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  1. Nov 2019
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      A. The typical domed appearance of a hydrocephalus-harboring skull is apparent as early as P4, as shown in a new side-by-side comparison of pups at that age (Fig. 1A). B. Though this is not stated in the MS

      1. Figure 6: Why has only....

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      <li style="font-weight: 400;">A. The typical domed appearance of a hydrocephalus-harboring skull is apparent as early as P4, as shown in a new side-by-side comparison of pups at that age (Fig. 1A). <li style="font-weight: 400;">B. Though this is not stated in the MS

        1. Figure 6: Why has only....*

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  2. Oct 2019
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      Referee #2

      Points of Critique

      I would urge them to reposition as a descriptive study rather than making too many grand statements about drug sensitivities.### Other Comments

      Van Alphen et al. describe phosphotyrosine proteomic profiling of a panel of AML cell lines and two patient-derived AML samples. Subsequent analyses attempt to identify potentially targetable kinases and pathways that would be considered vulnerabilities for drug treatments. Some hypotheses from these analyses are tested by drug treatment. The patient-derived samples are analyzed as a proof of concept and compared to the cell line profiles.

      I think that this study is a potentially valuable resource, but might be served better if positioned more as a catalog of pY signaling in AML and less as a drug-targeting effort. The analysis graphs and charts are quite handy, and perhaps they could be served on a more interactive website that could be expanded in the future as the authors continue similar studies. However, I find that many of the conclusions are overstated and that some of the internal logic is inconsistent. Further, while the bioinformatics analyses are carefully planned and well intentioned, I was confused by the inconsistent quantitative metrics used in different parts of the manuscript and curious why a more modern isobaric labeling technique wasn’t used to compare among this relatively small panel of cell lines. Below I offer several points that could be addressed to help to improve this manuscript.

      1. The authors claim that sensitivity to drugs predicted by their inferred kinase activity metrics “validates” their predictions. However, all of the drugs tested have demonstrable polypharmacology. How can they be sure the targets being hit that cause loss in viability are the same ones that they have predicted? Also, it seems curious that they only tested quizartinib in predicted FLT3-GoF lines and ponatanib in inferred FGFR-GoF lines. How do we know that these drugs just don’t kill all lines? It would be more convincing to show some lines where these drugs did not cause loss in viability.

      2. Along these same lines, phosphoproteomics seems like a long path to identify vulnerabilities in cancer cell lines. Screening drugs on cell lines is cheaper and easier. Indeed, the CTD2 project has a drug screening arm (as did CCLE), and new Cancer Dependency Map screening is enlarging these screens. These projects also have more comprehensive genetic characterization of the cell lines involved. If the logic is that the drug treatments “validate” the predictions of aberrant kinase activity, couldn’t the drug screening be used to make these predictions, to be later validated by phosphoproteomics? Perhaps screen all TKIs against the AML cell lines and see what common targets emerge?

      3. I think that claiming that the patient results match the cell line results is a bit of selective interpretation. The first thing I was drawn to is the whopping amounts of MAPK14 phosphorylation identified by their analysis in these samples. MAPK14 - a.k.a. p38 MAPK alpha - also has drugs that target it. If you were making a therapeutic hypothesis, wouldn’t you start with a p38 inhibitor rather than a FLT3 inhibitor? You already knew that the patients had FLT3-ITD, so you’d probably be starting there anyway. While MAPK14 is found to be phosphorylated in the cell lines as well, the degree to which it rises to the top in the patient samples is striking. This also illustrates an issue with drawing inferences from cell lines to real patient samples.

      4. On p.15, you state: “P15: “Kinase activity ranking analysis of the FLT3-ITD mutant cell lines MV4-11, MOLM-13, and Kasumi-6, and the V617F JAK2 cell line HEL showed a lower ranking of FLT3 and JAK phosphorylation than expected based on their mutation status, compared to other kinases (position 6-10). Interestingly, other high-ranking kinases in these cell lines were generally located downstream in the FLT3 and JAK2 cellular signaling hierarchy, thereby still implicating FLT3 and JAK2 as primary suspects of driver activity.”

      Perhaps this demonstrates the limitations of the approach? Genetics says FLT3 and/or JAK2 are mutated, proxy measurements say its activated, but the way you are estimating direct activity is not so great? Would a targeted panel on activation loop sites be better?

      1. Figure 6 is an interesting analysis but how it was generated is unclear. Again, polypharmacology of the drugs make it hard to interpret. Are the graphs for all potential targets? Just some? Weighted by in vitro IC50 concentrations and/or binding affinities?

      Minor points:

      IC50 is inappropriate nomenclature here, which describes the concentration at which 50% of an enzyme’s activity is inhibited. The authors should substitute EC50 throughout, as they are referring to the concentration at which viability is decreased by 50%.

      Ibrutinib is described as a pan-KI - this is confusing and misleading. It is a pretty specific BTK inhibitor.

      Please update sup table 5 to show the exact nature of the mutations. Also, why does Kasumi-6 have two different (presumably) allelic ratios for FLT3 (presumed ITD?)?

      Methods - p7 “as described elsewhere” - reference needed?

      The statistical rationale is not well explained.

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      Referee #1

      Other Comments

      This manuscript describes phosphotyrosine-focused phosphoproteomics for 16 AML cell lines to obtain molecular profiles of pY towards personal therapy using proper molecular targeting drugs. This is the revised (re-submitted) version and the authors added new data analysis especially on the relationship between kinase-ranking parameters and drug IC50 profiles to kinases. These results indicate the current progress and limitation of phosphoproteomics combined with genomics data and the related computational tools. Overall, the precise descriptions of the experimental procedures as well as the high quality of the experimental datasets are quite useful for researchers in this field. This manuscript should be published after some revisions shown below:

      (1) This research group just published a paper on kinase ranking using phosphoproteome datasets, named INKA. Mol Syst Biol. 2019 Apr 12;15(4):e8250. doi: 10.15252/msb.20188250 INKA, an integrative data analysis pipeline for phosphoproteomic inference of active kinases.

      INKA seems quite similar to the approaches in this manuscript. The authors should mention about INKA. Especially the parameters in Figure 6 should be described clearly whether these are the same in INKA or not.

      (2) Figure 1 as well as Abstract and the first section of RESULTS: the numbers of phosphotyrosine sites and phosphotyrosine peptides should also be described in addition to the current description.

      (3) Figure 2: the color for mutation is overlapped with colors for the heatmap. To avoid the misunderstanding, the authors should use the different colors.

  3. Sep 2019
  4. Jul 2016
  5. Mar 2016
    1. ple’s SquirrelFish Extremeinline threading interpreter and Google’s V8 JS compiler. Our system generates particularly efficient code for programs that b

      Nice