Integrative and comparative genomic analyses identify clinically relevant groups of pulmonary carcinoids and unveil the supra-carcinoids
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PROVIDER: EGAS00001003699 | EGA |
REPOSITORIES: EGA
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Project description:Multi-omic data for lung neuroendocrine neoplasms, including the first multi-omic sequencing data for the understudied lung atypical carcinoids. The data includes Whole-exomes, whole-genomes, RNA-seq, and EPIC 850K methylation array data.
| EGAD00001005087 | EGA
Project description:The worldwide incidence of pulmonary carcinoids is increasing, but little is known about their molecular characteristics. Through machine learning and multi-omics factor analysis, we compare and contrast the genomic profiles of 116 pulmonary carcinoids (including 35 atypical), 75 large-cell neuroendocrine carcinomas (LCNEC), and 66 small-cell lung cancers. Here we report that the integrative analyses on 257 lung neuroendocrine neoplasms stratify atypical carcinoids into two prognostic groups with a 10-year overall survival of 88% and 27%, respectively. We identify therapeutically relevant molecular groups of pulmonary carcinoids, suggesting DLL3 and the immune system as candidate therapeutic targets; we confirm the value of OTP expression levels for the prognosis and diagnosis of these diseases, and we unveil the group of supra-carcinoids. This group comprises samples with carcinoid-like morphology yet the molecular and clinical features of the deadly LCNEC, further supporting the previously proposed molecular link between the low- and high-grade lung neuroendocrine neoplasms.
| S-EPMC6702229 | biostudies-literature
Project description:Multi-Omic Molecular Profiling of Lung Cancer Risk in Chronic Obstructive Pulmonary Disease
| PRJNA418402 | ENA
Project description:Extra-pulmonary neuroendocrine carcinomas (EP-NECs) are lethal cancers with limited treatment options. Identification of contributing factors to the observed heterogeneity of clinical outcomes within the EP-NEC family is warranted, to enable identification of effective treatments. A multicentre retrospective study investigated potential differences in "real-world" treatment/survival outcomes between small-cell (SC) versus (vs.) non-SC EP-NECs. One-hundred and seventy patients were included: 77 (45.3%) had SC EP-NECs and 93 (54.7%) had non-SC EP-NECs. Compared to the SC subgroup, the non-SC subgroup had the following features: (1) a lower mean Ki-67 index (69.3% vs. 78.7%; p = 0.002); (2) a lower proportion of cases with a Ki-67 index of ≥55% (73.9% vs. 88.7%; p = 0.025); (3) reduced sensitivity to first-line platinum/etoposide (objective response rate: 31.6% vs. 55.1%, p = 0.015; and disease control rate; 59.7% vs. 79.6%, p = 0.027); (4) worse progression-free survival (PFS) (adjusted-HR = 1.615, p = 0.016) and overall survival (OS) (adjusted-HR = 1.640, p = 0.015) in the advanced setting. Within the advanced EP-NEC cohort, subgroups according to morphological subtype and Ki-67 index (<55% vs. ≥55%) had significantly different PFS (adjusted-p = 0.021) and OS (adjusted-p = 0.051), with the non-SC subgroup with a Ki-67 index of <55% and non-SC subgroup with a Ki-67 index of ≥55% showing the best and worst outcomes, respectively. To conclude, the morphological subtype of EP-NEC provides complementary information to the Ki-67 index and may aid identification of patients who could benefit from alternative first-line treatment strategies to platinum/etoposide.
