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Developmental History Provides a Roadmap for the Emergence of Tumor Plasticity.

Developmental cell | Mar 26, 2018

We show that the loss or gain of transcription factor programs that govern embryonic cell-fate specification is associated with a form of tumor plasticity characterized by the acquisition of alternative cell fates normally characteristic of adjacent organs. In human non-small cell lung cancers, downregulation of the lung lineage-specifying TF NKX2-1 is associated with tumors bearing features of various gut tissues. Loss of Nkx2-1 from murine alveolar, but not airway, epithelium results in conversion of lung cells to gastric-like cells. Superimposing oncogenic Kras activation enables further plasticity in both alveolar and airway epithelium, producing tumors that adopt midgut and hindgut fates. Conversely, coupling Nkx2-1 loss with foregut lineage-specifying SOX2 overexpression drives the formation of squamous cancers with features of esophageal differentiation. These findings demonstrate that elements of pathologic tumor plasticity mirror the normal developmental history of organs in that cancer cells acquire cell fates associated with developmentally related neighboring organs.

Pubmed ID: 29587142 RIS Download

Mesh terms: Adenocarcinoma, Mucinous | Animals | Carcinoma, Squamous Cell | Cell Differentiation | Cell Lineage | Cell Plasticity | Embryonic Development | Endoderm | Esophageal Neoplasms | Female | Gene Expression Regulation, Developmental | Humans | Lung Neoplasms | Male | Mice | Mice, Inbred C57BL | Mice, Knockout | Prognosis | SOXB1 Transcription Factors | Signal Transduction | Stomach Neoplasms | Survival Rate | Thyroid Nuclear Factor 1

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Associated grants

  • Agency: NHLBI NIH HHS, Id: R01 HL118185
  • Agency: NHLBI NIH HHS, Id: K99 HL127181
  • Agency: NHLBI NIH HHS, Id: R00 HL127181
  • Agency: NHLBI NIH HHS, Id: P30 HL101287
  • Agency: NIGMS NIH HHS, Id: T32 GM007205
  • Agency: Howard Hughes Medical Institute, Id:

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The Cancer Genome Atlas

Project exploring the spectrum of genomic changes involved in more than 20 types of human cancer that provides a platform for researchers to search, download, and analyze data sets generated. As a pilot project it confirmed that an atlas of changes could be created for specific cancer types. It also showed that a national network of research and technology teams working on distinct but related projects could pool the results of their efforts, create an economy of scale and develop an infrastructure for making the data publicly accessible. Its success committed resources to collect and characterize more than 20 additional tumor types. Components of the TCGA Research Network: * Biospecimen Core Resource (BCR); Tissue samples are carefully cataloged, processed, checked for quality and stored, complete with important medical information about the patient. * Genome Characterization Centers (GCCs); Several technologies will be used to analyze genomic changes involved in cancer. The genomic changes that are identified will be further studied by the Genome Sequencing Centers. * Genome Sequencing Centers (GSCs); High-throughput Genome Sequencing Centers will identify the changes in DNA sequences that are associated with specific types of cancer. * Proteome Characterization Centers (PCCs); The centers, a component of NCI's Clinical Proteomic Tumor Analysis Consortium, will ascertain and analyze the total proteomic content of a subset of TCGA samples. * Data Coordinating Center (DCC); The information that is generated by TCGA will be centrally managed at the DCC and entered into the TCGA Data Portal and Cancer Genomics Hub as it becomes available. Centralization of data facilitates data transfer between the network and the research community, and makes data analysis more efficient. The DCC manages the TCGA Data Portal. * Cancer Genomics Hub (CGHub); Lower level sequence data will be deposited into a secure repository. This database stores cancer genome sequences and alignments. * Genome Data Analysis Centers (GDACs) - Immense amounts of data from array and second-generation sequencing technologies must be integrated across thousands of samples. These centers will provide novel informatics tools to the entire research community to facilitate broader use of TCGA data. TCGA is actively developing a network of collaborators who are able to provide samples that are collected retrospectively (tissues that had already been collected and stored) or prospectively (tissues that will be collected in the future).

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ImageJ

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HOMER

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SAMTOOLS

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MACS

Software Python package for identifying transcript factor binding sites. Used to evaluate significance of enriched ChIP regions. Improves spatial resolution of binding sites through combining information of both sequencing tag position and orientation. Can be used for ChIP-Seq data alone, or with control sample with increase of specificity.

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