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  1. Evaluation Summary:

    The authors collected human samples from a rare cancer type in which evolutionary features have not been well-defined. They describe the clonal evolution through sampling at precancerous, primary tumour, and metastatic stages. Whole exome sequencing was performed and one of the mutation types was confirmed with other techniques.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

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  2. Reviewer #1 (Public Review):

    Kang et al. have performed whole exome sequencing of gall bladder carcinomas and associated metastases, including analysis of rapid autopsy specimens in selected cases. They have also attempted to delineate patterns of clonal and subclonal evolution across this cohort. In cases where BilIN was identified, the authors show that subclones within these precursor lesions can expand and diversify to populate the primary tumor and metastatic sites. They also demonstrate subclonal variation and branching evolution across metastatic sites within the same patient, with the suggestion that multiple subclonal populations may metastasize together to seed different sites. Lastly, they highlight ERBB2 amplification as a recurrent event observed in gall bladder carcinomas.

    While these data add to the literature and start to examine important questions related to clonal evolution in a relatively rare malignancy, the authors' findings are very descriptive and it is hard to draw many generalizable conclusions from their data. In addition, the presentation of their figures is somewhat confusing and difficult to interpret. For example, they do not separate their clonal analyses by disease site and by time in a readily interpretable manner, as in some instances of Figure 2 and Figure 3 the clone maps are from different sites collected at the same time point, while others show some samples at different time points. Depicting these hierarchies in a more organized and clearly understandable manner would help readers more easily interpret the authors' findings. In addition, the clinical implications of these clonal hierarchies and their heterogeneity are unclear, as the authors do not relate the observed evolution to intervening therapies and may not be powered to do so with this dataset.

    Additional areas that would require clarification include:
    1. There are very few details on how the authors performed their subclone analysis to identify major subclones, and what each of the clusters in Supplemental Figure 1 represents. In addition, they do not describe how they determined that the highlighted mutations in Table 2 were drivers for metastasis and subclonal expansion. Were these the only genes that exhibited increased allele frequencies in metastatic sites, or were other statistical criteria used?

    2. The authors do not discuss the relevance of variation in mutational signatures observed with disease progression/metastasis, e.g., is there any significance that signature 22 (aristolochic acid) and signature 24 (aflatoxin) are increased in metastases? In addition, when comparing their data to previously published reports in Figure 1B and Figure 4A, it would be helpful if the authors discussed possible reasons for some of the large differences in mutational or signature frequencies across datasets. For example, do the authors think the frequency of ERBB2 alterations is so much higher in their cohort than in prior reports due to methodological/data reasons or due to differences in patient population?

    3. The authors try to describe and draw conclusions about the possibility of metastasis to metastasis spread in p.6, lines 6-10 "In our study, of 7 patients with 2 or more metastatic lesions, evidence of metastasis-to-metastasis spread was found in 2 patients (28.6%). In GB-A1 (Figure 2A), it appears that CBD, omentum 1-2, mesentery, and abdominal wall 2-4 lesions may originate from abdominal wall 1 (old) rather than from primary GBAC considering clone F." The authors conclude here that the spread arose from abdominal wall 1, but this lesion is only separated from the CBD lesion by 1 month. There is no history given about whether this timing difference is significant or if it was simply due to clinically-driven differences in when each lesion was sampled. Given the proximity of the CBD lesion to the original gall bladder cancer, it seems just as likely that all of these distant lesions were seeded from the CBD lesion. If this is the case, the author's conclusion about "metastasis to metastasis" spread does not seem strongly supported. It would be helpful if the authors could clarify this point and/or provide additional data to strengthen this conclusion.

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  3. Reviewer #2 (Public Review):

    Minsu Kang et al. analyzed 11 patients with gallbladder adenocarcinoma using multi-point sampling. Mutational analysis revealed evolutional patterns during progression where the authors found metastasis-to-metastasis spread and the migration of a cluster of tumor cells are common in gallbladder adenocarcinomas. The signature analysis detected signatures 22 (aristolochic acid) and 24 (aflatoxin) in metastatic tumors. Overall, the analyses are well-performed using established algorithms. However, the manuscript is highly descriptive. Therefore, it is very difficult to understand what the novel findings are.

    Major comments
    1. The sections "Evolutionary trajectories and expansion of subclones during regional and distant metastasis", "Polyclonal metastasis and intermetastatic heterogeneity", "Mutational signatures during clonal evolution", and "Discussion" are highly descriptive which makes it difficult to understand what the novel and/or important findings are. Those sections would profit from reorganization.

    2. What would enhance this paper is more of a connection between the bioinformatics analysis and the biology. Although the authors analyzed multi-point sequencing data well, this paper lacks in-depth discussion. I understand that the results in the paper are "computationally" the most likely. However, the impact is lost by an incomplete connection to biology.

    3. In addition to the above concern, it is difficult to comprehend the cohort as the detailed information is lacking. I would suggest providing a brief table that contains the number of collected samples, frozen or FFPE, the clinical information, etc. by sample.

    4. The mutations with very low allele frequency (< 1%) are discussed in the manuscript. However, no validation data is provided. Please add a description of the accuracy of the mutation calling considering the following concerns.
    • FFPE samples are analyzed using the same method as frozen samples. FFPE contains much more artifacts. Is it adequate to use the same methods for both frozen and FFPE samples?
    • How were those mutations with low allele frequency validated? Are those variants validated by other methods? Especially in FFPE.
    • Is the low variant allele frequency (0.2~1%) significantly higher than the background noise level?

    5. The authors compared mutational signatures divided by stages or timings. How are the signatures calculated although each sample has a distinct number of somatic mutations? Did the authors correct the difference?

    6. In distant metastasis tumors, signatures 22 and 24 are increased. Those two signatures are strongly associated with a specific carcinogen. Although the clinical information lacks, do the authors think that those patients were exposed to those chemicals after the diagnosis? Why do the authors think the two signatures increased in the metastatic tumors? Were those signatures validated by other methods?

    7. Figures 2 are well-described. However, they are difficult for readers to fully understand. The colors for each clone are sometimes similar. The results of multi-time point and regional analyses in the cases with multiple sampling are not integrated. Driver mutations are separately described in the small phylogenetic trees. Evolutional patterns (linear or branching) are not described in the figures. Modifying the above concerns would improve the manuscript.

    8. "Among 6 patients having concurrent BilIN tissues, two patients were excluded from the further analysis because of low tumor purity in one patient and different mutational profiles between BilIN and primary GBAC in the other patient, suggesting different origins of the two tumors (Figure 1-figure supplement 2)." This seems cherry-picking. More explanation is necessary.
    • How is the tumor purity? Although the authors use 0.2% variant allele frequency as true mutation (for example Table 2), is the tumor purity lower than 0,2%?
    • BilIN and GBAC of GB-S7 have some shared mutations. Why do the authors conclude that BilIN and GBAC have distinct origins? Do the authors think that those shared mutations are germline mosaic mutations?
    • Was the copy number profile compared between BilIN and GBAC?

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