| S-EPMC8392018 | biostudies-literature
Project description:ObjectivesPost-imaging mathematical prediction models (MPMs) provide guidance for the management of solid pulmonary nodules by providing a lung cancer risk score from demographic and radiologists-indicated imaging characteristics. We hypothesized calibrating the MPM risk score threshold to a local study cohort would result in improved performance over the original recommended MPM thresholds. We compared the pre- and post-calibration performance of four MPM models and determined if improvement in MPM prediction occurs as nodules are imaged longitudinally.Materials and methodsA common cohort of 317 individuals with computed tomography-detected, solid nodules (80 malignant, 237 benign) were used to evaluate the MPM performance. We created a web-based application for this study that allows others to easily calibrate thresholds and analyze the performance of MPMs on their local cohort. Thirty patients with repeated imaging were tested for improved performance longitudinally.ResultsUsing calibrated thresholds, Mayo Clinic and Brock University (BU) MPMs performed the best (AUC = 0.63, 0.61) compared to the Veteran's Affairs (0.51) and Peking University (0.55). Only BU had consensus with the original MPM threshold; the other calibrated thresholds improved MPM accuracy. No significant improvements in accuracy were found longitudinally between time points.ConclusionsCalibration to a common cohort can select the best-performing MPM for your institution. Without calibration, BU has the most stable performance in solid nodules ≥ 8 mm but has only moderate potential to refine subjects into appropriate workup. Application of MPM is recommended only at initial evaluation as no increase in accuracy was achieved over time.Key points• Post-imaging lung cancer risk mathematical predication models (MPMs) perform poorly on local populations without calibration. • An application is provided to facilitate calibration to new study cohorts: the Mayo Clinic model, the U.S. Department of Veteran's Affairs model, the Brock University model, and the Peking University model. • No significant improvement in risk prediction occurred in nodules with repeated imaging sessions, indicating the potential value of risk prediction application is limited to the initial evaluation.
| S-EPMC6717521 | biostudies-literature
Project description:Resolution of infection results in development of trained innate immunity which is typically beneficial for the host in defense against unrelated secondary infection. Epigenetic changes underlie the establishment of trained innate immunity therefore host metabolism and this response are intimately linked. However, little is known regarding the influence of lipids on the development and function of trained immunity. Utilizing two models of pulmonary bacterial infection combined with multi-omic approaches, we identified persistent, pathogen-specific changes to the lung lipidome that correlated with differences in the trained immune response. Further, we establish the specific cellular populations in the lung that contribute to this altered lipidome. Together these results expand our understanding of the pulmonary trained innate immune response and the contributions of host lipids in informing that response.
2021-09-22 | GSE171510 | GEO
Project description:Multi-omic data (whole-genome sequencing, RNA-seq, EPIC 850K methylation arrays) for 123 samples from the MESOMICS project, the French project of molecular characterization of malignant pleural mesothelioma
| EGAS00001004812 | EGA
Project description:Drug-induced toxicity is a significant problem in clinical care. A key problem here is a general understanding of the molecular mechanisms accompanying the transition from desired drug effects to adverse events following administration of either therapeutic or toxic doses, in particular within a patient context. Here, a comparative toxicity analysis was performed for fifteen hepatotoxic drugs by evaluating toxic changes reflecting the transition from therapeutic drug responses to toxic reactions at the cellular level. By use of physiologically-based pharmacokinetic modeling, in vitro toxicity data were first contextualized to quantitatively describe time-resolved drug responses within a patient context. Comparatively studying toxic changes across the considered hepatotoxicants allowed the identification of subsets of drugs sharing similar perturbations on key cellular processes, functional classes of genes, and individual genes. The identified subsets of drugs were next analyzed with regard to drug-related characteristics and their physicochemical properties. Toxic changes were finally evaluated to predict both molecular biomarkers and potential drug-drug interactions. The results may facilitate the early diagnosis of adverse drug events in clinical application.
| S-EPMC5289425 | biostudies-literature
Project description:The development of metastases is a complex multi-step process manifested by diverse patterns, involving single or multiple organs and different time-courses of distant recurrences. The aim of this study was to characterise molecular patterns associated with patterns of organ-specific metastatic spread observed in the clinic from FFPE embedded breast carcinomas and lymph node metastases. This study includes patients with four patterns of first metastatic sites: bone only (with no visceral metastases within 6 months of first metastasis); to viscera only (with no bone metastasis within 6 months of first metastasis); to bone and visceral organs within a 6 month period; and no recorded distant metastasis. The study population was composed of 5,061 patients diagnosed with invasive primary breast cancer without distant metastasis at the time of diagnosis between 1975 and 2005 from Guyâs Hospital. From the study population, an incidence-based case-control design was used to include patients from each of the metastatic groups and a control series randomly matched to cases according to having no reported metastasis up to the calendar date of metastasis. The aim of this expression profiling study was to characterise any molecular patterns that may be associated with the organ-specific metastatic spread observed in the clinic from FFPE embedded breast carcinomas and lymph node metastases.
2017-12-01 | E-MTAB-4003 | biostudies-arrayexpress