Rearrangement of 3D genome organization in breast cancer epithelial - mesenchymal transition and metastasis organotropism

Reviewer #1 (Public review):

Summary:

This study utilized publicly available Hi-C data to ensemble a comprehensive set of breast cancer cell lines (luminal, Her2+, TNBC) with varying metastatic features to answer whether breast cancer cells would acquire organ-specific features at the 3D genome level to metastasize to that specific organ. The authors focused on lung metastasis and included several controls as the comparison including normal mammary lines, normal lung epithelial lines, and lung cancer cell lines. Due to the lower resolution at 250KB binning size, the authors only addressed the compartments (A for active compartment and B for inactive compartment) not the other 3D organization of the genome. They started by performing clustering and PCA analysis for the compartment identity and discovered that this panel of cell lines could be well separated based on Her2 and epithelial-mesenchymal features according to the compartment identity. While correlating with the transcriptomic changes, the authors noticed the existence of concordance and divergence between the compartment changes and transcriptomic changes. The authors then switched gears to tackle the core question of metastatic organotropism to the lung. They discovered a set of "lung permissive compartment changes" and concluded that "lung metastatic breast cancer cell lines acquire lung-like genome architecture" and "organotropic 3D genome changes match target organ more than an unrelated organ". To prove the latter point, the authors enlisted an additional non-breast cancer cell line (prostate cancer) in the setting of brain metastasis. This is a piece of pure dry computational work without wet bench experiments.

Strengths:

The authors embarked on an ambitious journey to seek the answer regarding 3D genome changes predisposing to metastatic organotropism. The authors succeeded in the assembly of a comprehensive panel of breast cancer cell lines and the aggregation of the 3D genome structure data to conduct a hypothesis-driven computation analysis. The authors also achieved in including proper controls representing normal non-cancerous epithelium and the end organ of interest. The authors did well in the citation of relevant references in 3D genome organization and EMT.

Weaknesses:

(1) The authors should clearly indicate how they determine the patterns of spread of the breast cancer cell lines being utilized in this manuscript. How did the authors arrive at the conclusion that certain cell lines would be determined as "localized spread" and "metastatic tropism to the lung"? This definition is crucial, and I will explain why.

Todd Golub's team from the Broad Institute of MIT and Harvard published "A metastasis map of human cancer cell lines" to exhaustively create a first-generation metastasis map (MetMap) that reveals organ-specific patterns of metastasis. (By the way, this work was not cited in the reference in this manuscript.) The MetMap Explorer (https://depmap.org/metmap/vis-app/index.html) is a public resource that could be openly accessed to visualize the metastatic potential of each cell line as determined by the in vivo barcoding approach as described in the MetMap paper in the format of petal plots. 5 organs were tested in the MetMap paper, including brain, lung, liver, kidney, and bone. The authors would discover that some of the organ-specific metastasis patterns defined in the MetMap Explorer would be different from the authors' classification. For example, the authors defined MCF7 as a line as lung metastatic, and rightly so the MetMap charted a signal towards lung with low penetrance and low metastatic potential. The authors defined ZR751 as a line with localized spread, however, the MetMap charted a signal towards the kidney with low penetrance and low metastatic potential, the signal strength similar to the lung metastasis in MCF7. A similar argument could be made for T47D. The TNBC line MDA-MB-231 is indeed highly metastatic, however, in MetMap data, its metastasis is not only specific to the lung but towards all 5 organs with high penetrance and metastatic potential. The 2 lung cancer cell lines mentioned in this study, A549 and H460, the authors defined them as localized spread to the lung. However, the MetMap data clearly indicated that A549 and H460 are highly metastatic to all 5 organs with high penetrance and high metastatic potential.

Since results will vary among different experimental models testing metastatic organotropism, (intra-cardiac injection was the metastasis model being adopted in the MetMap), the authors should state more clearly which experimental model system served as the basis for their definition of organ-specific metastasis. In my opinion, this is the most crucial first step for this entire study to be sound and solid.

(2) Figure 1b: The authors found that "MDA-MB-231 cells were grouped with the lung carcinoma cells. This implies that the genome organization of this cell line is closer to that of lung cells than to other breast epithelial cell lines.". In fact, another TNBC line BT549 was also clustered under the same clade. So this clade consisted of normal-like and highly metastatic lines. Therefore, the authors should be mindful of the fact that the compartment features might not directly link to metastasis (or even metastatic organotropism).

(3) Figure 3: In the text, the authors stated, "To further investigate this result, we examined the transcription status of genes that changed compartment across the EMT spectrum and, conversely, the compartment status of genes that changed transcription (Fig. 3b, c, and d)". However, it was not apparent in the figure that the cell lines were arranged according to an EMT spectrum. Also, the clustering heatmaps did not provide sufficient information regarding the genes with concordant/divergent compartments vs transcription changes. It would be more informative if the authors could spend more effort in annotating these genes/pathways.

(4) Figure 4: The title of the subheading of this section was 'Lung metastatic breast cancer cell lines acquire lung-like genome architecture". Echoing my comments in point 1, I am a bit hesitant to term it as "lung metastatic" but rather "metastatic' in general since cell lines such as MDA-MD-231 do metastasize to other organs as well. However, I do get the point that the definition of "lung metastasis" is derived from the common metastasis features among the cell lines here (MCF7, T47D, SKBR3, MDA-MB-231).

There might be another argument about whether the "lung" carcinoma cell lines can be considered "localized" since they are also capable of metastasizing to other organs. In a way, what the authors probably were trying to leverage here is the "tissue" identity of that organ. Having said this, in addition to showing the "lung permissive changes", the authors should show the "breast identity conservation" as well. Because this section started to deal with the concept of "tissue/lineage identify", the authors should also clarify whether these breast cancer cell lines capable of making lung metastasis are also preserving their original tissue identity from the compartment features (which would most likely be the case).

