Author response:
The following is the authors’ response to the original reviews.
Reviewer #1 (Public Review):
Strengths:
(1) In my assessment, the data sufficiently demonstrates that a modified version of Pertuzamab can bind both the wild-type and S310 mutant forms of ERBB2.
(2) The engineering strategy employed is rational and effectively combines computational and experimental techniques.
(3) Given the clinical activity of HER2-targeting ADCs, antibodies unaffected by ERBB2 mutations would be desired.
Weaknesses:
(1) There is no data showing that the engineered antibody is equally specific as Pertuzamab i.e. that it does not bind to other (non-ERBB2) proteins.
Showing the specificity of the engineered antibodies is indeed important. We did not address it in the current ms, but it can be tested in the future.
(2) There is no data showing that the engineered antibody has the desired pharmacokinetics/pharmacodynamics properties or efficacy in vivo.
In this ms we did not conduct in-vivo experiments. When moving forward, pharmacokinetics/pharmacodynamics properties and efficacy will be tested as well.
(3) Computational approaches are only used to design a phage-screen library, but not used to prioritize mutations that are likely to improve binding (e.g. based on predicted impact on the stability of the interaction). A demonstration of how computational pre-screening or lead optimization can improve the time-intensive process would be a welcome advance.
Thank you for this important comment. In the present ms we indeed used a computational approach for prioritizing residues to be mutated, but we did not prioritize the mutations that are likely to improve binding. In the initial library design, we did prioritize the mutations. However, due to experimental approach limitations with codon’s selection for the library, we had decided to allow all possible residues in each position, knowing that the selection will remove non-binding variants.
Context:
The conflict of interest statement is inadequate. Most authors of the study (but not the first author) are employees of Biolojic, a company developing multi-specific antibodies, but the statements do not clarify whether the presented antibodies represent Biolojic IP, whether the company sponsored the research, and whether the company is further developing the specific antibodies presented.
The Conflict-of-Interest statement will be revised as such: The Biolojic Design authors are employees of Biolojic Design and have stock options in Biolojic Design. The company did not sponsor the research, does not hold IP for the presented antibodies, and is not further developing the presented antibodies.
Reviewer #2 (Public Review):
Strengths:
(1) Deep computational analyses of large datasets of clinical data provide useful information about HER2 mutations and their potential relevance to antibody therapy resistance.
(2) There is valuable information analyzing the residues within or near the interface between the antigen HER2 and the Pertuzumab antibody (heavy chain). The experimental antibody library screening obtained 90+ clones from 3.86×1011 sequences for further functional validation.
Weaknesses:
(1) There is a lack of assessment for antibody variant functions in cancer cell phenotypes in vitro (proliferation, cell death, motility) or in vivo (tumor growth and animal survival). The only assay was the western blotting of phosphopho-HER3 in Figure 4. However, HER2 levels and phosphor-HER2 were not analyzed.
We indeed did not assess the engineered antibodies function in cancer cells. While a complete signaling assessment obviously requires functional assessment as well, due to the complexity of this assay, papers in this field (for example [1-3]) measure the signaling activation following HER2-HER3 dimerization by measuring pHER3, and we relied on them in this ms.
(2) There is a misleading impression from the title of computational engineering of a therapeutic antibody and the statement in the abstract "we designed a multi-specific version of Pertuzumab that retains original function while also bindings these HER2 variants" for a few reasons:
a. The primary method used for variant antibody identification for HER2 mutant binding is rather traditional experimental screening based on yeast display instead of the computational design of a multi-specific version of Pertuzumab.
b. There is insufficient or lack of computational power in the antibody design or prioritization in choosing variant residues for the library construction of 3.86×1011 sequences. It seems random combinations from 6 residues out of 4 groups with 20 amino acid options.
c. The final version of the tri-binding variant is a combination of screened antibody clones instead of computation design from scratch.
d. There is incomplete experimental evidence about the therapeutic values of newly obtained antibody clones.
Thank you for this relevant comment. When addressing relevant residues to be mutated, the number of potential variants is enormous. The computational approach was aimed at identifying the most preferable residues, in which variation can improve binding and is not likely to harm important interactions. Although an initial smaller number of residues could be chosen, we decided to broaden our view and create a larger library, in the aim of combining the computational selection with an experimental selection. This indeed is not a computational design from scratch, but rather an intercourse between the computer and the lab, that yielded the presented results.
(3) Figures can be improved with better labeling and organization. Some essential pieces of data such as Supplementary Figure 1B on HER2 mutations in S310 that abrogated its binding to Pertuzumab should be placed in the main figures.
Thank you for this comment, the relevant figures were moved to the main text, and the labels were revised.
(4) It is recommended to provide a clear rationale or flowchart overview into the main Figure 1. Figure 2A can be combined with Figure 1 to the list of targeted residues.
Figures 1 and 2 were divided differently, and the rationale was moved to the main text.
(5) The quality of Figures such as Figure 2B-C flow data needs to be improved.
High-quality figures were submitted with the revised ms.
Reviewer #1 (Recommendations for The Authors):
Major:
(1) It should be clarified whether the S310 somatic mutations represent resistance mutations to Pertuzamab (i.e. emerge post-therapy) or are general mutations that activate HER2. This is important because mutations that specifically "evade" the binding of an antibody may be substantially more difficult to overcome than mutations that only by chance occur in the antibody binding site. This concern should be addressed in the introduction and discussion as it changes the interpretation of the data.
