A molecular phenotypic map of malignant pleural mesothelioma

  1. Alex Di Genova
  2. Lise Mangiante
  3. Alexandra Sexton-Oates
  4. Catherine Voegele
  5. Lynnette Fernandez-Cuesta
  6. Nicolas Alcala
  7. Matthieu Foll

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Abstract

Background

Malignant pleural mesothelioma (MPM) is a rare understudied cancer associated with exposure to asbestos. So far, MPM patients have benefited marginally from the genomics medicine revolution due to the limited size or breadth of existing molecular studies. In the context of the MESOMICS project, we have performed the most comprehensive molecular characterization of MPM to date, with the underlying dataset made of the largest whole-genome sequencing series yet reported, together with transcriptome sequencing and methylation arrays for 120 MPM patients.

Results

We first provide comprehensive quality controls for all samples, of both raw and processed data. Due to the difficulty in collecting specimens from such rare tumors, a part of the cohort does not include matched normal material. We provide a detailed analysis of data processing of these tumor-only samples, showing that all somatic alteration calls match very stringent criteria of precision and recall. Finally, integrating our data with previously published multiomic MPM datasets (n = 374 in total), we provide an extensive molecular phenotype map of MPM based on the multitask theory. The generated map can be interactively explored and interrogated on the UCSC TumorMap portal (https://tumormap.ucsc.edu/?p=RCG_MESOMICS/MPM_Archetypes ).

Conclusions

This new high-quality MPM multiomics dataset, together with the state-of-art bioinformatics and interactive visualization tools we provide, will support the development of precision medicine in MPM that is particularly challenging to implement in rare cancers due to limited molecular studies.

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  1. GigaScience

    Background Malignant Pleural Mesothelioma (MPM)

    This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giac128), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

    Reviewer 1: Saurabh V Laddha

    Authors did a fantastic job by integrating MPM multi-omics datasets and created an integrative and interactive map for users to explore these datasets. MPM is a rare cancer type and understudied so such resources are very useful to move the field forward at a molecular level. The comprehensive data is well presented and the manuscript is well written to explain the complex genomics dataset for MPM. All the figures are well explained and very clear to understand

    Minor point:

    • Author mentioned an evaluation of tumor purity was done using pathological review, did author used molecular data such as genomic data to find tumor purity ? and if yes, how was the consensus ? This is very important factor to interpret the genomic results as the data was sequenced at 30X
    • In the same line, RNAseq can also be used to identify tumor purity and it will be really helpful for users to clear picture on tumor purity.
    • Is it not very clear from method section that the same MPM samples were used to sequence at DNA , RNA and DNA methylation level ? A brief explanation or table will be very easy for users to understand.
    • Recent WHO classify MPM into three different histopathological types. Did author do any unsupervised analysis from these comprehensive data to understand MPM heterogeneity or replicate WHO classification? or did author find WHO subtypes of MPM using molecular dataset ? A brief analysis/comment on usage of histological classification Vs Molecular classification will certainly move the MPM research field forward as researcher have found vast differences between histological vs molecular classification and the field is moving towards more molecular based classification in clinic.

    **Reviewer 2: Jeremy Warner **

    In this paper, the authors describe a new public resource for the molecular characterization of malignant pleural mesothelioma (MPM), which they describe as the most comprehensive to date. They perform WGS, transcriptome, and methylation arrays for 120 patients with MPM sourced through the MESOMICS project and integrate this dataset with an additional several hundred patients from previously published datasets.

    Although I cannot independently verify their claim that this is the largest and most comprehensive dataset for MPM, it is quite impressive and expansive. The pipeline utilized is well described and the results at all stages are transparently shared for prospective users of this dataset.

    The description of the methods to identify and remove germline variants is interesting, although the length somewhat detracts from the main goal of the paper in describing an MPM resource. Perhaps, this part could be condensed with the technical details presented in supplement. This comment pertains to both the Point Mutations and Structural Variants sections.

