Dataset Information

Gene signature driving invasive mucinous adenocarcinoma of the lung.

ABSTRACT: Though invasive mucinous adenocarcinoma of the lung (IMA) is pathologically distinctive, the molecular mechanism driving IMA is not well understood, which hampers efforts to identify therapeutic targets. Here, by analyzing gene expression profiles of human and mouse IMA, we identified a Mucinous Lung Tumor Signature of 143 genes, which was unexpectedly enriched in mucin-producing gastrointestinal, pancreatic, and breast cancers. The signature genes included transcription factors FOXA3, SPDEF, HNF4A, mucins MUC5AC, MUC5B, MUC3, and an inhibitory immune checkpoint VTCN1/B7-H4 (but not PD-L1/B7-H1). Importantly, induction of FOXA3 or SPDEF along with mutant KRAS in lung epithelium was sufficient to develop benign or malignant mucinous lung tumors, respectively, in transgenic mice. FOXA3 and SPDEF induced MUC5AC and MUC5B, while HNF4A induced MUC3 in human mucinous lung cancer cells harboring a KRAS mutation. ChIP-seq combined with CRISPR/Cas9 determined that upstream enhancer regions of the mucin genes MUC5AC and MUC5B, which were bound by SPDEF, were required for the expression of the mucin genes. Here, we report the molecular signature and gene regulatory network driving mucinous lung tumors.

SUBMITTER: Guo M

PROVIDER: S-EPMC5376761 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Gene signature driving invasive mucinous adenocarcinoma of the lung.

Guo Minzhe M Tomoshige Koichi K Meister Michael M Muley Thomas T Fukazawa Takuya T Tsuchiya Tomoshi T Karns Rebekah R Warth Arne A Fink-Baldauf Iris M IM Nagayasu Takeshi T Naomoto Yoshio Y Xu Yan Y Mall Marcus A MA Maeda Yutaka Y

EMBO molecular medicine 20170401 4

Though invasive mucinous adenocarcinoma of the lung (IMA) is pathologically distinctive, the molecular mechanism driving IMA is not well understood, which hampers efforts to identify therapeutic targets. Here, by analyzing gene expression profiles of human and mouse IMA, we identified a Mucinous Lung Tumor Signature of 143 genes, which was unexpectedly enriched in mucin-producing gastrointestinal, pancreatic, and breast cancers. The signature genes included transcription factors <i>FOXA3, SPDEF, ...[more]

PMID: 28255028

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Project description:Objective: This study aimed to develop and validate predictive models using clinical parameters, radiomic features, and a combination of both for invasive mucinous adenocarcinoma (IMA) of the lung in patients with lung adenocarcinoma. Method: A total of 173 and 391 patients with IMA and non-IMA, respectively, were retrospectively analyzed from January 2017 to September 2022 in our hospital. Propensity Score Matching was used to match the 2 groups of patients. A total of 1037 radiomic features were extracted from contrast-enhanced computed tomography (CT). The patients were randomly divided into training and test groups at a ratio of 7:3. The least absolute shrinkage and selection operator algorithm was used for radiomic feature selection. Three radiomics prediction models were applied: logistic regression (logistic), support vector machine (SVM), and decision tree. The best-performing model was adopted, and the radiomics score (Radscore) was then computed. A clinical model was developed using logistic regression. Finally, a combined model was established based on a clinical model and a radiomics model. The area under the receiver operating characteristic (ROC) curve (AUC) and decision curve analysis were used to evaluate the predictive value of the developed models. Results: Both clinical and radiomics models established using the logistic method showed the best performance. The Delong test revealed that the combined model was superior to the clinical and radiomics models (P = .018 and .020, respectively). The ROC-AUC (also decision curve analysis) of the combined model was 0.840 and 0.850 in the training and testing groups, respectively, which showed good predictive performance for IMA. The Brier scores for the combined model were 0.161 and 0.154 in the training and testing groups, respectively. Conclusion: The combined model incorporating radiomic CT features and clinical predictors may have the potential to predict IMA in patients with lung cancer.

Project description:BackgroundInvasive mucinous adenocarcinoma (IMA) of the lung is a rare and distinct subtype of adenocarcinoma that can appear as airspace opacities on computed tomography (CT). In daily practice, we have occasionally encountered spontaneous regression of airspace opacities (SRAs) without treatment on serial CTs in patients with IMAs, which has not previously been described in the literature. Here, we describe serial CT findings with emphasis on SRAs in relation to clinicopathological features and treatment outcomes in patients with IMAs.MethodsA total of 46 patients with pathologically-confirmed IMAs of the lung from January 2013 to June 2018 were included. Serial CT scans were reviewed and the patients were classified into SRA and no-SRA groups according to the presence of SRA. Radiological features, clinicopathological characteristics, and treatment outcomes were compared between the SRA and no-SRA groups.ResultsA total of 32 patients were included in the no-SRA group and 14 patients in the SRA group. IMAs in the SRA group were mostly pneumonic (P < 0.001), larger (P < 0.001), multifocal (P = 0.001), and showed higher stage (P < 0.001) on initial CT. Of seven patients who died during follow-up, six were from the SRA group (P < 0.001). Mean overall survival for all IMAs was 86.6 months (range, 0-110 months), and the SRA group showed significantly worse overall survival (P < 0.001).ConclusionsIMAs of the lung showing SRAs on serial CTs are larger and multifocal, and tend to be pneumonic in type on initial CT. Patients present at a higher stage of disease, with higher mortality rate and reduced overall survival.Key pointsSIGNIFICANT FINDINGS OF THE STUDY: Invasive mucinous adenocarcinomas (IMAs) of the lung can show spontaneous regression of airspace opacities (SRAs) on serial CTs, without being correlated to the administration of anticancer drugs. IMAs that showed SRAs demonstrated reduced overall survival in patients.What this study addsWhen airspace opacities show regression on CT, IMA should still be included in the differential diagnosis. A more careful application of RECIST 1.1 is needed in the assessment of tumor response of IMAs.

Dataset Information

Gene signature driving invasive mucinous adenocarcinoma of the lung.

Publications

Gene signature driving invasive mucinous adenocarcinoma of the lung.

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