This is a very important note. To the best of our knowledge, these mutations were not identified as resistance mutations that emerged post-therapy. However, as mentioned in the introduction, these mutations form hydrophobic interactions that stabilize HER2 dimerization. Moreover, cells expressing these mutations show hyperphosphorylation of HER2 and an increase in the subsequent activation of signaling pathways. Thus, these mutations do not necessarily evade Pertuzumab binding, but benefit cancer growth. This point was clarified in the introduction of the revised text.
(2) While the authors claim that S310 germline pathogenic variants exist, I could not find evidence that this is the case. The dbGAP ID does not provide any evidence (either in the form of a citation or prevalence). The variants do not exist in GnomAD. A recent article discussing pathogenic ERBB2 germline variants only mentions S310 as a somatic variant https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8268839/ and I could not find evidence for S310 being a germline variant in the references provided by the author (https://www.nature.com/articles/nbt.3391) - where it is only mentioned as a somatic mutation. I could not find evidence of a cancer predisposition syndrome associated with this variant.
Thank you for highlighting this matter. We had assumed that the presence of the variant in dbSNP means it is also a germline mutations, what may not be correct. However, we did find some evidence of this mutation as germline in ClinVar, and this was edited in the revised ms. https://www.ncbi.nlm.nih.gov/clinvar/RCV001311879.7.
(3) The authors should consider experiments that show that the modified Pertuzamab has the same mechanism of action as the original Pertuzamab in preventing dimerization of the ERBB2 homodimer and/or interactions with ERBB3. I cannot recommend a specific approach, but at present it is not clear whether the mechanism or just the effect (phosphorylation of ERBB3) is the same.
As mentioned above, for the assessment of HER-HER3 binding and HER3 signaling, in this ms we relied on a previous works [1-3] that also measured the signaling activation following HER2-HER3 dimerization by measuring pHER3.
(4) The authors should perform in vitro experiments to demonstrate that the engineered antibody has similar on-target specificity not only sensitivity. I don't know what the ideal experiments would be, but should probably probe native epitopes. Western blots, immunoprecipitation of cell lysates?
As mentioned above, showing the specificity of the engineered antibodies is indeed important. We did not address it in the current ms, but it can be tested in future work.
Minor:
(1) The introduction should review better the literature on the computational/rational design of antibodies, especially multi-specific - and likely de-emphasize small molecules (and mutations associated with the resistance thereof) as the presented research does not inform the design of mutation-agnostic small molecules.
Thank you for these comments, the introduction was revised accordingly.
(2) The authors should better present the fact that the lack of binding of Pertuzamab to HER2 S310 was previously known, thus the whole strategy of searching COSMIC, and computationally predicting their binding impact was unnecessary. Rather it would be helpful to learn how many other COSMIC hotspots could have a similar effect on other clinical antibodies.
The lack of binding was indeed previously known, as mentioned in the introduction. However, we did not start our analysis targeting HER2 specifically, but we rather found these mutations because they were located in the binding pocket, which enabled our strategy to compensate for these mutations with alteration of the original Pertuzumab. Regarding other potential hotspots, the numbers appeared in Supplementary Table 1, and were moved to the main text.
Stylistic:
(1) Avoid using the term "drug" for an antibody.
The term was changed to “antibody therapeutics” in the revised text.
(2) Avoid repetition in the introduction.
Thank you, we revised the introduction with this comment in mind.
Reviewer #2 (Recommendations For The Authors):
The quality of Figure 2B-C flow data needs to be improved:
a. The diagonal populations suggest inappropriate color compensation or indicate cells are derived from unhealthy populations.
We believe there may be some confusion here. The figures you are referring to are figures of very diverse library. The selected clones show nice diagonals, as shown in Supplementary Figure 5.
b. Additional round 3 and round 4 did not seem to improve the enrichment of targeted clones but rather had similar binding profiles to each of the three proteins over and over.
Two sets of the fourth round of selection were done, each originated from a different sub-population in round 3: 1. Clones that bind the S310Y mutation 2. Clones that bind the S310F mutation. The aim of the R4 was to examine this binders against the second mutation and canonical HER2 in the search for multi-specificity. Additional clarification of this point will be added to the main text.
c. Figure legends are vague with non-specific descriptions of cells and conditions, and unclear statements of "FACS results...".
The legends were edited in the revised version.
d. Text fonts are in low resolution.
High-quality figures were submitted with the revised ms.
(1) Diwanji, D., et al., Structures of the HER2-HER3-NRG1β complex reveal a dynamic dimer interface. Nature, 2021. 600(7888): p. 339-343.
(2) Yamashita-Kashima, Y., et al., Mode of action of pertuzumab in combination with trastuzumab plus docetaxel therapy in a HER2-positive breast cancer xenograft model. Oncol Lett, 2017. 14(4): p. 4197-4205.
(3) Kang, J.C., et al., Engineering multivalent antibodies to target heregulin-induced HER3 signaling in breast cancer cells. MAbs, 2014. 6(2): p. 340-53.