    Additional moderate concerns:

    There are insufficient details provided on the clinical and epidemiological parameters. Indirectly, it would appear that sex, age class, and smoking status are the clinical parameters - but what are the age classes? Is smoking status binary ever/never, or more involved? There ought to be a data dictionary provided as a supplemental table which describes each clinical/epidemiological variable, along with the possible values that the variable can take on. It should additionally be explained why other important clinical parameters are not available - most importantly, the presence of accompanying pulmonary comorbidity such as chronic obstructive pulmonary disease (COPD) and the existence of conditions that might preclude the use of standard systemic therapies, such as renal disease precluding the use of platinum agents.

    Context: I would like to see more here about the role of asbestos in the etiology, including what might be known about the pathophysiology of asbestos fibers at the molecular level. Also, there is nothing here about the evolution of treatment for MPM; the latest "state-of-the-art" regimens (platinum doublet + bevacizumab [MAPS; NCT00651456] and dual checkpoint inhibition [Checkmate 743; NCT02899299]) have reported median survival in the 18-month range, which is distinctly better than the median survivals quoted by the authors. Finally, I would like to see one or more direct references to the clinical trials where molecular heterogeneity has "fueled the implementation of drug trials for more tailored MPM treatments".

    Data Description: All specimens in the MESOMICS study are said to be collected from surgically resected MPM; this also appears to be the case for the integrated multi-omic studies from Bueno et al. and Hmeljak et al. and this should be explicitly indicated. Somewhere, it should also be explicitly discussed that this is an important limitation in the future utility of this data - surgical specimens are convenience samples and while they do provide important information, they lack treatment exposure. Given that many if not most patients with MPM will survive to 2nd or 3rd line systemic therapy, and that 1st line is fairly standardized, a knowledge of induced mutations is going to be essential to the ultimate goal of precision medicine.

    Minor concerns:

    The labels in the figures (e.g., Figure 2 - "Unmapped..too.short") are human-readable but could still be presented in a more friendly fashion. All acronyms should be defined.

    **Reviewer 3: Mary Ann Tuli **

    I have been asked to review the process of accessing the controlled data cited in this study to ensure that the process is clear and smooth. The study is available from the European Genome-phenome Archive (EGA) under accession number EGAS00001004812 (https://ega-archive.org/studies/EGAS00001004812). The paper is clear about how to obtain the DAA.

    The study has three datasets.

    I can confirm that the author was very prompt in his response to me requesting the DAC, in providing the DAA and in responding to the queries I had when completing the DAA. The completed DAA was sent to the EGA by the author on 29-Jul, and EGA responded within 3 working days, stating access had been granted. This is an excellent response time, so I conclude that the process of obtaining the DAA and the EGA making the data available to the user is very good.

    Today (1-Sep) I have attempted to gain access to the data via EGA. I was easily able to login to my EGA account and see that the datasets are available to me to download. Users need to download data using the EGA download client - pyEGA3. EGA provides a video on how to install the client, but I hit a problem and require technical support.

    I emailed the EGA help desk but have not had a response yet. I was quite surprised to receive a response from the author and have learnt that EGA include the owner of the study in RT tickets so they see any communication. I commend the author for his prompt response to my ticket (though it didn't solve my problem).

    I cannot hold on to this review for any longer, and I am not yet in a position to comment on the nature of the data held within this study.

    I do have concerns that the process of accessing controlled data held in the EGA is not straight forward. Users need to watch a 12 minute video to learn how to install the download client and may need to install programs on their computer). There is a FAQ which is very technical. This is not an issue for the author to resolve though.

    I understand the author has some minor revisions to make, so hopefully I should have a response from the EGA help desk before a final decision needs to be made (?).

  2. Version published to 10.1093/gigascience/giac128
  3. Version published to 10.1101/2022.07.06.499003v1 on bioRxiv