(5) Rest of the sections: The authors started to claim that the organ-specific metastasis permissive compartmental features mimic the destinated end organ. The authors utilized additional non-breast cancer cell lines (prostate cancer cell lines LNCaP as localized and DU145 as brain metastatic) in brain metastasis to strengthen this claim. (DU145 in MetMap again is highly metastatic to lung, brain, and kidney). However, this makes one wonder that for cell lines that are capable of metastasizing to multiple organ sites (eg. MDA-MB-231, DU145, A459, H460), does it mean that they all acquire the permissive features for all these organs? This scenario is clinically relevant in Stage 4 patients who often present with not only one metastatic lesion in one single organ but multiple metastatic lesions in more than one organ (eg. concomitant liver and lung metastasis). Do the authors think that there might be different clones having different tropism-permissive 3D genome features or there might be evolutionary trajectory in this?

In my opinion, to further prove this point, the authors might need to consider doing in vivo experiments to collect paired primary and organ-specific metastatic samples to look at the 3D genome changes.

(6) Technically, the study utilized public Hi-C data without generating new Hi-C data. The resolution of the Hi-C data for compartments was set at 250KB as the binning size indicating that the Hi-C data was at lower resolution so it might not be ideal to address other 3D genome architecture changes such as TADs or long-range loops. It is therefore unknown whether there might be permissive TAD/loop changes associated with organotropism and this is the limitation of this study.

(7) In the final sentence of the discussion the authors stated "Overall, our results suggest that genome spatial compartment changes can help encode a cell state that favors metastasis (EMT)". The "metastasis (EMT)" was in fact not clearly linked inside the manuscript. The authors did not provide a strong link between metastasis and EMT in their result description. It is also unclear whether the EMT-associated compartment identity would also correlate with the organotropic compartment identity.

Reviewer #2 (Public review):

Summary:

This work addresses an important question of chromosome architecture changes associated with organotopic metastatic traits, showing important trends in genome reorganization. The most important observation is that 3D genome changes consistent with adaptations for new microenvironments, including lung metastatic breast cells exhibiting signatures of the genome architecture typical to a lung cell-like conformation and brain metastatic prostate cancer cells showing compartment shifts toward a brain-like state.

Strengths:

This work presents interesting original results, which will be important for future studies and biomedical implications of epigenetic regulation in norm and pathology.

Weaknesses:

The authors used publicly available data for 15 cell types. They should show how many different sources the data were obtained from and demonstrate that obtained results are consistent if the data from different sources were used.

Author response:

We appreciate the constructive feedback from the reviewers and will work to address many of these concerns in a revised version. Here, we provide initial responses to a few key points that the reviewers raised:

(1) The reviewers rightly pointed out that it is very important to clearly define and explain what qualifies as metastatic potential to particular organs in our system. We acknowledge the valuable contributions of animal models in metastatic cancer studies, but here we intentionally limited our scope to metastasis that had occurred within the human system only. For example, we use data from cancer cells that model human organotropism from the breast to the lung, since the cells originated from infiltrative ductal carcinoma (human breast) but were collected from pleural effusions (human lung). We propose that in this case a comparison with a human lung cancer-derived cell line that was itself purified from a pleural effusion could reveal factors essential for lung metastasis, without adding the confounder of an animal microenvironment. The MetMap Explorer contains valuable information, but the “metastatic potential of each cell line” is measured in a mouse environment. Knowing that a particular cell line, which originated from a human lung metastasis, can further metastasize to other organs in a mouse does not necessarily mean that those cells could do so in humans. The microenvironment responses to metastatic colonization can differ among species. Further, the changes a cell needs to make to adapt to a new organ system in a mouse could be confounded by the changes needed to adapt to mouse conditions in general. Finally, migration from a site of ectopic injection may not mimic migration from an initial tumor site. We agree that the very best data would come from matched primary and metastatic tumors in the same human patient, but those data do not currently exist and generating them would require future work beyond the scope of this study. In our revision, we will ensure that we more clearly explain how and why we chose the cell lines we did and what the advantages and limitations of this choice are.

(2) The reviewers are correct that our unsupervised Principal component analysis (PCA) does not precisely stratify cells according to epithelial-mesenchymal status. In a high dimensional, complex system, it is expected than an unsupervised analysis such as this will not capture just one biological feature in the first principal component. Therefore, when we performed PCA on the compartmental organization profiles of different healthy and cancerous cell lines, instead of finding the largest variation (PC1) following exactly EMT state, it captured an ordering that includes influences from epithelial-mesenchymal state, disease condition, nuclear geometry, and other cellular properties. However, it was striking that this completely unsupervised analysis did match previous annotations of EMT state so well (as seen in supp fig 1b). Therefore, we conclude that the most prominent variations in A/B compartment signature strongly relate to EMT state. In the revision, we will more clearly present the caveats of this interpretation.

(3) Our decision to focus on A/B compartmentalization rather than TAD or loop structure in this analysis was intentional and biologically motivated, rather than solely being a reflection of data resolution. Both compartments and topologically associated domains (TADs) are key parts of genome organization and disruption of these structures has the potential to alter downstream gene regulation, as shown by numerous studies. But, compartments have been found, more so than TADs, to be strongly associated with cell type and cell fate. Therefore, in this manuscript, we decided to focus only on the compartment organization changes between different healthy and cancerous cells as they are more likely to represent the stable alterations of the genome organization malignant transformations.