign of reductive carboxylation in their U-13C glutamine experiments?
    5. Can the authors speculate how the C1, SB, 10C and M cell lines would react when glucose would be replaced with galactose in the culture environment and forcing the cells to increase oxidative phosphorylation (OXPHOS).
    6. Can the authors comment whether C1, SB, 10C and M cell lines show differences in coping with oxidative stress?
    7. In the ASNS knockdown experiments do the authors detect an increase in glucose uptake in ASNS deficient cells.
    8. Can the authors provide gene expression data that would explain the metabolic switch between early and late-stage adenocarcinoma? Do the authors detect any differences in mTORC1 activation among the C1, SB, 10C and M cell lines? ASNS is an ATF4 target, can the authors provide any expression data on ATF4 in their cell lines.

    Referees cross-commenting

    The comments from the other reviewers are fair and would significantly improve the quality of the manuscript. Overall all three reviewers think that this manuscript is of great interest for the scientific community.

    Significance

    Overall, this is a very interesting and important manuscript describing a previously unknown metabolic liability in late-stage adenocarcinoma. The authors also nicely demonstrate how cancer metabolism is adapting and changing during cancer progression. These findings are clearly of high relevance in the context of better understanding of how metastatic cancer cells gain abilities to metastasize. Therefore, the findings presented here would advance the field.

    The findings are novel and have not been described in this detail before.

    The findings would be of importance for a broad audience and ideally will help to identify novel pharmacological inhibitors that could be used in patients.

    My expertise is in cancer metastasis, cancer metabolism and translational/clinical medicine.

  10. Version published to 10.1101/2022.03.23.485483v4 on bioRxiv
  11. Version published to 10.1101/2022.03.23.485483v3 on bioRxiv
  12. Version published to 10.1101/2022.03.23.485483v2 on bioRxiv
  13. Version published to 10.1101/2022.03.23.485483v1 on bioRxiv