Dataset Information

Establishment and Characterization of Patient-Derived Xenografts (PDXs) of Different Histology from Malignant Pleural Mesothelioma Patients.

ABSTRACT:

Background

Malignant pleural mesothelioma (MPM) is a very aggressive tumor originating from mesothelial cells. Although several etiological factors were reported to contribute to MPM onset, environmental exposure to asbestos is certainly a major risk factor. The latency between asbestos (or asbestos-like fibers) exposure and MPM onset is very long. MPM continues to be a tumor with poor prognosis despite the introduction of new therapies including immunotherapy. One of the major problems is the low number of preclinical models able to recapitulate the features of human tumors. This impacts the possible discovery of new treatments and combinations.

Methods

In this work, we aimed to generate patient-derived xenografts (PDXs) from MPM patients covering the three major histotypes (epithelioid, sarcomatoid, and mixed) occurring in the clinic. To do this, we obtained fresh tumors from biopsies or pleurectomies, and samples were subcutaneously implanted in immunodeficient mice within 24 h.

Results

We successfully isolated different PDXs and particularly concentrated our efforts on three covering the three histotypes. The tumors that grew in mice compared well histologically with the tumors of origin, and showed stable growth in mice and a low response to cisplatin, as was observed in the clinic.

Conclusions

These models are helpful in testing new drugs and combinations that, if successful, could rapidly translate to the clinical setting.

SUBMITTER: Affatato R

PROVIDER: S-EPMC7766019 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Publications

Establishment and Characterization of Patient-Derived Xenografts (PDXs) of Different Histology from Malignant Pleural Mesothelioma Patients.

Affatato Roberta R Mendogni Paolo P Del Gobbo Alessandro A Ferrero Stefano S Ricci Francesca F Broggini Massimo M Rosso Lorenzo L

Cancers 20201220 12

<h4>Background</h4>Malignant pleural mesothelioma (MPM) is a very aggressive tumor originating from mesothelial cells. Although several etiological factors were reported to contribute to MPM onset, environmental exposure to asbestos is certainly a major risk factor. The latency between asbestos (or asbestos-like fibers) exposure and MPM onset is very long. MPM continues to be a tumor with poor prognosis despite the introduction of new therapies including immunotherapy. One of the major problems ...[more]

PMID: 33419364

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Project description:Malignant pleural mesothelioma (MPM) is a highly lethal, poorly understood neoplasm that is typically associated with asbestos exposure. We performed transcriptional profiling using high-density oligonucleotide microarrays containing approximately 22,000 genes to elucidate potential molecular and pathobiological pathways in MPM using discarded human MPM tumor specimens (n=40), normal lung specimens (n=4), normal pleura specimens (n=5), and MPM and SV40-immortalized mesothelial cell lines (n=5). In global expression analysis using unsupervised clustering techniques, we found two potential subclasses of mesothelioma which correlate loosely with tumor histology. We also identified sets of genes with expression levels that distinguish between multiple tumor subclasses, normal and tumor tissues, and tumors with different morphologies. Microarray gene expression data were confirmed using quantitative reverse transcriptase-polymerase chain reaction (RT-PCR) and protein analysis for three novel candidate oncogenes (NME2, CRI1, and PDGFC) and one candidate tumor suppressor (GSN). Finally, we used bioinformatics tools (i.e. software) to create and explore complex physiological pathways that may be relevant in mesothelioma tumorigenesis, pathobiology, or both. Tissues and cell lines profiled using microarrays. Discarded MPM surgical specimens (n=40), normal pleura specimens (n=5), and normal lung specimens (n=4) were freshly collected (and snap frozen) from patients who underwent surgery at Boston's Brigham and Women's Hospital (BWH) between October 1998 and August 2000. All of these patients underwent extrapleural pneumonectomy with heated intra-pleural cisplatin chemotherapy delivered after the specimens were removed. All normal specimens were obtained from patients who were never diagnosed with MPM. Two human MPM cell lines (MS589 and MS428) were kindly provided by Jonathan A. Fletcher, M.D., Department of Pathology, BWH. The JMN1B MPM cell line19,20 has been described previously. The SV40-immortalized, non-tumorigenic mesothelial cell line (Met-5A)21 and the MPM cell line MSTO-211H22 were purchased from the American Type Culture Collection. Normal tissues were obtained from additional consented patients undergoing treatment for diseases other than MPM. All MPM samples used in these studies contained relatively pure tumor (greater than 50% tumor cells per high power field examined in a section adjacent to the tissue used). The microscopic slides from the patients' resection specimens were reviewed by one of the authors (J.G.), and the diagnosis and histologic subclassification of MPM confirmed in all cases. Linked clinical and pathological data were obtained for all patients who contributed tumor specimens. Specimens and data were rendered anonymous to protect patient confidentiality. Studies utilizing human tissues were approved by and conducted in accordance with the policies of the Institutional Review Board at BWH.

