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Association of Tumor Protein p53 and Ataxia-Telangiectasia Mutated Comutation With Response to Immune Checkpoint Inhibitors and Mortality in Patients With Non-Small Cell Lung Cancer.

Yu Chen | Gang Chen | Jin Li | Ying-Ying Huang | Yi Li | Jing Lin | Li-Zhu Chen | Jian-Ping Lu | Yu-Qi Wang | Chang-Xi Wang | Leong Kin Pan | Xue-Feng Xia | Xin Yi | Chuan-Ben Chen | Xiong-Wei Zheng | Zeng-Qing Guo | Jian-Ji Pan
JAMA network open | 2019

Immune checkpoint inhibitors (ICIs) can elicit durable antitumor responses in patients with non-small cell lung cancer (NSCLC), but only 20% to 25% of patients respond to treatment. As important genes in the DNA damage response pathway, comutation in the tumor protein p53 (TP53) and ataxia-telangiectasia mutated (ATM) genes may be associated with genomic instability and hypermutation. However, the prevalence of TP53 and ATM comutation and its association with response to ICIs are not fully understood.

Pubmed ID: 31539077

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SPSS (tool)

RRID:SCR_002865

Software package used for interactive, or batched, statistical analysis in social science, health sciences and marketing. Software platform offers advanced statistical analysis, a library of machine-learning algorithms, text analysis, open-source extensibility, integration with big data and deployment into applications.Versions that were produced by SPSS Inc. before the IBM acquisition (Versions 18 and earlier) would be given origin or publisher of SPSS Inc. in Chicago.

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PRISM (tool)

RRID:SCR_005375

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.

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