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      An NKX2-1/ERK/WNT feedback loop modulates gastric identity and response to targeted therapy in lung adenocarcinoma

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          Abstract

          Cancer cells undergo lineage switching during natural progression and in response to therapy. NKX2-1 loss in human and murine lung adenocarcinoma leads to invasive mucinous adenocarcinoma (IMA), a lung cancer subtype that exhibits gastric differentiation and harbors a distinct spectrum of driver oncogenes. In murine BRAF V600E-driven lung adenocarcinoma, NKX2-1 is required for early tumorigenesis, but dispensable for established tumor growth. NKX2-1-deficient, BRAF V600E-driven tumors resemble human IMA and exhibit a distinct response to BRAF/MEK inhibitors. Whereas BRAF/MEK inhibitors drive NKX2-1-positive tumor cells into quiescence, NKX2-1-negative cells fail to exit the cell cycle after the same therapy. BRAF/MEK inhibitors induce cell identity switching in NKX2-1-negative lung tumors within the gastric lineage, which is driven in part by WNT signaling and FoxA1/2. These data elucidate a complex, reciprocal relationship between lineage specifiers and oncogenic signaling pathways in the regulation of lung adenocarcinoma identity that is likely to impact lineage-specific therapeutic strategies.

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          When cells become cancerous they grow uncontrollably and spread into surrounding healthy tissue. As the cancer progresses different genes are switched on and off which can cause tumor cells to change their identity and transition into other types of cell. How closely tumor cells resemble the healthy tissue they came from can influence how well the cancer responds to treatment.

          Many lung cancers have an identity similar to normal lung cells. However, some turn off a gene that codes for a protein called NKX2-1, which leads to a type of cancer called invasive mucinous adenocarcinoma (or IMA for short). Cells from this type of cancer develop an identity similar to mucous cells that line the surface of the stomach. But it was unclear how IMA tumor cells that developed from a mutation in the BRAF gene are affected by this loss in NKX2-1, and how transitioning to a different cell type impacts their response to treatment.

          To investigate this, Zewdu et al. studied lung cells from patients with IMA tumors driven by a mutation in BRAF and cells from mice that have been genetically engineered to have a similar form of cancer. This revealed that the NKX2-1 protein is needed to initiate the formation of cancer cells but is not required for the growth of already established BRAF-driven tumors. Further experiments showed that removing the gene for NKX2-1 made these cancer cells less responsive to drugs known as BRAF/MEK inhibitors that are commonly used to treat cancer. These drugs caused the IMA cancer cells to change their identity and become another type of stomach cell. This identity change was found to depend on two signaling pathways which cells use to communicate.

          This study provides some explanation of how IMA lung cancers that lack the gene for NKX2-1 resist treatment with BRAF/MEK inhibitors. It also shows new relationships between key genes in these cancers and systems for cell communication. These findings could lead to better therapies for lung cancer, particularly for patients whose tumor cells are deficient in NKX2-1 and therefore require specialized treatment.

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          Most cited references104

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

            In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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              STAR: ultrafast universal RNA-seq aligner.

              Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                06 April 2021
                2021
                : 10
                : e66788
                Affiliations
                [1 ]Huntsman Cancer Institute Salt Lake CityUnited States
                [2 ]Department of Pathology, University of Utah Salt Lake CityUnited States
                [3 ]School of Computing, University of Utah Salt Lake CityUnited States
                [4 ]Department of Oncological Sciences, University of Utah Salt Lake CityUnited States
                The Wistar Institute United States
                The Wistar Institute United States
                The Wistar Institute United States
                Stanford University School of Medicine United States
                Author information
                https://orcid.org/0000-0001-6784-6068
                http://orcid.org/0000-0002-6490-1794
                https://orcid.org/0000-0003-3591-3195
                Article
                66788
                10.7554/eLife.66788
                8102067
                33821796
                9f9cd4f2-876a-42ca-a409-198499146b81
                © 2021, Zewdu et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 22 January 2021
                : 05 April 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100001368, V Foundation for Cancer Research;
                Award ID: V Scholar Award
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000861, Burroughs Wellcome Fund;
                Award ID: Career Award for Medical Scientists
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000054, National Cancer Institute;
                Award ID: R01CA212415
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000054, National Cancer Institute;
                Award ID: R01CA240317
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000054, National Cancer Institute;
                Award ID: F31CA243427
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100010638, Huntsman Cancer Institute;
                Award ID: Pilot Project Award
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100009633, Eunice Kennedy Shriver National Institute of Child Health and Human Development;
                Award ID: T32HD007491
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Cancer Biology
                Developmental Biology
                Custom metadata
                Genetically engineered murine models reveal novel mechanisms of cell identity regulation in lung cancer and provide insights into the complex interplay between lineage specifiers and oncogenic signaling pathways in this disease.

                Life sciences
                lung adenocarcinoma,lineage switching,targeted therapy,nkx2-1,erk,human,mouse
                Life sciences
                lung adenocarcinoma, lineage switching, targeted therapy, nkx2-1, erk, human, mouse

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