Project description:BackgroundThe classification of diffuse malignant mesothelioma into epithelioid, biphasic, and sarcomatoid types is based on histologic patterns. The diagnosis is made on biopsies, and because of intratumoral heterogeneity, they may not be representative of the entire tumor. The number and volume of biopsies needed to reach diagnostic accuracy in diffuse malignant mesothelioma and their prognostic value remain unclear.MethodsThis study examined 759 consecutive patients with pleural diffuse malignant mesothelioma treated by pleurectomy/decortication or extrapleural pneumonectomy for the presence of epithelioid and/or sarcomatoid histology and classified both the presurgery biopsies (core-needle or thoracoscopic) and surgical resection specimens. The number and volume of biopsies were correlated with pre- and postsurgery histologies and overall survival.ResultsDiffuse malignant mesothelioma was classified as epithelioid (76%), biphasic (18%), sarcomatoid (5%), or indeterminate (1%) in biopsies and as epithelioid (64%), biphasic (32%), and sarcomatoid (4%) in surgical resection specimens (overall concordance, 80.6%). The positive likelihood ratios were 2.4, 13.6, and 90.1 for biopsies with epithelioid, biphasic, and sarcomatoid histologies, respectively. Concordant histologies between biopsies and resections were associated with a higher number of biopsies (median tissue blocks for concordant histologies vs discordant histologies, 3 vs 2; P < .002) but were less associated with a higher volume (median, 1.2 vs 1.1 cm3 ; P = .06). In a multivariate analysis, overall survival was independently predicted by histology in the resection specimen (P < .0001) but not in the biopsy (P = .09).ConclusionsIn contrast to epithelioid histology, sarcomatoid histology in biopsies is highly accurate. Despite intratumoral heterogeneity, the accuracy of histologic classification increases with the number of tissue blocks examined, emphasizing the diagnostic value of extensive sampling by presurgery biopsies.

Project description:BACKGROUND:Patient-derived xenograft (PDX) models are in vivo models of human cancer that have been used for translational cancer research and therapy selection for individual patients. The Jackson Laboratory (JAX) PDX resource comprises 455 models originating from 34 different primary sites (as of 05/08/2019). The models undergo rigorous quality control and are genomically characterized to identify somatic mutations, copy number alterations, and transcriptional profiles. Bioinformatics workflows for analyzing genomic data obtained from human tumors engrafted in a mouse host (i.e., Patient-Derived Xenografts; PDXs) must address challenges such as discriminating between mouse and human sequence reads and accurately identifying somatic mutations and copy number alterations when paired non-tumor DNA from the patient is not available for comparison. RESULTS:We report here data analysis workflows and guidelines that address these challenges and achieve reliable identification of somatic mutations, copy number alterations, and transcriptomic profiles of tumors from PDX models that lack genomic data from paired non-tumor tissue for comparison. Our workflows incorporate commonly used software and public databases but are tailored to address the specific challenges of PDX genomics data analysis through parameter tuning and customized data filters and result in improved accuracy for the detection of somatic alterations in PDX models. We also report a gene expression-based classifier that can identify EBV-transformed tumors. We validated our analytical approaches using data simulations and demonstrated the overall concordance of the genomic properties of xenograft tumors with data from primary human tumors in The Cancer Genome Atlas (TCGA). CONCLUSIONS:The analysis workflows that we have developed to accurately predict somatic profiles of tumors from PDX models that lack normal tissue for comparison enable the identification of the key oncogenic genomic and expression signatures to support model selection and/or biomarker development in therapeutic studies. A reference implementation of our analysis recommendations is available at https://github.com/TheJacksonLaboratory/PDX-Analysis-Workflows .

Dataset Information

Establishment and Characterization of Patient-Derived Xenografts (PDXs) of Different Histology from Malignant Pleural Mesothelioma Patients.

Background

Methods

Results

Conclusions

Publications

Establishment and Characterization of Patient-Derived Xenografts (PDXs) of Different Histology from Malignant Pleural Mesothelioma Patients.

Similar Datasets

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